Building risk prediction models  with a focus on GenomeWide Association Studies. Charles Kooperberg


 Logan Dennis
 1 years ago
 Views:
Transcription
1 Building risk prediction models  with a focus on GenomeWide Association Studies
2 Risk prediction models Based on data: (D i, X i1,..., X ip ) i = 1,..., n we like to fit a model P(D = 1 X 1,..., X p ) = f (X 1,..., X p ), and evaluate how good the model is. Issues: Which X to use (model selection). What form to use for f (nonparametric(?) regression). Selection of smoothing/control parameters. Estimation of coefficients in the final model. Unbiased (?) evaluation of the model.
3 What form to use for f Not discussed in this talk! There are many (nonparametric) regression methods. Quite a few of these methods will involve variable selection, and/or selection of some hyperparameters, in addition to fitting a regression model. In my experience often (not always) when there are many predictors linear models work (almost) as good as more complicated models (trees, splines, interactions... ), and they are often easier to explain, and come with less variance.
4 Selection of predictors. Selection of predictors on the same data as training and/or evaluating models can (sometimes severely) influence your results. In different ways. Be aware! Questions to ask  If certain markers are selected for the prediction, because they are the most significant ones, as reported in the literature, was the data you have in hand part of the data that was used to select those markers? Can you resist the temptation to use your testset for anything until you have selected your final model. Are you sure that the subjects in your test data have not been used for this selection mentioned in the previous point. Remember, you can not even use it to select between the last few models! Using the same data to evaluate your model as is part of your crossvalidation procedure biases your results. Carefully examine which steps influence each other.
5 In an ideal world you have loads of data 1 Selection data to select which predictors to use. 2 Training data to select control parameters (like λ in the Lasso) and parameters. You may need to do crossvalidation to select λ, and then refit using all training data to fit the parameters. Alternatively, you may want separate data for Training and Validation. 3 Evaluation or Test data to evaluate your rule that has not been used before. If you don t have enough data, you sometimes can do a second level of crossvalidation to effectively increase your sample size. The training and selection data can sometimes be combined, if you careful of what you do on which data.
6 Risk prediction models for GWAS Combine significant SNPs and environmental factors to predict risk of a disease. Do not worry about cause and effect of SNPs and these factors, as the goal is prediction. Do not worry about the form of the predictors: a black box is fine.
7 Where to worry about? Model selection: while there are many SNPs that can be significant (or not), there are many more possible models. We need efficient strategies to select models, as well as fair ways to compare them. Model evaluation: after all selection we also want to evaluate the quality of the model. We need new data for that. On a practical level  it is getting increasingly easy to get GWAS data from other groups. However, true prediction models also need information on other risk factors. Harmonizing those is a lot of work!
8 Lasso, LARS, boosting The traditional way to fit a logistic model with predictions X 1,..., X p is maximum likelihood. This works well provided the predictors are independent, and p < n the sample size. The Lasso (Tibshirani) noted that for prediction it is often better that some of the coefficients are shrunk (maybe all the way down to 0). This can be achieved by maximizing l(x, D; β) λ k β k, where l(x, D; β) is the logistic likelihood with parameters β, and λ > 0 is a penalty parameter.
9 Lasso, LARS, boosting (cont) There are close relations between the Lasso and (some forms of) boosting, or other related stepwise methods. These relations are partly formalized using the LARS (Efron, Friedman, Hastie, Tibshirani) algorithm. Parameter selection is usually via crossvalidation. (But we still need a testset to evaluate how good the prediction is.) It should be noted that these methods select the best predictors, not necessarily the significantly associated variables.
10 Lasso and GWAS The code may be efficient. But it cannot deal efficiently with 100,000s of predictors, and be able to do comparative simulation studies. The natural approach would appear to be to filter at a particular α level and only consider those predictors. The clean approach is to select significant SNPs each time separately for each crossvalidation run (for λ). Potentially, the dirty way increases bias and decreases variance.
11 WTCCC data 3000 common controls 2000 cases for seven diseases each (Coronary artery disease, type 1 diabetes, type 2 diabetes, Crohn s, rheumatoid arthritis, bipolar disorder, hypertension). Affymetrix 5.0 ( 500,000 SNPs). Carried out experiment for T1D, T2D, Crohn s. For each of the diseases divide data in training set of 3000 and test set of Apply various prediction methods.
12 Preprocessing Filter out all SNPs with MAF < 5%, missingness > 5%, HW control P < Fill in missing data using single (random) imputation. (Makes life much easier.) Many methods can use with large datasets, but 500,000 predictors  certainly for simulations  is too much. Thus, filter and select the top p predictors. How? Correlations between predictors can get very high  as high as some methods will need some prior filtering.
13 Five selection procedures For each procedure set part of data apart as testset (we took 40%). 1 Cheat Select the best predictors using all data. Fit using Lasso. Select the best λ using crossvalidation (CV) on training data. Refit with that λ on all training data. 2 NoTest Select the best predictors using training data. Fit using Lasso. Select the best λ looking at the test data. 3 PartCV Select the best predictors using training data. Fit using Lasso. Select the best λ using CV on training data. Refit with λ on all training data. 4 FullCV CV: Select best predictors on 9/10th of training data. Get score for each λ, and evaluate on remaining 1/10th of training data. Select the best λ. Select best predictors using training data. Refit with λ on all training data. 5 Wei et al Divide training data in two parts. Use one part to select best predictors. Use other part to find best λ using CV. Refit with λ on second part of training data.
14 Five selection procedures For each procedure set part of data apart as testset (we took 40%). 1 Cheat Select the best Training predictors using all data. Fit using Test Lasso. Select the best λ using crossvalidation (CV) on training data. Refit with that λ on all training data. 2 NoTest Select the best predictors using training data. Fit using Lasso. Select the best λ looking at the test data. 3 PartCV Select the best predictors using training data. Fit using Lasso. Select the best λ using CV on training data. Refit with λ on all training data. 4 FullCV CV: Select best predictors on 9/10th of training data. Get score for each λ, and evaluate on remaining 1/10th of training data. Select the best λ. Select best predictors using training data. Refit with λ on all training data. 5 Wei et al Divide training data in two parts. Use one part to select best predictors. Use other part to find best λ using CV. Refit with λ on second part of training data. CV select lambda evaluate
15 Five selection procedures For each procedure set part of data apart as testset (we took 40%). 1 Cheat Select the best Training predictors using all data. Fit using Test Lasso. Select the best λ using crossvalidation (CV) on training data. Refit with that λ on all training data. Training Test 2 NoTest Select the best predictors using training data. Fit using Lasso. Select the best λ looking at the test data. 3 PartCV Select the best predictors using training data. Fit using Lasso. Select the best λ using CV on training data. Refit with λ on all training data. 4 FullCV CV: Select best predictors on 9/10th of training data. Get score for each λ, and evaluate on remaining 1/10th of training data. Select the best λ. Select best predictors using training data. Refit with λ on all training data. 5 Wei et al Divide training data in two parts. Use one part to select best predictors. Use other part to find best λ using CV. Refit with λ on second part of training data. CV select lambda evaluate select lambda evaluate
16 Five selection procedures For each procedure set part of data apart as testset (we took 40%). 1 Cheat Select the best Training predictors using all data. Fit using Test Lasso. Select the best λ using crossvalidation (CV) on training data. Refit with that λ on all training data. Training Test 2 NoTestSelect the best predictors using training data. Fit using Lasso. Select the best λ looking at the test data. 3 PartCV Select the best predictors using training data. Fit Training CV Test using Lasso. Select the best λ using CV on training data. Refit with λ on all training data. 4 FullCV CV: Select best predictors on 9/10th of training data. Get score for each λ, and evaluate on remaining 1/10th of training data. Select the best λ. Select best predictors using training data. Refit with λ on all training data. 5 Wei et al Divide training data in two parts. Use one part to select best predictors. Use other part to find best λ using CV. Refit with λ on second part of training data. CV select lambda evaluate select lambda evaluate select lambda evaluate
17 Five selection procedures For each procedure set part of data apart as testset (we took 40%). 1 Cheat Select the best Training predictors using all data. Fit using Test Lasso. Select the best λ using crossvalidation (CV) on training data. Refit with that λ on all training data. Training Test 2 NoTest Select the best predictors using training data. Fit using Lasso. Select the best λ looking at the test data. 3 PartCV Select the best predictors using training data. Fit Training CV Test using Lasso. Select the best λ using CV on training data. Refit with λ on all training data. 4 FullCV CV: Select best predictors on 9/10th of training data. Training CV Test Get score for each λ, and evaluate on remaining 1/10th of training data. Select the best λ. Select best predictors using training data. Refit with λ on all training data. 5 Wei et al Divide training data in two parts. Use one part to select best predictors. Use other part to find best λ using CV. Refit with λ on second part of training data. CV select lambda evaluate select lambda evaluate select lambda evaluate select lambda evaluate
18 Five selection procedures For each procedure set part of data apart as testset (we took 40%). 1 Cheat Select the best Training predictors using all data. Fit using Test Lasso. Select the best λ using crossvalidation (CV) on training data. Refit with that λ on all training data. Training Test 2 NoTest Select the best predictors using training data. Fit using Lasso. Select the best λ looking at the test data. 3 PartCV Select the best predictors using training data. Fit Training CV Test using Lasso. Select the best λ using CV on training data. Refit with λ on all training data. 4 FullCV CV: Select best predictors on 9/10th of training data. Training CV Test Get score for each λ, and evaluate on remaining 1/10th of training data. Select the best λ. Select best predictors using training data. Refit with λ on all training data. 5 Wei et al Divide training data in two parts. Use one part to select best predictors. Use other part to find best λ using CV. Refit with λ on second part of training data. CV Selection Training CV Test select lambda evaluate select lambda evaluate select lambda evaluate select lambda evaluate select lambda evaluate
19 Crohn s disease  Lasso Approach Cheat NoTest PartCV FullCV Filter All Training Training CV Parameters CV Test CV CV
20 Crohn s  average loglikelihood log likelihood training approach 1 test approach 1 training approach 3 test approach 3 training approach 4 test approach number of SNPs considered
21 Number of SNPs actually used GLM 17 Fitting the best p predictors; select p like λ. filtered GLM 22 Fitting the best p predictors; remove SNPs with R 2 > 0.9, select p like λ. stepwise GLM 23 Stepwise selection using BIC. Lasso 10 top SNPs considered 6 25 top SNPs considered top SNPs considered top SNPs considered top SNPs considered top SNPs considered top SNPs considered top SNPs considered 177
22 Crohn s average log likelihood training log lik test log lik training AUC test AUC GLM filtered GLM stepwise GLM AUC number of SNPs considered
23 ROC  Crohn s Number of SNPs considered
24 SNPs used  Crohn s BIC AIC glm filtered lasso: number of top SNPs considered SNP not used SNP used SNP used
25 Approach 5  Wei et al. AJHG 2013
26 Approach 5  Wei et al. AJHG 2013 Selection set 13, 273 Training set α 13, 273 Test set 13, 273 Not used (1 α) 13, 273
27 Verify results on another GWAS NIDDK Crohn s disease GWAS Illumina 300K 792 cases, 932 controls Refit selected models on complete WTCCC data. Impute the essential SNPs in the NIDDK data using MACH. Use ten probability samples, average results. Apply model to NIDDK data. Adjust intercept of logistic model to correct for different case/control ratio.
28 This is a high bar Different platform: we have to impute > 90% of the SNPs. Different populations. Different continents. No information whether disease adjudication is comparable.
29 AUC  NIDDK and WTCCC comparison AUC NIDDK WTCCC number of SNPs considered
30 Crossstudy experiment fraction that is case WTCCC all as train NIDDK test fitted probability
31 Conclusions It is possible to develop prediction models with moderate predictive power using GWAS data. These predictive models produce results that are reproducible on other GWAS studies You have to be honest in crossvalidation. Using more SNPs than are identified as significant helps. A shrinkage method like the Lasso helps.
32 References/Thanks Thanks: Michael LeBlanc, Valerie Obenchain, Li Hsu References: Kooperberg C, LeBlanc M, Obenchain V (2010). Risk prediction using genomewide association studies. Genetic Epidemiology, 34, Wei Z, Wang W,... (2013). Large sample size, wide variant spectrum, and advanced machinelearning technique boost risk prediction for inflammatory bowel disease. American Journal of Human Genetics, 92,
Regularized Logistic Regression for Mind Reading with Parallel Validation
Regularized Logistic Regression for Mind Reading with Parallel Validation Heikki Huttunen, JukkaPekka Kauppi, Jussi Tohka Tampere University of Technology Department of Signal Processing Tampere, Finland
More informationPenalized Logistic Regression and Classification of Microarray Data
Penalized Logistic Regression and Classification of Microarray Data Milan, May 2003 Anestis Antoniadis Laboratoire IMAGLMC University Joseph Fourier Grenoble, France Penalized Logistic Regression andclassification
More informationFactors for success in big data science
Factors for success in big data science Damjan Vukcevic Data Science Murdoch Childrens Research Institute 16 October 2014 Big Data Reading Group (Department of Mathematics & Statistics, University of Melbourne)
More informationWe discuss 2 resampling methods in this chapter  crossvalidation  the bootstrap
Statistical Learning: Chapter 5 Resampling methods (Crossvalidation and bootstrap) (Note: prior to these notes, we'll discuss a modification of an earlier train/test experiment from Ch 2) We discuss 2
More informationModel selection in R featuring the lasso. Chris Franck LISA Short Course March 26, 2013
Model selection in R featuring the lasso Chris Franck LISA Short Course March 26, 2013 Goals Overview of LISA Classic data example: prostate data (Stamey et. al) Brief review of regression and model selection.
More informationFrom Disease Association to Risk Assessment: An Optimistic View from GenomeWide Association Studies on Type 1 Diabetes
From Disease Association to Risk Assessment: An Optimistic View from GenomeWide Association Studies on Type 1 Diabetes Zhi Wei 1., Kai Wang 2., HuiQi Qu 3, Haitao Zhang 2, Jonathan Bradfield 2, Cecilia
More informationLinear Model Selection and Regularization
Linear Model Selection and Regularization Recall the linear model Y = β 0 + β 1 X 1 + + β p X p + ɛ. In the lectures that follow, we consider some approaches for extending the linear model framework. In
More informationLasso on Categorical Data
Lasso on Categorical Data Yunjin Choi, Rina Park, Michael Seo December 14, 2012 1 Introduction In social science studies, the variables of interest are often categorical, such as race, gender, and nationality.
More informationBOOSTED REGRESSION TREES: A MODERN WAY TO ENHANCE ACTUARIAL MODELLING
BOOSTED REGRESSION TREES: A MODERN WAY TO ENHANCE ACTUARIAL MODELLING Xavier Conort xavier.conort@gearanalytics.com Session Number: TBR14 Insurance has always been a data business The industry has successfully
More informationLogistic regression: Model selection
Logistic regression: April 14 The WCGS data Measures of predictive power Today we will look at issues of model selection and measuring the predictive power of a model in logistic regression Our data set
More informationLeveraging electronic health records for predictive modeling of surgical complications. Grant Weller
Leveraging electronic health records for predictive modeling of surgical complications Grant Weller ISCB 2015 Utrecht NL August 26, 2015 Collaborators: David W. Larson, MD; Jenna Lovely, PharmD, RPh; Berton
More informationLogistic Regression (1/24/13)
STA63/CBB540: Statistical methods in computational biology Logistic Regression (/24/3) Lecturer: Barbara Engelhardt Scribe: Dinesh Manandhar Introduction Logistic regression is model for regression used
More informationActuarial. Modeling Seminar Part 2. Matthew Morton FSA, MAAA Ben Williams
Actuarial Data Analytics / Predictive Modeling Seminar Part 2 Matthew Morton FSA, MAAA Ben Williams Agenda Introduction Overview of Seminar Traditional Experience Study Traditional vs. Predictive Modeling
More informationExample: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.
Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation:  Feature vector X,  qualitative response Y, taking values in C
More informationAdequacy of Biomath. Models. Empirical Modeling Tools. Bayesian Modeling. Model Uncertainty / Selection
Directions in Statistical Methodology for Multivariable Predictive Modeling Frank E Harrell Jr University of Virginia Seattle WA 19May98 Overview of Modeling Process Model selection Regression shape Diagnostics
More informationMISSING DATA TECHNIQUES WITH SAS. IDRE Statistical Consulting Group
MISSING DATA TECHNIQUES WITH SAS IDRE Statistical Consulting Group ROAD MAP FOR TODAY To discuss: 1. Commonly used techniques for handling missing data, focusing on multiple imputation 2. Issues that could
More informationNew Work Item for ISO 35345 Predictive Analytics (Initial Notes and Thoughts) Introduction
Introduction New Work Item for ISO 35345 Predictive Analytics (Initial Notes and Thoughts) Predictive analytics encompasses the body of statistical knowledge supporting the analysis of massive data sets.
More informationClassification and Regression Trees
Classification and Regression Trees Bob Stine Dept of Statistics, School University of Pennsylvania Trees Familiar metaphor Biology Decision tree Medical diagnosis Org chart Properties Recursive, partitioning
More informationSTATISTICA Formula Guide: Logistic Regression. Table of Contents
: Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 SigmaRestricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary
More information5. Multiple regression
5. Multiple regression QBUS6840 Predictive Analytics https://www.otexts.org/fpp/5 QBUS6840 Predictive Analytics 5. Multiple regression 2/39 Outline Introduction to multiple linear regression Some useful
More informationRegularization and Variable Selection via the Elastic Net
ElasticNet Hui Zou, Stanford University 1 Regularization and Variable Selection via the Elastic Net Hui Zou and Trevor Hastie Department of Statistics Stanford University ElasticNet Hui Zou, Stanford University
More informationRidge Regression. Patrick Breheny. September 1. Ridge regression Selection of λ Ridge regression in R/SAS
Ridge Regression Patrick Breheny September 1 Patrick Breheny BST 764: Applied Statistical Modeling 1/22 Ridge regression: Definition Definition and solution Properties As mentioned in the previous lecture,
More informationRisk pricing for Australian Motor Insurance
Risk pricing for Australian Motor Insurance Dr Richard Brookes November 2012 Contents 1. Background Scope How many models? 2. Approach Data Variable filtering GLM Interactions Credibility overlay 3. Model
More informationGENETIC DATA ANALYSIS
GENETIC DATA ANALYSIS 1 Genetic Data: Future of Personalized Healthcare To achieve personalization in Healthcare, there is a need for more advancements in the field of Genomics. The human genome is made
More informationCombining Data from Different Genotyping Platforms. Gonçalo Abecasis Center for Statistical Genetics University of Michigan
Combining Data from Different Genotyping Platforms Gonçalo Abecasis Center for Statistical Genetics University of Michigan The Challenge Detecting small effects requires very large sample sizes Combined
More informationPublication List. Chen Zehua Department of Statistics & Applied Probability National University of Singapore
Publication List Chen Zehua Department of Statistics & Applied Probability National University of Singapore Publications Journal Papers 1. Y. He and Z. Chen (2014). A sequential procedure for feature selection
More informationLocation matters. 3 techniques to incorporate geospatial effects in one's predictive model
Location matters. 3 techniques to incorporate geospatial effects in one's predictive model Xavier Conort xavier.conort@gearanalytics.com Motivation Location matters! Observed value at one location is
More informationKnowledge Discovery and Data Mining. Bootstrap review. Bagging Important Concepts. Notes. Lecture 19  Bagging. Tom Kelsey. Notes
Knowledge Discovery and Data Mining Lecture 19  Bagging Tom Kelsey School of Computer Science University of St Andrews http://tom.host.cs.standrews.ac.uk twk@standrews.ac.uk Tom Kelsey ID505919B &
More informationCross Validation. Dr. Thomas Jensen Expedia.com
Cross Validation Dr. Thomas Jensen Expedia.com About Me PhD from ETH Used to be a statistician at Link, now Senior Business Analyst at Expedia Manage a database with 720,000 Hotels that are not on contract
More informationSection 6: Model Selection, Logistic Regression and more...
Section 6: Model Selection, Logistic Regression and more... Carlos M. Carvalho The University of Texas McCombs School of Business http://faculty.mccombs.utexas.edu/carlos.carvalho/teaching/ 1 Model Building
More informationVertical data integration for melanoma prognosis. Australia 3 Melanoma Institute Australia, NSW 2060 Australia. kaushala@maths.usyd.edu.au.
Vertical integration for melanoma prognosis Kaushala Jayawardana 1,4, Samuel Müller 1, SarahJane Schramm 2,3, Graham J. Mann 2,3 and Jean Yang 1 1 School of Mathematics and Statistics, University of Sydney,
More informationPackage metafuse. November 7, 2015
Type Package Package metafuse November 7, 2015 Title Fused Lasso Approach in Regression Coefficient Clustering Version 1.01 Date 20151106 Author Lu Tang, Peter X.K. Song Maintainer Lu Tang
More informationModern regression 2: The lasso
Modern regression 2: The lasso Ryan Tibshirani Data Mining: 36462/36662 March 21 2013 Optional reading: ISL 6.2.2, ESL 3.4.2, 3.4.3 1 Reminder: ridge regression and variable selection Recall our setup:
More informationPredictive Modeling and Big Data
Predictive Modeling and Presented by Eileen Burns, FSA, MAAA Milliman Agenda Current uses of predictive modeling in the life insurance industry Potential applications of 2 1 June 16, 2014 [Enter presentation
More informationOur future in big data science
Our future in big data science Damjan Vukcevic http://damjan.vukcevic.net/ 13 October 2015 SSA Canberra, Young Statisticians Workshop What is big data? You know it when you see it? Telltale signs: Need
More informationLecture 3: Linear methods for classification
Lecture 3: Linear methods for classification Rafael A. Irizarry and Hector Corrada Bravo February, 2010 Today we describe four specific algorithms useful for classification problems: linear regression,
More informationYiming Peng, Department of Statistics. February 12, 2013
Regression Analysis Using JMP Yiming Peng, Department of Statistics February 12, 2013 2 Presentation and Data http://www.lisa.stat.vt.edu Short Courses Regression Analysis Using JMP Download Data to Desktop
More informationI L L I N O I S UNIVERSITY OF ILLINOIS AT URBANACHAMPAIGN
Beckman HLM Reading Group: Questions, Answers and Examples Carolyn J. Anderson Department of Educational Psychology I L L I N O I S UNIVERSITY OF ILLINOIS AT URBANACHAMPAIGN Linear Algebra Slide 1 of
More informationCombining Multiple Imputation and Inverse Probability Weighting
Combining Multiple Imputation and Inverse Probability Weighting Shaun Seaman 1, Ian White 1, Andrew Copas 2,3, Leah Li 4 1 MRC Biostatistics Unit, Cambridge 2 MRC Clinical Trials Unit, London 3 UCL Research
More informationChapter 13 Introduction to Nonlinear Regression( 非 線 性 迴 歸 )
Chapter 13 Introduction to Nonlinear Regression( 非 線 性 迴 歸 ) and Neural Networks( 類 神 經 網 路 ) 許 湘 伶 Applied Linear Regression Models (Kutner, Nachtsheim, Neter, Li) hsuhl (NUK) LR Chap 10 1 / 35 13 Examples
More informationα α λ α = = λ λ α ψ = = α α α λ λ ψ α = + β = > θ θ β > β β θ θ θ β θ β γ θ β = γ θ > β > γ θ β γ = θ β = θ β = θ β = β θ = β β θ = = = β β θ = + α α α α α = = λ λ λ λ λ λ λ = λ λ α α α α λ ψ + α =
More informationStatistical issues in the analysis of microarray data
Statistical issues in the analysis of microarray data Daniel Gerhard Institute of Biostatistics Leibniz University of Hannover ESNATS Summerschool, Zermatt D. Gerhard (LUH) Analysis of microarray data
More informationNonparametric statistics and model selection
Chapter 5 Nonparametric statistics and model selection In Chapter, we learned about the ttest and its variations. These were designed to compare sample means, and relied heavily on assumptions of normality.
More informationGovernment of Russian Federation. Faculty of Computer Science School of Data Analysis and Artificial Intelligence
Government of Russian Federation Federal State Autonomous Educational Institution of High Professional Education National Research University «Higher School of Economics» Faculty of Computer Science School
More informationHarnessing the information contained within genomewide association studies to improve individual prediction of complex disease risk
Harnessing the information contained within genomewide association studies to improve individual prediction of complex disease risk David M. Evans 1,, Peter M. Visscher 2 and Naomi R. Wray 2 1 Department
More informationCopyright 2006, SAS Institute Inc. All rights reserved. Predictive Modeling using SAS
Predictive Modeling using SAS Purpose of Predictive Modeling To Predict the Future x To identify statistically significant attributes or risk factors x To publish findings in Science, Nature, or the New
More informationHow is Big Data Different? A Paradigm Shift
How is Big Data Different? A Paradigm Shift Jennifer Clarke, Ph.D. Associate Professor Department of Statistics Department of Food Science and Technology University of Nebraska Lincoln ASA Snake River
More informationAuxiliary Variables in Mixture Modeling: 3Step Approaches Using Mplus
Auxiliary Variables in Mixture Modeling: 3Step Approaches Using Mplus Tihomir Asparouhov and Bengt Muthén Mplus Web Notes: No. 15 Version 8, August 5, 2014 1 Abstract This paper discusses alternatives
More informationPredicting The Risk Of Rheumatoid Arthritis
Predicting The Risk Of Rheumatoid Arthritis Modelling Genetic And Environmental Risk Factors Ian Scott Arthritis Research UK Clinical Research Fellow Declaration Of Interests: No Competing Interests Describe
More informationInterpretive Risk Assessment on GWA Data with Sparse Linear Regression
Interpretive Risk Assessment on GWA Data with Sparse Linear Regression Ingrid Brænne, Kai Labusch, Thomas Martinetz and Amir Madany Mamlouk Institute for Neuro and Bioinformatics, University of Luebeck
More informationStatistical Machine Learning
Statistical Machine Learning UoC Stats 37700, Winter quarter Lecture 4: classical linear and quadratic discriminants. 1 / 25 Linear separation For two classes in R d : simple idea: separate the classes
More informationLocal classification and local likelihoods
Local classification and local likelihoods November 18 knearest neighbors The idea of local regression can be extended to classification as well The simplest way of doing so is called nearest neighbor
More information[3] Big Data: Model Selection
[3] Big Data: Model Selection Matt Taddy, University of Chicago Booth School of Business faculty.chicagobooth.edu/matt.taddy/teaching [3] Making Model Decisions OutofSample vs InSample performance Regularization
More informationWednesday PM. Multiple regression. Multiple regression in SPSS. Presentation of AM results Multiple linear regression. Logistic regression
Wednesday PM Presentation of AM results Multiple linear regression Simultaneous Stepwise Hierarchical Logistic regression Multiple regression Multiple regression extends simple linear regression to consider
More informationOn the Creation of the BeSiVa Algorithm to Predict Voter Support
On the Creation of the BeSiVa Algorithm to Predict Voter Support By Benjamin Rogers Submitted to the graduate degree program in Political Science and the Graduate Faculty of the University of Kansas in
More informationAgenda. Mathias Lanner Sas Institute. Predictive Modeling Applications. Predictive Modeling Training Data. Beslutsträd och andra prediktiva modeller
Agenda Introduktion till Prediktiva modeller Beslutsträd Beslutsträd och andra prediktiva modeller Mathias Lanner Sas Institute Pruning Regressioner Neurala Nätverk Utvärdering av modeller 2 Predictive
More informationLeast Squares Estimation
Least Squares Estimation SARA A VAN DE GEER Volume 2, pp 1041 1045 in Encyclopedia of Statistics in Behavioral Science ISBN13: 9780470860809 ISBN10: 0470860804 Editors Brian S Everitt & David
More informationPenalized regression: Introduction
Penalized regression: Introduction Patrick Breheny August 30 Patrick Breheny BST 764: Applied Statistical Modeling 1/19 Maximum likelihood Much of 20thcentury statistics dealt with maximum likelihood
More informationEstimation of σ 2, the variance of ɛ
Estimation of σ 2, the variance of ɛ The variance of the errors σ 2 indicates how much observations deviate from the fitted surface. If σ 2 is small, parameters β 0, β 1,..., β k will be reliably estimated
More informationThe general form of the PROC GLM statement is
Linear Regression Analysis using PROC GLM Regression analysis is a statistical method of obtaining an equation that represents a linear relationship between two variables (simple linear regression), or
More informationData Mining and Data Warehousing. Henryk Maciejewski. Data Mining Predictive modelling: regression
Data Mining and Data Warehousing Henryk Maciejewski Data Mining Predictive modelling: regression Algorithms for Predictive Modelling Contents Regression Classification Auxiliary topics: Estimation of prediction
More informationAdvanced Linear Modeling
Ronald Christensen Advanced Linear Modeling Multivariate, Time Series, and Spatial Data; Nonparametric Regression and Response Surface Maximization Second Edition Springer Preface to the Second Edition
More informationBenchmarking of different classes of models used for credit scoring
Benchmarking of different classes of models used for credit scoring We use this competition as an opportunity to compare the performance of different classes of predictive models. In particular we want
More informationLinear Classification. Volker Tresp Summer 2015
Linear Classification Volker Tresp Summer 2015 1 Classification Classification is the central task of pattern recognition Sensors supply information about an object: to which class do the object belong
More informationTHE HYBRID CARTLOGIT MODEL IN CLASSIFICATION AND DATA MINING. Dan Steinberg and N. Scott Cardell
THE HYBID CATLOGIT MODEL IN CLASSIFICATION AND DATA MINING Introduction Dan Steinberg and N. Scott Cardell Most datamining projects involve classification problems assigning objects to classes whether
More informationJetBlue Airways Stock Price Analysis and Prediction
JetBlue Airways Stock Price Analysis and Prediction Team Member: Lulu Liu, Jiaojiao Liu DSO530 Final Project JETBLUE AIRWAYS STOCK PRICE ANALYSIS AND PREDICTION 1 Motivation Started in February 2000, JetBlue
More informationStatistics in Retail Finance. Chapter 2: Statistical models of default
Statistics in Retail Finance 1 Overview > We consider how to build statistical models of default, or delinquency, and how such models are traditionally used for credit application scoring and decision
More informationLASSO Regression. Machine Learning/Statistics for Big Data CSE599C1/STAT592, University of Washington Emily Fox February 21 th, 2013.
Case Study 3: fmri Prediction LASSO Regression Machine Learning/Statistics for Big Data CSE599C1/STAT592, University of Washington Emily Fox February 21 th, 2013 Emily Fo013 1 LASSO Regression LASSO: least
More informationFINDING SUBGROUPS OF ENHANCED TREATMENT EFFECT. Jeremy M G Taylor Jared Foster University of Michigan Steve Ruberg Eli Lilly
FINDING SUBGROUPS OF ENHANCED TREATMENT EFFECT Jeremy M G Taylor Jared Foster University of Michigan Steve Ruberg Eli Lilly 1 1. INTRODUCTION and MOTIVATION 2. PROPOSED METHOD Random Forests Classification
More informationPackage acrm. R topics documented: February 19, 2015
Package acrm February 19, 2015 Type Package Title Convenience functions for analytical Customer Relationship Management Version 0.1.1 Date 20140328 Imports dummies, randomforest, kernelfactory, ada Author
More informationNeural Networks & Boosting
Neural Networks & Boosting Bob Stine Dept of Statistics, School University of Pennsylvania Questions How is logistic regression different from OLS? Logistic mean function for probabilities Larger weight
More informationData Mining. Nonlinear Classification
Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Nonlinear Classification Classes may not be separable by a linear boundary Suppose we randomly generate a data set as follows: X has range between 0 to 15
More informationModelbased boosting in R
Modelbased boosting in R Introduction to Gradient Boosting Matthias Schmid Institut für Medizininformatik, Biometrie und Epidemiologie (IMBE) FriedrichAlexanderUniversität ErlangenNürnberg Statistical
More informationPREDICTIVE MODELING IN HEALTHCARE COSTS USING REGRESSION TECHNIQUES
PREDICTIVE MODELING IN HEALTHCARE COSTS USING REGRESSION TECHNIQUES Michael Loginov, Emily Marlow, Victoria Potruch University of California, Santa Barbara Introduction Building a model that predicts an
More informationLassobased Spam Filtering with Chinese Emails
Journal of Computational Information Systems 8: 8 (2012) 3315 3322 Available at http://www.jofcis.com Lassobased Spam Filtering with Chinese Emails Zunxiong LIU 1, Xianlong ZHANG 1,, Shujuan ZHENG 2 1
More informationResearch Methods & Experimental Design
Research Methods & Experimental Design 16.422 Human Supervisory Control April 2004 Research Methods Qualitative vs. quantitative Understanding the relationship between objectives (research question) and
More informationModelling and added value
Modelling and added value Course: Statistical Evaluation of Diagnostic and Predictive Models Thomas Alexander Gerds (University of Copenhagen) Summer School, Barcelona, June 30, 2015 1 / 53 Multiple regression
More informationSession 11 PD, Provider Perspectives of Values Based Payment Programs. Moderator: William T. O'Brien, FSA, FCA
Session 11 PD, Provider Perspectives of Values Based Payment Programs Moderator: William T. O'Brien, FSA, FCA Presenters: Donald Fry, M.D. Lillian Louise Dittrick, FSA, MAAA Colleen Audrey Norris, ASA,
More informationBig Data Analytics for Healthcare
Big Data Analytics for Healthcare Jimeng Sun Chandan K. Reddy Healthcare Analytics Department IBM TJ Watson Research Center Department of Computer Science Wayne State University Tutorial presentation at
More informationVI. Introduction to Logistic Regression
VI. Introduction to Logistic Regression We turn our attention now to the topic of modeling a categorical outcome as a function of (possibly) several factors. The framework of generalized linear models
More informationExamining a Fitted Logistic Model
STAT 536 Lecture 16 1 Examining a Fitted Logistic Model Deviance Test for Lack of Fit The data below describes the male birth fraction male births/total births over the years 1931 to 1990. A simple logistic
More information" Y. Notation and Equations for Regression Lecture 11/4. Notation:
Notation: Notation and Equations for Regression Lecture 11/4 m: The number of predictor variables in a regression Xi: One of multiple predictor variables. The subscript i represents any number from 1 through
More informationEnsemble Approach for the Classification of Imbalanced Data
Ensemble Approach for the Classification of Imbalanced Data Vladimir Nikulin 1, Geoffrey J. McLachlan 1, and Shu Kay Ng 2 1 Department of Mathematics, University of Queensland v.nikulin@uq.edu.au, gjm@maths.uq.edu.au
More informationEvidence to Action: Use of Predictive Models for Beach Water Postings
Evidence to Action: Use of Predictive Models for Beach Water Postings Canadian Society for Epidemiology and Biostatistics Caitlyn Paget, June 4 th 2015 Goal is to improve program delivery Can we improve
More informationInteger Programming: Algorithms  3
Week 9 Integer Programming: Algorithms  3 OPR 992 Applied Mathematical Programming OPR 992  Applied Mathematical Programming  p. 1/12 DantzigWolfe Reformulation Example Strength of the Linear Programming
More informationPredictive Gene Signature Selection for Adjuvant Chemotherapy in NonSmall Cell Lung Cancer Patients
Predictive Gene Signature Selection for Adjuvant Chemotherapy in NonSmall Cell Lung Cancer Patients by Li Liu A practicum report submitted to the Department of Public Health Sciences in conformity with
More informationPredicting Health Care Costs by Twopart Model with Sparse Regularization
Predicting Health Care Costs by Twopart Model with Sparse Regularization Atsuyuki Kogure Keio University, Japan July, 2015 Abstract We consider the problem of predicting health care costs using the twopart
More informationPart 2: Analysis of Relationship Between Two Variables
Part 2: Analysis of Relationship Between Two Variables Linear Regression Linear correlation Significance Tests Multiple regression Linear Regression Y = a X + b Dependent Variable Independent Variable
More informationJoint models for classification and comparison of mortality in different countries.
Joint models for classification and comparison of mortality in different countries. Viani D. Biatat 1 and Iain D. Currie 1 1 Department of Actuarial Mathematics and Statistics, and the Maxwell Institute
More informationCausal Leading Indicators Detection for Demand Forecasting
Causal Leading Indicators Detection for Demand Forecasting Yves R. Sagaert, ElHoussaine Aghezzaf, Nikolaos Kourentzes, Bram Desmet Department of Industrial Management, Ghent University 13/07/2015 EURO
More informationFitting Subjectspecific Curves to Grouped Longitudinal Data
Fitting Subjectspecific Curves to Grouped Longitudinal Data Djeundje, Viani HeriotWatt University, Department of Actuarial Mathematics & Statistics Edinburgh, EH14 4AS, UK Email: vad5@hw.ac.uk Currie,
More informationGLM III: Advanced Modeling Strategy 2005 CAS Seminar on Predictive Modeling Duncan Anderson MA FIA Watson Wyatt Worldwide
GLM III: Advanced Modeling Strategy 25 CAS Seminar on Predictive Modeling Duncan Anderson MA FIA Watson Wyatt Worldwide W W W. W A T S O N W Y A T T. C O M Agenda Introduction Testing the link function
More informationClassification of Bad Accounts in Credit Card Industry
Classification of Bad Accounts in Credit Card Industry Chengwei Yuan December 12, 2014 Introduction Risk management is critical for a credit card company to survive in such competing industry. In addition
More informationBasic Statistics and Data Analysis for Health Researchers from Foreign Countries
Basic Statistics and Data Analysis for Health Researchers from Foreign Countries Volkert Siersma siersma@sund.ku.dk The Research Unit for General Practice in Copenhagen Dias 1 Content Quantifying association
More informationMissing Data: Part 1 What to Do? Carol B. Thompson Johns Hopkins Biostatistics Center SON Brown Bag 3/20/13
Missing Data: Part 1 What to Do? Carol B. Thompson Johns Hopkins Biostatistics Center SON Brown Bag 3/20/13 Overview Missingness and impact on statistical analysis Missing data assumptions/mechanisms Conventional
More informationApplied Data Mining Analysis: A StepbyStep Introduction Using RealWorld Data Sets
Applied Data Mining Analysis: A StepbyStep Introduction Using RealWorld Data Sets http://info.salfordsystems.com/jsm2015ctw August 2015 Salford Systems Course Outline Demonstration of two classification
More informationSmart ESG Integration: Factoring in Sustainability
Smart ESG Integration: Factoring in Sustainability Abstract Smart ESG integration is an advanced ESG integration method developed by RobecoSAM s Quantitative Research team. In a first step, an improved
More information11. Analysis of Casecontrol Studies Logistic Regression
Research methods II 113 11. Analysis of Casecontrol Studies Logistic Regression This chapter builds upon and further develops the concepts and strategies described in Ch.6 of Mother and Child Health:
More informationLogistic Regression. http://faculty.chass.ncsu.edu/garson/pa765/logistic.htm#sigtests
Logistic Regression http://faculty.chass.ncsu.edu/garson/pa765/logistic.htm#sigtests Overview Binary (or binomial) logistic regression is a form of regression which is used when the dependent is a dichotomy
More informationModel Validation Techniques
Model Validation Techniques Kevin Mahoney, FCAS kmahoney@ travelers.com CAS RPM Seminar March 17, 2010 Uses of Statistical Models in P/C Insurance Examples of Applications Determine expected loss cost
More information