# Sample Size Designs to Assess Controls

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1 Sample Size Designs to Assess Controls B. Ricky Rambharat, PhD, PStat Lead Statistician Office of the Comptroller of the Currency U.S. Department of the Treasury Washington, DC FCSM Research Conference Session B-2 Nov. 4 th, 2013

2 Disclaimer The views expressed in this presentation are solely those of the author. This talk neither represents the opinions of the U.S. Department of the Treasury nor the Office of the Comptroller of the Currency (OCC). All errors are author's responsibility. As this talk is based on a paper in draft status, please do not reference without conferring with the author.

3 Motivation: How to Set Control?

4 Examples of Controls Currency Transaction Report (CTR) Flag if > US \$10, Credit score cutoffs Accept / Deny applicant College applications and entrance exams Job applications and interview process Challenge: control-setting and risk

5 Type I vs. Type II Error Control Type I error: False positives Compliance costs Manpower Resource constraints Job offer to weak candidates Accept weak students Type II error: False negatives Criminal activity Unfair consumer practices

6 Sample Design & Analysis C Goal: Assess control C Definition: What is an error? Q: What sample size n to gauge Type II error? Assumption: Rare error view? Result: estimate error rate below C Follow-on: How to do deep-dives? 2-stage?

7 Sampling Models Hyper-Geometric distribution Sampling without replacement model Normal distribution (approx.) Cochran (1977) Accommodate rare errors? Binomial distribution (approx.) n, p Any 0<p<1; numerical solution Poisson distribution (approx.) Related to Binomial

8 Inference I: Total Errors Estimation of population error rate: p Hypothesis-testing problems & deep-dives Ballpark notion of error incidence Useful for regression modeling? Estimation of total number of errors: M Relevance to key policy decisions A different view of risk Informative for deep-dive studies?

9 Inference II: Total Errors Sampling models for M (discrete data) Hyper-Geometric distribution Binomial (alternative) Normal (OK could be inaccurate) Computations Prob. of exactly k errors in a sample of size n Prob. of at least k errors in a sample of size n

10 Bayesian Estimation Goal: Posterior distribution of M How are total errors distributed? Variability measures Risk evaluations Relevance for deep-dive analysis Markov chain Monte Carlo Alternative: Posterior distribution of p Beta-Binomial framework

11 Discrete Uniform Prior Distributions Mass on {0,1,2,,N} Vague / Non-informative (reflects ignorance) Poisson Mass on {0,1,2,3, } Leverage data Empirical Bayes Plan: use mixture to construct prior, p(m). Balance ignorance and data.

12 Likelihood Function Quantities of interest Pr(exactly k errors) Pr(at least k errors) Hyper-Geometric (HG) likelihood Limited for large N (population size) Workaround: Compute log-probabilities

13 Proposal Distribution Poisson proposal distribution Choose λ: another mixture? Weight λ ( ignorance vs. MCMC learning ) MCMC mixing properties Convergence of MCMC chain Satisfaction of detailed balance condition

14 Numerical Experiments INPUTS Set 1 Set 2 Set 3 Set 4 k (# of errors) n (sample size) N (pop. size) 30,000 30,000 30,000 1,000,000 Weights: Prior {DU; Pois} Weights: Prop. {Fix; Dynamic} {0.999; 0.001} {0.01; 0.99} {0.01; 0.99} {0; 1} {0.90; 0.10} {0.01; 0.99} {0.001; 0.999} {0; 1}

15 Posterior Results: Univariate Errors

16 Deep-Dive Sample Design Leverage stage 1 results for stage 2 Posterior simulations and deep-dive Probabilistic view of risk Easier for uni-modal posteriors Practical constraints vs. posterior outcome E.g., bi-modal distributions Relevance for sample size n = n(posterior)

17 Multinomial Experiment (Schematic)

18 Sample Size & Control-Setting Multinomial probabilities Chi-squared tests of significance Deep-dive and sample size by segment Estimate total errors in risky sub-populations Straightforward mapping to error rate Relevance to control settings

19 Multiple Levels of Errors Attribute sampling: binary error incidence E.g., degree of injury to consumer Posterior analysis Bivariate HG likelihood Bivariate Poisson proposal distribution Co-dependence between errors? General modeling E.g., Ordinal, Multinomial logit

20 Preliminary Conclusions Total number of errors vs. Error rate Posterior analysis of total errors Subjective / Data influence Sample design and posterior results Link control settings to risky sub-populations

21 Future Work Framework for multiple levels of errors MCMC estimation / posterior analysis Sample size and posterior results Logic for control settings Computational tool

22 END: THANK YOU

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