Survey Sampling Inference

My research in survey sampling focuses on model-based (Bayesian) methods for complex survey designs that are robust to misspecification, and comparing the resulting inferences to classical methods based on the randomization distribution. A theme of my work is that a Bayesian approach that incorporates the design variables in the model can yield results that have good frequentist properties, and dominate design-based alternatives (Little, 2004, 2011, 2012, 2022, Little & Zheng 2007, Zangeneh & Little 2015). My work has developed this theme in the context of unequal probability sampling (Little 1983, 1991; Zheng & Little 2003, 2004, 2005; Little & Zheng 2007; Chen, Elliott & Little 2010), poststratification (Little & Wu 1991, Little 1993a, Zangeneh & Little 2022), and weight smoothing (Lazzeroni & Little 1998, Elliott & Little 2000). Another focus is developing measures of deviation from ignorable (e.g. probability) selection using pattern-mixture models (Andridge et al. 2019, Little et al. 2020, Boonstra et al. 2021, West et al. 2021).

Methods for survey nonresponse are discussed in the section on missing data research.

References

Andridge, R.R., West, B.T., Little, R.J.A., Boonstra, P.S. and Alvarado-Leiton, F. (2019). Indices of non-ignorable selection bias for proportions estimated from non-probability samples. Applied Statistics, 68, 5, 1465-1483. DOI: 10.1111/rssc.12371

Boonstra, P.S., Little, R.J., West, B.T., Andridge*, R.R. and Alvaredo-Leiton, F. (2021). A simulation study of diagnostics for bias in non-probability samples. Journal of Official Statistics, 37, 3, 751-769.

Chen, Q., Elliott, M.R. & Little, R.J. (2010). Bayesian Penalized Spline Model-Based Estimation of the Finite Population Proportion for Probability-Proportional-to-Size Samples. Survey Methodology, 36, 23-34.

Little, R.J. (2012). Calibrated Bayes: an Alternative Inferential Paradigm for Official Statistics (with discussion and rejoinder). Journal of Official Statistics, 28, 3, 309-372.

Little, R.J. (2011). Calibrated Bayes, for Statistics in General, and Missing Data in Particular (with Discussion and Rejoinder). Statistical Science 26, 2, 162-186. DOI: 10.1214/10-STS318.

Little, R.J. (2022). Bayes, buttressed by design-based ideas, is the best overarching paradigm for sample survey inference. To appear in Survey Methodology.

Little, R.J., West, B.T., Boonstra, P.S. and Hu, J. (2020). Measures of the Degree of Departure from Ignorable Sample Selection. Journal of Survey Statistics and Methodology, 8, 5, 932-964.

Elliott, M. R. & Little, R.J.A. (2000). Model-Based Alternatives to Trimming Survey Weights. Journal of Official Statistics, 16, No. 3, 191-209.

Elliott, M. & Little, R.J. (2005). A Bayesian Approach to Census 2000 Evaluation Using A.C.E. Survey Data and Demographic Analysis. Journal of the American Statistical Association, 100, 380-388.

Lazzeroni, L.C. & Little, R.J.A. (1998). Random-Effects Models for Smoothing Post-Stratification Weights. Journal of Official Statistics, 14, 1, 61-78.

Little, R.J.A. (1983). Estimating a finite population mean from unequal probability samples. Journal of the American Statistical Association, 78, 596-604.

Little, R.J.A. (1991). Inference with survey weights. Journal of Official Statistics, 7, 405-424.

Little, R.J.A. (1993). Post‑Stratification: a Modeler's Perspective. Journal of the American Statistical Association 88, 1001-1012. {97}

Little, R.J.A. (2004). To Model or Not to Model? Competing Modes of Inference for Finite Population Sampling. Journal of the American Statistical Association, 99, 546-556.

Little, R.J.A. & Wu, M.M. (1991). Models for Contingency Tables with Known Margins when Target and Sampled Populations Differ. Journal of the American Statistical Association, 86, 87‑95.

Little, R.J. & Zheng, H. (2007). The Bayesian Approach to the Analysis of Finite Population Surveys. Bayesian Statistics 8, J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith & M. West (Eds.), 283-302 (with discussion and rejoinder), Oxford University Press.

West, B.T., Little, R.J., Andridge, R.R., Boonstra, P.S., Ware, E.B., Pandit, A., Alvarado-Leiton, F. (2021). Measures of Selection Bias in Regression Coefficients Estimated from Non-Probability Samples. The Annals of Applied Statistics, 15 (3), 1556-1581.

Zanganeh, S.Z. & Little, R.J. (2015). Bayesian inference for the finite population total in heteroscedastic probability proportional to size samples. Journal of Survey Statistics and Methodology, 3, 162-192.

Zangeneh, S.Z. and Little, R.J. (2022). Likelihood based estimation of the finite population mean with post-stratification information under nonignorable nonresponse. To appear in International Statistical Review.

Zheng, H. & Little, R.J. (2003). Penalized Spline Model-Based Estimation of the Finite Population Total from Probability-Proportional-To-Size Samples. Journal of Official Statistics, 19, 2, 99-117.

Zheng, H. & Little, R.J. (2004). Penalized Spline Nonparametric Mixed Models for Inference about a Finite Population Mean from Two-Stage Samples. Survey Methodology, 30, 2, 209-218.

Zheng, H. & Little, R.J. (2005). Inference for the Population Total from Probability-Proportional-to-Size Samples Based on Predictions from a Penalized Spline Nonparametric Model. Journal of Official Statistics, 21, 1-20.

(b) Disclosure Limitation

An, D. & Little, R.J. (2007). Multiple Imputation: an Alternative to Top-Coding for Statistical Disclosure Control. Journal of the Royal Statistical Society, Ser. A, 170, 4, 923-940. {15}

An, D., Little, R.J. & McNally, J. (2010). A Multiple Imputation Approach to Disclosure Limitation for High-Age Individuals in Longitudinal Studies. Statistics in Medicine, 29, 17, 1769-1778.

Little, R.J.A. (1993b). Statistical Analysis of Masked Data. Journal of Official Statistics 9, 407-426.

Little, R.J., Liu, F. & Raghunathan, T. (2004). Statistical Disclosure Techniques Based on Multiple Imputation. In “Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives”, A. Gelman & X.-L. Meng, eds., pp. 141-152. Wiley: New York. {18}

Liu, F. & Little, R.J.A. (2002). Multiple Imputation and Statistical Disclosure Control in Microdata. Proceedings of the Survey Research Methods Section, American Statistical Association 2002, 2133-2138.

Liu, F. & Little, R.J.A. (2003). Smike vs. Data Swapping and PRAM for Statistical Disclosure Control in Microdata: a Simulated Study. Proceedings of the Survey Research Methods Section, American Statistical Association 2003, 2497-2502.

(revised December 8, 2011)