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Model based estimation of population total in presence of non-ignorable non-response


Autoři: Shakeel Ahmed aff001;  Javid Shabbir aff001
Působiště autorů: Department of Statistics Quaid-i-Azam University, Islamabad, Pakistan aff001
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222701

Souhrn

The problem of handling non-ignorable non-response has been typically addressed under the design-based approach using the well-known sub-sampling technique introduced by Hansen and Hurwitz [1946, Journal of the American Statistical Association, Vol 41(236), Page 517- 529]. Alternatively, the model-based paradigm emphasizes on utilizing the underlying model relationship between the outcome variable and one or more covariate(s) whose population values are known prior to the survey. This article utilizes the model relationship between the study variable and covariate(s) for handling non-ignorable non-response and obtaining an unbiased estimator for the population total under the sub-sampling technique. The main idea is to combine the estimates obtained from the sample on first call and the sub-sample from second call using separate model relationships. The contribution of this paper helps us in providing unbiased estimates with an improved efficiency under model-based paradigm in presence of non-ignorable non-response. The provided method is more economical than the available estimators under callback methods as we are working sub-sampling and also increase response rate as a stronger mode of interview is employed for data collection. A numerical study using Monte Carlo is presented to illustrate the behavior of the proposed and the efficiency comparison.

Klíčová slova:

Blood – Employment – Surveys – Census – Data processing – Statistical inference – Blood transfusion


Zdroje

1. Barton J, Bain C, Hennekens CH, Rosner B, Belanger C, Roth A, Speizer FE. Characteristics of respondents and non-respondents to a mailed questionnaire. American Journal of Public Health. 1980 Aug;70(8):823–5. doi: 10.2105/ajph.70.8.823 7416342

2. Wood AM, White IR, Hotopf M. Using number of failed contact attempts to adjust for non–ignorable non–response. Journal of the Royal Statistical Society–Series A Statistics in Society. 2006 Jul 1;169(3):525–542. doi: 10.1111/j.1467-985X.2006.00405.x

3. Peytchev A, Baxter RK, Carley-Baxter LR. Not all survey effort is equal: Reduction of non–response bias and non–response error. Public Opinion Quarterly. 2009 Jan 1;73(4):785–806. doi: 10.1093/poq/nfp037

4. Biemer PP, Chen P, Wang K. Using level–of–effort paradata in non–response adjustments with application to field surveys. Journal of the Royal Statistical Society: Series A (Statistics in Society). 2013 Jan 1;176(1):147–68. doi: 10.1111/j.1467-985X.2012.01058.x

5. Knudsen AK, Hotopf M, Skogen JC, Overland S, Mykletun A. The health status of nonparticipants in a population-based health study: the Hordaland Health Study. American journal of epidemiology. 2010 Sep 15;172(11):1306–14. doi: 10.1093/aje/kwq257 20843863

6. Copas AJ, Farewell VT. Dealing with non–ignorable non–response by using an enthusiasm-to-respond variable. Journal of the Royal Statistical Society: Series A (Statistics in Society). 1998;161(3):385–96. doi: 10.1111/1467-985X.00115

7. Moore RE, editor. Reliability in computing: the role of interval methods in scientific computing. Elsevier; 2014 May 10.

8. Huang GB. Tai-kang (2009). Taking the Opportunities of Health Care Reform to Develop Clinical Pharmacy. Asian Journal of Social Pharmacy 4.;2:65–9.

9. White IR, Kalaitzaki E, Thompson SG. Allowing for missing outcome data and incomplete uptake of randomised interventions, with application to an Internet–based alcohol trial. Statistics in medicine. 2011 Nov 30;30(27):3192–207. doi: 10.1002/sim.4360 21948462

10. Guan Z, Leung DH, Qin J. Semi–parametric maximum likelihood inference for non–ignorable non–response with callbacks. Scandinavian Journal of Statistics. 2018 Dec;45(4):962–84. doi: 10.1111/sjos.12330

11. Hansen MH, Hurwitz WN. The problem of non-response in sample surveys. Journal of the American Statistical Association. 1946 Dec 1;41(236):517–29. doi: 10.1080/01621459.1946.10501894 20279350

12. Khare BB, Srivastava S. Estimation of population mean using auxiliary character in presence of non-response. National Academy Science Letters. 1993 Mar 1;16:111-.

13. Khare BB, Srivastava S. Study of conventional and alternative two phase sampling ratio, product and regression estimators in presence of non–response. Proceedings-National Academy Of Sciences India Section A. 1995;65:195–204.

14. Khare BB, Sinha RR. On class of estimators for population mean using multi-auxiliary characters in the presence of non-response. Statistics in Transition. 2009;10(1):3–14.

15. Singh HP, Kumar S. A REGRESSION APPROACH TO THE ESTIMATION OF THE FINITE POPULATION MEAN IN THE PRESENCE OF NON–RESPONSE. Australian & New Zealand Journal of Statistics. 2008 Dec;50(4):395–408. doi: 10.1111/j.1467-842X.2008.00525.x

16. Ericson WA. Optimal sample design with non–response. Journal of the American Statistical Association, 1967, 62(317):63–78. doi: 10.1080/01621459.1967.10482888

17. Smouse EP. Bayesian estimation of a finite population total using auxiliary information in the presence of non–response. Journal of the American Statistical Association. 1982 Mar 1;77(377):97–102. doi: 10.1080/01621459.1982.10477771

18. Fuller WA. Simple estimators for the mean of skewed populations. Statistica Sinica. 1991 Jan 1:137–58.

19. Royall RM, Cumberland WG. The finite-population linear regression estimator and estimators of its variance—An empirical study. Journal of the American Statistical Association. 1981 Dec 1;76(376):924–930. doi: 10.1080/01621459.1981.10477742

20. Royall RM. The linear least-squares prediction approach to two-stage sampling. Journal of the American Statistical Association. 1976 Sep 1;71(355):657–664. doi: 10.1080/01621459.1976.10481542

21. Godambe VP. A unified theory of sampling from finite populations. Journal of the Royal Statistical Society: Series B (Methodological). 1955 Jul;17(2):269–78.

22. Godambe VP, Joshi VM. Admissibility and Bayes estimation in sampling finite populations. I. The Annals of Mathematical Statistics. 1965 Dec 1;36(6):1707–22. doi: 10.1214/aoms/1177699799

23. Basu D. An essay on the logical foundations of survey sampling, Part I. Foundations of Statistical Inferences, VP Godambe and DA Sprott. 1971, 203–233.

24. Sarndal CE, Thomsen I, Hoem JM, Lindley DV, Barndorff-Nielsen O, Dalenius T. Design-based and model-based inference in survey sampling [with discussion and reply]. Scandinavian Journal of Statistics. 1978 Jan 1:27–52.

25. Godambe VP. Estimation in survey sampling: robustness and optimality. Journal of the American Statistical Association. 1982 Jun 1;77(378):393–403.

26. Little RJ. Survey non–response adjustments for estimates of means. International Statistical Review/Revue Internationale de Statistique. 1986 Aug 1:139–157.

27. Bellhouse DR. Model-based estimation in finite population sampling. The American Statistician. 1987 Nov 1;41(4):260–2. doi: 10.1080/00031305.1987.10475496

28. Valliant R. Finite population sampling and inference: a prediction approach. 2000.

29. Jiang J, Lahiri P. Mixed model prediction and small area estimation. Test. 2006 Jun 1;15(1):1. doi: 10.1007/BF02595419

30. Sarndal CE, Swensson B, Wretman J. Model assisted survey sampling. Springer Science & Business Media; 2003 Oct 31.

31. Brewer K. and Gregoire TG. Introduction to survey sampling. In Handbook of Statistics. 2009, 29: 9–37. Elsevier

32. Valliant R. Model-based prediction of finite population totals. Sample Surveys: Inference and Analysis, 2009, 29B:23–31.

33. Mukhopadhyay P. Estimation of a finite population total under regression models: a review. Sankhyā: The Indian Journal of Statistics, Series B. 1993 Aug 1:141–155.

34. Rawlings JO, Pantula SG, Dickey DA. Applied regression analysis: a research tool. Springer Science & Business Media; 2001 Apr 6.

35. Draper NR and Smith H. Applied regression analysis, 2014, 326. John Wiley & Sons

36. Holt D. and Elliot D. (1991). Methods of weighting for unit non-response. The Statistician, pages 333–342. doi: 10.2307/2348286

37. Särndal CE. The calibration approach in survey theory and practice. Survey Methodology. 2007 Dec 1;33(2):99–119.

38. Ahmed S. & Shabbir J. Extreme-cum-median ranked set sampling, Brazilian Journal of Probability and Statistics, 2019, 33 (1): 24–38. doi: 10.1214/17-BJPS373

39. Chambers R, Clark R. An introduction to model-based survey sampling with applications. OUP Oxford; 2012 Jan 12.

40. Hoerl AE, Kennard RW. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics. 1970 Feb 1;12(1):55–67. doi: 10.1080/00401706.1970.10488634

41. Vinod HD, Ullah A. Recent advances in regression methods. Marcel Dekker Incorporated; 1981, 41.

42. Yeh IC, Yang KJ, Ting TM. Knowledge discovery on RFM model using Bernoulli sequence. Expert Systems with Applications. 2009 Apr 1;36(3):5866–5871. doi: 10.1016/j.eswa.2008.07.018

43. Najarian S, Arashi M, Kibria BG. A simulation study on some restricted ridge regression estimators. Communications in Statistics-Simulation and Computation. 2013 Apr 1;42(4):871–890. doi: 10.1080/03610918.2012.659953


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