পদ্ধতির তুলনা করুন

	জরিপ ওয়েটিং এবং ক্যালিব্রেশন	ক্ষুদ্র এলাকা প্রাক্কলন (ফে-হেরিয়ট মডেল)	স্তরবিন্যস্ত নমুনায়ন (Stratified Sampling)
ক্ষেত্র	জরিপ পদ্ধতি	জরিপ পদ্ধতি	জরিপ পদ্ধতি
পরিবার	Process / pipeline	Regression model	Process / pipeline
উদ্ভবের বছর	2010	1979	1977
প্রবর্তক	Sharon Lohr	Robert Fay & Roger Herriot	William G. Cochran
ধরন	Estimation adjustment procedure	Model-based survey estimator	Probability-based survey sampling design
মৌলিক উৎস	Lohr, S. L. (2010). Sampling: Design and Analysis (2nd ed.). Brooks/Cole. ISBN: 978-0-495-10527-5	Fay, R. E., & Herriot, R. A. (1979). Estimates of income for small places: An application of James-Stein procedures to census data. Journal of the American Statistical Association, 74(366), 269–277.	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7
অপর নাম	Survey Calibration, Post-Stratification Weighting, Raking Adjustment, Ağırlıklandırma (Anket)	SAE, Model-Based Small Area Estimation, Area-Level Model, Küçük Alan Tahmini	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
সম্পর্কিত	3	2	2
সারসংক্ষেপ	Survey weighting is a statistical procedure that assigns a numeric weight to each sampled unit so that the weighted sample reproduces known population totals. Rooted in classical sampling theory and systematically synthesized by Sharon Lohr (2010), the approach corrects for unequal selection probabilities, unit nonresponse, and coverage gaps, producing estimates that are more representative of the target population than raw sample means or totals would be.	Small Area Estimation (SAE) refers to statistical techniques that produce reliable estimates for subpopulations — geographical regions, demographic groups, or administrative units — where direct survey samples are too sparse to yield acceptable precision. The Fay-Herriot model, introduced by Robert Fay and Roger Herriot in 1979, is the canonical area-level SAE model. It supplements weak direct survey estimates with auxiliary covariate information through an empirical Bayes or BLUP framework, substantially reducing mean squared error for small domains.	Stratified sampling is a probability sampling design in which the target population is partitioned into non-overlapping, exhaustive subgroups called strata, and independent probability samples are drawn within each stratum. Formalized by William G. Cochran in Sampling Techniques (1977), the method exploits known population structure to reduce variance and guarantee representativeness of all major subgroups, making it a cornerstone of large-scale survey research and official statistics.