Large sample inference in finite population problems/ Anurag Dey
Material type:
TextPublication details: Kolkata: Indian Statistical Institute, 2024Description: xxvi, 262 pagesSubject(s): DDC classification: - 23 SA.081 An636
- Guided by Prof. Probal Chaudhuri
| Item type | Current library | Call number | Status | Notes | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|
| THESIS | ISI Library, Kolkata | SA.081 An636 (Browse shelf(Opens below)) | Available | E-Thesis | TH599 |
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| SA.076 ISI.C.77 Question paper for computer's certificate examinations | SA.08 C496 Survey sampling/ | SA.08 T575 Sampling algorithms/ | SA.081 An636 Large sample inference in finite population problems/ | SA.081 C496 Randomized response techniques: certain thought-provoking aspects/ | SA.081 C663 Sampling techniques/ | SA.081 C663 Sampling techniques/ |
Thesis (Ph.D.)- Indian statistical Institute, 2024
Includes bibliography
A comparison of estimators of mean and its functions in finite populations -- Estimators of the mean of infinite dimensional data in finite populations -- Quantile processes and their applications in finite populations -- Regression analysis and related estimators in finite populations
Guided by Prof. Probal Chaudhuri
The main objectives of this thesis is to compare various estimators of finite population parameters under different sampling designs (with no non-response) and superpopulation models, and to identify asymptotically
efficient estimators among them. Another objective of this thesis is to understand the role of
auxiliary information in the implementation of different sampling designs and in the construction
of different estimators.
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