Home Books Mathematics Mathematics and Statistics T-Classes of Linear Estimators and the Thoery of Successive Sampling

T-Classes of Linear Estimators and the Thoery of Successive Sampling

Categories

T-Classes of Linear Estimators and the Thoery of Successive Sampling

G.C. Tikkiwal

  • ISBN
  • E-ISBN
  • Book Format
  • Binding
  • Language
  • Edition
  • Imprint
  • ©Year
  • Pages
  • Size (Inch)
  • Weight
Select Format USD( )
Print Book 24.00 16.00 33%Off
Individual E Book Buy Now
Institutional E Book Price available on request
Add To Cart Buy Now  Sample Chapter Read eBook  Request Demo for Ebooks  Request Complimentary Copy

Blurb

The book is concerned with the study of different classes of linear estimators in survey sampling, known as T-classes of linear estimators and the theory of successive sampling. The theory of classification of linear estimators in different classes has been developed mainly by Horvitz and Thompson, Godambe, Koop, Prabhu Ajgaonkar, Tikkiwal and the theory of successive sampling by Jessen, Yates, Paterson, Tikkiwal and others. The book presents a detailed study of all the seven T-classes along with the unified theory of unordering. It also discusses the technique of combined unordering and its applications. The chapter on the theory of successive sampling deals with the theory under less restrictive assumptions for finite population, there by making it possible to obtain the main results given in text books on survey sampling, as a special case of the these results. The theory of T-classes along with the theory of successive sampling provide more serviceable estimation procedure based on the time honoured principles of inference than the one provided by Basu, Godambe and others. The material present in this book is meant for one specialised sample survey course in semester scheme for the post graduate students of statistics. Therefore, it can be used as a text book. The book is also useful for research students and faculty engaged in research on theoretical foundations of inference from finite population.

© 2024 SCIENTIFIC PUBLISHERS | All rights reserved.