Sampling and Estimation from Finite Populations (eBook)

Yves Tille (Autor)

eBook Download: EPUB

2020
John Wiley & Sons (Verlag)
978-1-119-07127-3 (ISBN)

Infos

A much-needed reference on survey sampling and its applications that presents the latest advances in the field

Seeking to show that sampling theory is a living discipline with a very broad scope, this book examines the modern development of the theory of survey sampling and the foundations of survey sampling. It offers readers a critical approach to the subject and discusses putting theory into practice. It also explores the treatment of non-sampling errors featuring a range of topics from the problems of coverage to the treatment of non-response. In addition, the book includes real examples, applications, and a large set of exercises with solutions.

Sampling and Estimation from Finite Populations begins with a look at the history of survey sampling. It then offers chapters on: population, sample, and estimation; simple and systematic designs; stratification; sampling with unequal probabilities; balanced sampling; cluster and two-stage sampling; and other topics on sampling, such as spatial sampling, coordination in repeated surveys, and multiple survey frames. The book also includes sections on: post-stratification and calibration on marginal totals; calibration estimation; estimation of complex parameters; variance estimation by linearization; and much more.

• Provides an up-to-date review of the theory of sampling
• Discusses the foundation of inference in survey sampling, in particular, the model-based and design-based frameworks
• Reviews the problems of application of the theory into practice
• Also deals with the treatment of non sampling errors

Sampling and Estimation from Finite Populations is an excellent book for methodologists and researchers in survey agencies and advanced undergraduate and graduate students in social science, statistics, and survey courses.

YVES TILLÉ, PhD, is a Professor at the University of Neuchâtel (Université de Neuchâtel) in Neuchâtel, Switzerland.

Rezensionen

"A task for the current, and future, generation is the research and development of methods for integrating data from multiple sources by explicitly addressing the different measurement errors. Those who read this book and address its challenges will be well placed to deal with the research opportunities ahead--both foreseen and yet to be identified."
--Carl M. O'Brien, Lowestoft Laboratory, International Statistical Review (2020) doi:10.1111/insr.12420

Textauszug

Table of Notations

cardinal (number of elements in a set)

much less than

complement of in

function is the derivative of

factorial:

number of ways to choose units from units

interval

is approximately equal to

is proportional to

follows a specific probability distribution (for a random value)

equals 1 if is true and 0 otherwise

number of times unit  is in the sample

vector of

population regression coefficients

vector of population regression coefficients

regression coefficients for model

vector of regression coefficients of model

vector of estimated regression coefficients

vector of estimated regression coefficients of the model

cube whose vertices are samples

covariance between random variables and

estimated covariance between random variables and

population coefficient of variation

estimated coefficient of variation

expansion estimator survey weights

mathematical expectation under the sampling design of estimator

mathematical expectation under the model of estimator

mathematical expectation under the nonresponse mechanism of estimator

mathematical expectation under the imputation mechanism of estimator

mean square error

sampling fraction

pseudo‐distance derivative for calibration

adjustment factor after calibration called ‐weight

pseudo‐distance for calibration

strata or post‐strata index

confidence interval with confidence level

ou	indicates a statistical unit, or

intersection of the cube and constraint space for the cube method

number of clusters or primary units in the sample of clusters or primary units

number of clusters or primary units in the population

Sample size (without replacement)

number of secondary units sampled in primary unit

size of the sample in if the size is random

population size

Sample size in stratum or post‐stratum

number of units in stratum or post‐stratum

number of secondary units in primary unit

population totals when is a contingency table

set of natural numbers

set of positive natural numbers with zero

probability of selecting sample

probability of sampling unit  for sampling with replacement

or	proportion of units belonging to domain

probability that event occurs

probability that event occurs, given occurred

subspace of constraints for the cube method

response indicator

set of real numbers

set of positive real numbers with zero

set of strictly positive real numbers

Sample or subset of the population,

Sample variance of variable

Sample variance of in stratum or post‐stratum

covariance between variables and in the sample

random sample such that

variance of variance in the population

covariance between variables and in the population

random sample selected in stratum or post‐stratum

population variance of in the stratum or post‐stratum

vector is the transpose of vector

finite population of size

stratum or post‐stratum , where

linearized variable

Horvitz–Thompson estimator of the variance of estimator

Sen–Yates–Grundy estimator of the variance of estimator

variance of estimator  under the survey design

variance of estimator  under the model

variance of estimator  under the nonresponse mechanism

variance of estimator  under the imputation mechanism

variance estimator of estimator

or	weight associated with individual in the sample after calibration

auxiliary variable

auxiliary variable value of unit

vector in of the values taken by the auxiliary variables on

total value of the auxiliary variable over all the units of

expansion estimator of

mean value of the auxiliary variables over all the units of

expansion estimator of

variable of interest

value of the variable of interest for unit

imputed value of for  (treating nonresponse)

total value of the variable of interest over all the units of

total value of the variable of interest over all the units in stratum or post‐stratum

total of in primary unit or cluster

expansion estimator of

mean value of the variable of interest over all the units of

mean value of the variable of interest over all units of stratum or post‐stratum

estimator of the mean value of the variable of interest over all units of stratum or post‐stratum

expansion estimator of

best unbiased linear estimator under the model of total

calibrated estimator of total

difference estimator of total

estimator of total  in stratum or post‐stratum

Hájek estimator of

Hansen–Hurwitz estimator of

estimator used when missing values are imputed

expansion estimator of the total in an optimal stratified design

post‐stratified estimator of the total

expansion estimator of the total in a stratified design with proportional allocation

regression estimator of total

multiple regression estimator of total

optimal regression estimator of total

Rao–Blackwellized estimator of

ratio estimator of the total

expansion estimator of the total in a stratified design

...

Produkt-Details

• Erscheint lt. Verlag: 26.2.2020
• Reihe/Serie: Wiley Series in Survey Methodology
• Reihe/Serie: Wiley Series in Survey Methodology
• Reihe/Serie: Wiley Series in Survey Methodology
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Schlagworte: Adaptive sampling • Data • Data Analysis • estimation • Finite Population • finite population</p> • <p>sampling • Methoden der Daten- u. Stichprobenerhebung • population sampling • sampling statistics • Social Science • Statistics • Statistik • Survey • survey methodology • Survey Research Methods & Sampling • Surveys • Survey Sampling • Time Series
• ISBN-10: 1-119-07127-5 / 1119071275
• ISBN-13: 978-1-119-07127-3 / 9781119071273