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Springer Biased Sampling and Over-identified Parameters - Statistics

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Description

This comprehensive Springer resource addresses biased sampling problems, commonly known as choice-based sampling in econometrics, alongside over-identified parameter estimation challenges. The book explores applications across medicine, epidemiology, public health, social sciences and economics, providing essential coverage of case and control studies, causal inference methodologies, missing data handling, meta-analysis techniques, renewal processes, length biased sampling, capture and recapture methods, case cohort studies and exponential tilting genetic mixture models. Ideal for researchers and practitioners tackling selection bias and complex parameter estimation in real-world data.

Key Features

The book covers biased sampling and choice-based sampling problems relevant to econometric research and statistical analysis.

Comprehensive treatment of case and control studies, causal inference and missing data problems across multiple disciplines.

Addresses advanced topics including meta-analysis, renewal processes, capture and recapture problems and case cohort studies.

Explores exponential tilting and genetic mixture models for sophisticated parameter estimation in biased sampling contexts.

Part of the Springer ICSA Book Series in Statistics, providing peer-reviewed academic content for researchers in economics and public health.

Product Specifications

Colour
White
Format
hardcover
Domain
Amazon UK
Release Date
29 June 2017
Listed Since
06 April 2017

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No barcode data available

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