£88.00

Bayesian Nonparametrics for Causal Inference and Missing Data (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

Business & Home Office

Price data last checked 182 day(s) ago - refreshing...

View at Amazon

We'll watch every seller, every day. One email when your price arrives.

Same price for 10 weeks. Today is much like next week.

£88 for 69 days straight · last change was Oct 2025

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 527 days • 527 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 527 days • 5 price ranges

Days at Price

Current Price

Price Analysis

Most common range: £85-90 (251 days, 47.6%)

Price range: £68 - £90

Price levels: 5 price ranges over 527 days

Description

Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data. This book emphasizes the importance of making untestable assumptions to identify estimands of interest, such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies. Unlike parametric methods, the BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification. The overall strategy is to first specify BNP models for observed data and then to specify additional uncheckable assumptions to identify estimands of interest. The book is divided into three parts. Part I develops the key concepts in causal inference and missing data and reviews relevant concepts in Bayesian inference. Part II introduces the fundamental BNP tools required to address causal inference and missing data problems. Part III shows how the BNP approach can be applied in a variety of case studies. The datasets in the case studies come from electronic health records data, survey data, cohort studies, and randomized clinical trials. Features • Thorough discussion of both BNP and its interplay with causal inference and missing data • How to use BNP and g-computation for causal inference and non-ignorable missingness • How to derive and calibrate sensitivity parameters to assess sensitivity to deviations from uncheckable causal and/or missingness assumptions • Detailed case studies illustrating the application of BNP methods to causal inference and missing data • R code and/or packages to implement BNP in causal inference and missing data problems The book is primarily aimed at researchers and graduate students from statistics and biostatistics. It will also serve as a useful practical reference for mathematically sophisticated epidemiologists and medical researchers.

Product Specifications

Format: hardcover
ASIN: 036734100X
Category: Books > Subjects > Computing & Internet > Software & Graphics > Business & Home Office
Domain: Amazon UK
Release Date: 23 August 2023
Listed Since: 22 February 2023

Barcode

No barcode data available

Bayesian Nonparametrics for Causal Inference and Missing Data (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

Same price for 10 weeks. Today is much like next week.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

SOME RECENT ADVANCES IN MATHEMATICS AND STATISTICS - PROCEEDINGS OF STATISTICS 2011 CANADA/IMST 2011-FIM XX

Introduction to Statistical Modelling and Inference

Bayesian Methods and Ethics in a Clinical Trial Design: 214 (Wiley Series in Probability and Statistics)

Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics: 858 (Studies in Computational Intelligence, 858)

Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics: 858 (Studies in Computational Intelligence, 858)

Chapman and Hall/CRC Statistical Analytics for Health Data Science

Bayesian Statistical Methods: With Applications to Machine Learning (Chapman & Hall/CRC Texts in Statistical Science)

Bayesian Survival Analysis (Springer Series in Statistics)

Exploring Medical Statistics: Biostatistics, Clinical Trials, and Epidemiology

Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation: 32 (Chapman & Hall/CRC Biostatistics)

Linear Mixed Models: A Practical Guide Using Statistical Software

Interpretación Fácil de la Bioestadística: La Conexión Entre La Evidencia Y Las Decisiones Médicas

Interval-Censored Time-to-Event Data: Methods and Applications: 52 (Chapman & Hall/CRC Biostatistics Series)

Interval-Censored Time-to-Event Data: Methods and Applications (Chapman & Hall/CRC Biostatistics Series)

Applied Meta-Analysis with R and Stata (Chapman & Hall/CRC Biostatistics Series)

Using R for Bayesian Spatial and Spatio-Temporal Health Modeling (Chapman & Hall/CRC The R Series)

Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis: 109 (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

Statistical Meta-Analysis with Applications (Wiley Series in Probability and Statistics)

Applied Missing Data Analysis, Second Edition (Methodology in the Social Sciences)

Deep-Learning-Assisted Statistical Methods with Examples in R (Chapman & Hall/CRC Data Science Series)

Quantitative Methods for Health Research: A Practical Interactive Guide to Epidemiology and Statistics

Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition: 68 (Chapman & Hall/CRC Texts in Statistical Science)

Applied Meta-Analysis with R and Stata - CRC Press Book

Statistical Questions In Evidence-Based Medicine