Missing Data Analysis in Practice

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A01=Trivellore Raghunathan
advanced missing data applications
ANOVA Method
Author_Trivellore Raghunathan
Bayesian methods
case
Category=JHB
Category=JMB
Category=PBT
Category=PS
causal inference techniques
complete
Complete Case Analysis
Cumulative Distribution Function
Data Set
disclosure limitation
Dummy Variable
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
eq_society-politics
Hot Deck
Hot Deck Method
Ignorable missing data mechanisms
imputation
Imputation Model
Imputed Values
Inverse Probability Weighting
Longitudinal Analysis
MAR Mechanism
measurement error analysis
mechanism
Missing Data
Missing Data Mechanism
Missing Values
model
multiple
Multiple Imputation
Multiple Imputation Estimate
Multiply Imputed Data Sets
non-ignorable missing data mechanisms
Nonignorable Missing Data Mechanisms
Post-stratification Weights
Post-stratified Estimator
process
propensity
quantitative research
regression
Regression Analysis
Regression Model
response
Response Propensity
Scatter Plots
Sensitivity Analysis
Sequential Regression Approach
Software Mouse
statistical inference
Synthetic Data Sets
Weighting methods

Product details

  • ISBN 9780367737665
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 18 Dec 2020
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. Drawing on his 25 years of experience researching, teaching, and consulting in quantitative areas, the author presents both frequentist and Bayesian perspectives. He describes easy-to-implement approaches, the underlying assumptions, and practical means for assessing these assumptions. Actual and simulated data sets illustrate important concepts, with the data sets and codes available online.

The book underscores the development of missing data methods and their adaptation to practical problems. It mainly focuses on the traditional missing data problem. The author also shows how to use the missing data framework in many other statistical problems, such as measurement error, finite population inference, disclosure limitation, combing information from multiple data sources, and causal inference.

Trivellore Raghunathan is the director of the Survey Research Center in the Institute for Social Research and professor of biostatistics in the School of Public Health at the University of Michigan. He has published numerous papers in a range of statistical and public health journals. His research interests include applied regression analysis, linear models, design of experiments, sample survey methods, and Bayesian inference.

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