Download Missing data analysis in practice by Trivellore Raghunathan PDF

By Trivellore Raghunathan

Missing facts research in Practice offers functional tools for interpreting lacking facts in addition to the heuristic reasoning for realizing the theoretical underpinnings. Drawing on his 25 years of expertise learning, educating, and consulting in quantitative parts, the writer provides either frequentist and Bayesian views. He describes easy-to-implement ways, the underlying assumptions, and useful capability for assessing those assumptions. real and simulated info units illustrate very important recommendations, with the information units and codes on hand online.

The e-book underscores the advance of lacking facts tools and their variation to functional difficulties. It generally makes a speciality of the conventional lacking info challenge. the writer additionally exhibits the way to use the lacking facts framework in lots of different statistical difficulties, resembling size errors, finite inhabitants inference, disclosure quandary, combing info from a number of info resources, and causal inference.

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Missing data analysis in practice

Lacking info research in perform offers useful equipment for studying lacking info besides the heuristic reasoning for realizing the theoretical underpinnings. Drawing on his 25 years of expertise studying, instructing, and consulting in quantitative parts, the writer offers either frequentist and Bayesian views.

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A Bayesian accepts that there is a true value θo but, more importantly, the Bayesian view is that since θo is not known then there is a priori uncertainty about its value, and that should be expressed in a form of a prior distribution for θ, with the density π(θ) defined on the parameter space. This prior distribution could be constructed from pilot data, data from similar populations or the subject matter knowledge. The statistical model accepted by both, frequentists and Bayesians, specifies f (x|θ) and, therefore, the product f (x|θ)π(θ) is the joint distribution 1 of (x, θ).

6, g = 1, 2 and 4 race margin totals, N++r , r = 1, 2, 3, 4. More recently, the idea of post-stratification has been extended to develop what are called calibration weights. Suppose that population totals T1 , T2 , . . , Tp for p variables X1 , X2 , . . , Xp are known. Suppose that these variables are also measured in a survey with n respondents. The calibration weights wi , i = 1, 2, . . , n attached to the respondents should satisfy the constraints, i wi xji = Tj for j = 1, 2, . . , p where xji is the value of the variable Xj measured on subject i.

The importance of these step cannot be understated. Here are some techniques that can be used to check the balance of the covariates, conditional on the estimated propensity scores: 1. Suppose that a covariate, say, X1 is a continuous variable. Regress X1 on p (or L) and obtain the residual e1 . Compare the distributions of e1 for the respondents and nonrespondents. If they overlap and look similar then the propensity score balances the distribution of this covariate between the two groups (respondents and nonrespondents).

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