Joseph L Schafer
Biographic Data
| ID | 254871 |
|---|---|
| NAME | Joseph L Schafer |
| GIVEN NAMES | Joseph L |
| FAMILY NAME | Schafer |
| SIGNATURE | SCHAFER J L |
| AFFILIATIONS | Pennsylvania State University |
| VERIFIED | No |
| TOTAL WORKS | 10 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 10 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1997 |
| LATEST PUBLICATION YEAR | 2008 |
| H-INDEX | 0 |
Average causal effects from nonrandomized studies: A practical guide and simulated example.
In a well-designed experiment, random assignment of participants to treatments makes causal inference straightforward. However, if participants are not randomized (as in observational study, quasi-experiment, or nonequivalent control-group designs), group comparisons may be biased by confounders that influence both the outcome and the alleged cause. Traditional analysis of covariance, which includes confounders as predictors in a regression model…
Proc Lca: A SAS Procedure for Latent Class Analysis
Latent class analysis (LCA) is a statistical method used to identify a set of discrete, mutually exclusive latent classes of individuals based on their responses to a set of observed categorical variables. In multiple-group LCA, both the measurement part and structural part of the model can vary across groups, and measurement invariance across groups can be empirically tested. LCA with covariates extends the model to include predictors of class m…
Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data
When outcomes are missing for reasons beyond an investigator’s control, there are two different ways to adjust a parameter estimate for covariates that may be related both to the outcome and to missingness. One approach is to model the relationships between the covariates and the outcome and use those relationships to predict the missing values. Another is to model the probabilities of missingness given the covariates and incorporate them into a …
Missing data: Our view of the state of the art.
Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical a…
Discussion on “A Nonparametric Approach to Matched Pairs with Missing Data”
A comparison of inclusive and restrictive strategies in modern missing data procedures.
Two classes of modern missing data procedures, maximum likelihood (ML) and multiple imputation (MI), tend to yield similar results when implemented in comparable ways. In either approach, it is possible to include auxiliary variables solely for the purpose of improving the missing data procedure. A simulation was presented to assess the potential costs and benefits of a restrictive strategy, which makes minimal use of auxiliary variables, versus …
A Two-Part Random-Effects Model for Semicontinuous Longitudinal Data
A semicontinuous variable has a portion of responses equal to a single value (typically 0) and a continuous, often skewed, distribution among the remaining values. In cross-sectional analyses, variables of this type may be described by a pair of regression models; for example, a logistic model for the probability of nonzero response and a conditional linear model for the mean response given that it is nonzero. We extend this two-part regression a…
Multiple imputation: A primer
In recent years, multiple imputation has emerged as a convenient and flexible paradigm for analysing data with missing values. Essential features of multiple imputation are reviewed, with answers to frequently asked questions about using the method in practice.
Multiple imputation: A primer
Analysis of Incomplete Multivariate Data
The last two decades have seen enormous developments in statistical methods for incomplete data. The EM algorithm and its extensions, multiple imputation, and Markov Chain Monte Carlo provide a set of flexible and reliable tools from inference in large classes of missing-data problems. Yet, in practical terms, those developments have had surprisingly little impact on the way most data analysts handle missing values on a routine basis. Analysis of…
No prominent works on this page.
Analysis of Incomplete Multivariate Data
The last two decades have seen enormous developments in statistical methods for incomplete data. The EM algorithm and its extensions, multiple imputation, and Markov Chain Monte Carlo provide a set of flexible and reliable tools from inference in large classes of missing-data problems. Yet, in practical terms, those developments have had surprisingly little impact on the way most data analysts handle missing values on a routine basis. Analysis of…
Multiple imputation: A primer
In recent years, multiple imputation has emerged as a convenient and flexible paradigm for analysing data with missing values. Essential features of multiple imputation are reviewed, with answers to frequently asked questions about using the method in practice.
Multiple imputation: A primer
A comparison of inclusive and restrictive strategies in modern missing data procedures.
Two classes of modern missing data procedures, maximum likelihood (ML) and multiple imputation (MI), tend to yield similar results when implemented in comparable ways. In either approach, it is possible to include auxiliary variables solely for the purpose of improving the missing data procedure. A simulation was presented to assess the potential costs and benefits of a restrictive strategy, which makes minimal use of auxiliary variables, versus …
A Two-Part Random-Effects Model for Semicontinuous Longitudinal Data
A semicontinuous variable has a portion of responses equal to a single value (typically 0) and a continuous, often skewed, distribution among the remaining values. In cross-sectional analyses, variables of this type may be described by a pair of regression models; for example, a logistic model for the probability of nonzero response and a conditional linear model for the mean response given that it is nonzero. We extend this two-part regression a…
Missing data: Our view of the state of the art.
Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical a…
Discussion on “A Nonparametric Approach to Matched Pairs with Missing Data”
Proc Lca: A SAS Procedure for Latent Class Analysis
Latent class analysis (LCA) is a statistical method used to identify a set of discrete, mutually exclusive latent classes of individuals based on their responses to a set of observed categorical variables. In multiple-group LCA, both the measurement part and structural part of the model can vary across groups, and measurement invariance across groups can be empirically tested. LCA with covariates extends the model to include predictors of class m…
Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data
When outcomes are missing for reasons beyond an investigator’s control, there are two different ways to adjust a parameter estimate for covariates that may be related both to the outcome and to missingness. One approach is to model the relationships between the covariates and the outcome and use those relationships to predict the missing values. Another is to model the probabilities of missingness given the covariates and incorporate them into a …
Average causal effects from nonrandomized studies: A practical guide and simulated example.
In a well-designed experiment, random assignment of participants to treatments makes causal inference straightforward. However, if participants are not randomized (as in observational study, quasi-experiment, or nonequivalent control-group designs), group comparisons may be biased by confounders that influence both the outcome and the alleged cause. Traditional analysis of covariance, which includes confounders as predictors in a regression model…
Computer Science (9 works) · Mathematics (9 works) · Statistical Methods and Bayesian Inference (8 works) · Statistics (8 works) · Econometrics (6 works) · Missing data (6 works) · Statistical Methods and Inference (6 works) · Artificial Intelligence (4 works) · Bayesian Methods and Mixture Models (4 works) · Data mining (4 works)