Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Joseph L. Schafer"'
Autor:
Maren K. Olsen, Joseph L Schafer
Publikováno v:
Multivariate behavioral research. 33(4)
Analyses of multivariate data are frequently hampered by missing values. Until recently, the only missing-data methods available to most data analysts have been relatively ad1 hoc practices such as listwise deletion. Recent dramatic advances in theor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3784fff77d6241c14a1aef41efc8a786
https://pubmed.ncbi.nlm.nih.gov/26753828
https://pubmed.ncbi.nlm.nih.gov/26753828
Publikováno v:
Computational Statistics & Data Analysis. 55:802-812
When we have data with missing values, the assumption that data are missing at random is very convenient. It is, however, sometimes questionable because some of the missing values could be strongly related to the underlying true values. We introduce
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::79ec7050e55ff76c0a786e81ac1e7ef7
https://doi.org/10.1016/j.csda.2010.07.002
https://doi.org/10.1016/j.csda.2010.07.002
Publikováno v:
Journal of the Royal Statistical Society Series A: Statistics in Society. 174:689-712
Earlier age of drinking is a well-known predictor for a variety of adverse public health consequences in the United States and worldwide. In longitudinal research on early-onset drinkers, a great deal of attention has been paid to the identification
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5415a811095c36d97edcfdbae97527c
https://doi.org/10.1111/j.1467-985x.2010.00674.x
https://doi.org/10.1111/j.1467-985x.2010.00674.x
Autor:
Ofer Harel, Joseph L. Schafer
Publikováno v:
Biometrika. 96:37-50
When an assumption of missing at random is untenable, it becomes necessary to model missing-data indicators, which carry information about the parameters of the complete-data population. Within a given application, however, researchers may believe th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0378f9763d5f59c709d6681d4249754
https://doi.org/10.1093/biomet/asn069
https://doi.org/10.1093/biomet/asn069
Autor:
Joseph Kang, Joseph L. Schafer
Publikováno v:
Psychological Methods. 13:279-313
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),
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be89467cf74bbd55b51c726bd8ad31db
https://doi.org/10.1037/a0014268
https://doi.org/10.1037/a0014268
Publikováno v:
Structural Equation Modeling: A Multidisciplinary Journal. 14:671-694
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae50d3912df5402fde0bd55a97d125b9
https://doi.org/10.1080/10705510701575602
https://doi.org/10.1080/10705510701575602
Publikováno v:
Journal of the Royal Statistical Society Series A: Statistics in Society. 169:723-743
SummaryAnalysing the use of marijuana is challenging in part because there is no widely accepted single measure of individual use. Similarly, there is no single response variable that effectively captures attitudes toward its social and moral accepta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::95ca5a960de0cac82848d80896f5aac6
https://doi.org/10.1111/j.1467-985x.2006.00419.x
https://doi.org/10.1111/j.1467-985x.2006.00419.x
Publikováno v:
Psychological Methods. 10:84-100
Latent class analysis (LCA) provides a means of identifying a mixture of subgroups in a population measured by multiple categorical indicators. Latent transition analysis (LTA) is a type of LCA that facilitates addressing research questions concernin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42267769c29d58d4e4eb5c34543ff4e3
https://doi.org/10.1037/1082-989x.10.1.84
https://doi.org/10.1037/1082-989x.10.1.84
Publikováno v:
The American Statistician. 58:152-158
Likelihood functions from finite mixture models have many unusual features. Maximum likelihood (ML) estimates may behave poorly over repeated samples, and the abnormal shape of the likelihood often makes it difficult to assess the uncertainty in para
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::715ab98a60930961a359d9f065b74049
https://doi.org/10.1198/0003130043286
https://doi.org/10.1198/0003130043286
Publikováno v:
Journal of the American Statistical Association. 98:807-817
Multiple imputation replaces an incomplete dataset with m > 1 simulated complete versions that are analyzed separately by standard methods. We present a natural extension of multiple imputation for handling the dual problems of nonresponse and respon
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::423766a1384418a4b197a9c3987f9d36
https://doi.org/10.1198/016214503000000738
https://doi.org/10.1198/016214503000000738