Conditional Specification of Statistical Models (Springer Series in Statistics)

The Contrary-to-Fact Conditional
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Bibliographic Information

Efforts to visualize multivariate densities necessarily involve the use of cross-sections, or, equivalently, conditional densities. Title Conditional Specification of Statistical Models. Author Barry C. Arnold, B. See details. See all 2 brand new listings. Buy It Now. Add to cart. Sarabia , Hardcover. Be the first to write a review About this product. About this product Product Information Efforts to visualize multivariate densities necessarily involve the use of cross-sections, or, equivalently, conditional densities.

This book focuses on distributions that are completely specified in terms of conditional densities. They are appropriately used in any modeling situation where conditional information is completely or partially available. All statistical researchers seeking more flexible models than those provided by classical models will find conditionally specified distributions of interest.

New York, NY: Springer. Gelman, Andrew. Gelman, Andrew, and Jennifer Hill. Cambridge, England: Cambridge University Press. Gelman, Andrew, and Iain Pardoe. Goldman, Nick, and Simon Whelan. Gotelli, Nicholas J. A Primer of Ecological Statistics. Sunderland, MA: Sinauer. Greven, Sonja, and Thomas Kneib. Harrison, Xavier A. Heisterkamp, Simon H. Hinde, John.

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Introduction Other sources of help Model definition Model specification Should I treat factor xxx as fixed or random? Nested or crossed? Are they reliable? Methods for testing single parameters Tests of effects i. What other options do I have? How do I count the number of degrees of freedom for a random effect? Model summaries goodness-of-fit, decomposition of variance, etc. Should I use aov , nlme , or lme4 , or some other package? Introduction This is an informal FAQ list for the r-sig-mixed-models mailing list. Searching on StackOverflow with the [r] [mixed-models] tags , or on CrossValidated with the [mixed-model] tag may be helpful these sites also have an [lme4] tag.

In order to use these tools you should have at least a general acquaintance with classical mixed-model experimental designs but you should also, probably, read something about modern mixed model approaches. Littell et al. Other useful references include Gelman and Hill focused on Bayesian methods and Zuur et al. If you are going to use generalized linear mixed models, you should understand generalized linear models Dobson and Barnett , Faraway , and McCullagh and Nelder are standard references; the last is the canonical reference, but also the most challenging.

All of the issues that arise with regular linear or generalized-linear modeling e. Should I treat factor xxx as fixed or random? For example, from Gelman : Before discussing the technical issues, we briefly review what is meant by fixed and random effects. Relatively few mixed effect modeling packages can handle crossed random effects, i.

This definition is confusing, and I would happily accept a better one. A classic example is crossed temporal and spatial effects. If there is random variation among temporal blocks e. Also, in the case of fixed effects, crossed and nested specifications change the parameterization of the model, but not anything else e. Whether you explicitly specify a random effect as nested or not depends in part on the way the levels of the random effects are coded.

If the lower-level random effect has the same labels within each larger group e. A comparison of multiple-imputation methods for handling missing data in repeated measurements observational studies.

About this book

Conditional Specification of Statistical Models. Access this title on SpringerLink – Click here! Statistics · Springer Series in Statistics. Free Preview cover. Conditional Specification of Statistical Models. Springer Series in Statistics at an appropriate level for a graduate course in statistics, and appear to provide.

Royal Statistical Society. Schafer JL. Analysis of incomplete multivariate data. A multivariate technique for multiply imputing missing values using a sequence of regression models. Sampling Methodology. Fully conditional specification in multivariate imputation.

Poisson Regression - Modelling Count Data - Statistical Models

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Lifecourse health development: past, present and future. Matern Child Health J. Statistical issues in life course epidemiology. Am J Epidemiol. Does maternal smoking during pregnancy have a direct effect on future offspring obesity? Evidence from a prospective birth cohort study. Risk of childhood overweight after exposure to tobacco smoking in prenatal and early postnatal life.

Conditional Specification of Statistical Models

PLoS One. The association between passive and active tobacco smoke exposure and child weight status among Spanish children. Early life predictors of increased body mass index among indigenous Australian children. Thoemmes F, Mohan K. Graphical representation of missing data problems. Struct Equ Model Multidiscip J. The impact of missing data on analyses of a time-dependent exposure in a longitudinal cohort: a simulation study.

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Duplicate citations

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Tuning multiple imputation by predictive mean matching and local residual draws. Comparison of methods for imputing limited-range variables: a simulation study. Multiple imputation for missing data: fully conditional specification versus multivariate Normal imputation. Comparison of methods for imputing ordinal data using multivariate normal imputation: a case study of non-linear effects in a large cohort study. Rounding non-binary categorical variables following multivariate normal imputation: evaluation of simple methods and implications for practice.

Multilevel models with multivariate mixed response types. Statistical Modelling: An International Journal. Quartagno M, Carpenter J. Package 'jomo'. R statistical software package. Download references. All data generated and analysed during the current study are available from the corresponding author on reasonable request.

All authors were responsible for critical revision of the manuscript and have approved the final version to be published. Correspondence to Anurika Priyanjali De Silva. For the simulation study, data were completely simulated, which did not require approval from the ethics committee or consent from participants. The case study example used in this study was based on the infant cohort of LSAC which has been provided ethical clearance by the Australian Institute of Family Studies Ethics Committee.

Written informed consent was obtained from the caregiver on behalf of each of the study children, as the children were minors at the time of data collection. The signed consent forms are retained by the field agency Australian Bureau of Statistics. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Reprints and Permissions.

Search all BMC articles Search. Abstract Background Longitudinal categorical variables are sometimes restricted in terms of how individuals transition between categories over time. Methods We designed a simulation study based on the Longitudinal Study of Australian Children, where the target analysis was the association between incomplete maternal smoking and childhood obesity.

Results Overall, we observed reduced bias when applying multiple imputation methods with restrictions, and fully conditional specification with predictive mean matching performed the best. Conclusion In a similar longitudinal setting we recommend the use of fully conditional specification with predictive mean matching, with restrictions applied during the imputation stage. Open Peer Review reports. Background The problem of missing data is prominent in longitudinal studies as these studies involve gathering information from respondents at multiple waves over a long period of time [ 1 ].

Epidemiological analysis of interest Childhood obesity is a growing epidemic in most developed countries, and a common problem among Australian children [ 14 ].