Using the EM algorithm for Bayesian variable selection in logistic regression models with related covariates.

about

Bayesian variable selection for multistate Markov models with interval-censored data in an ecological momentary assessment study of smoking cessation.

P2860

Q47653479-4535D1BE-78E0-47AF-9B05-8B18855B3290

P2860

Using the EM algorithm for Bayesian variable selection in logistic regression models with related covariates.

Item

http://www.wikidata.org/entity/Q55081482

description

2017 nî lūn-bûn

@nan

2017年の論文

@ja

2017年学术文章

@wuu

2017年学术文章

@zh

2017年学术文章

@zh-cn

2017年学术文章

@zh-hans

2017年学术文章

@zh-my

2017年学术文章

@zh-sg

2017年學術文章

@yue

2017年學術文章

@zh-hant

name

Using the EM algorithm for Bay ...... odels with related covariates.

@en

Using the EM algorithm for Bay ...... odels with related covariates.

@nl

type

Item

label

Using the EM algorithm for Bay ...... odels with related covariates.

@en

Using the EM algorithm for Bay ...... odels with related covariates.

@nl

prefLabel

Using the EM algorithm for Bay ...... odels with related covariates.

@en

Using the EM algorithm for Bay ...... odels with related covariates.

@nl

P1476

Using the EM algorithm for Bay ...... models with related covariates

@en

P2093

A V Wilkinson

http://www.w3.org/2001/XMLSchema#string

L Leon-Novelo

http://www.w3.org/2001/XMLSchema#string

W Chan

http://www.w3.org/2001/XMLSchema#string

P2860

Exposure to smoking imagery in the movies and experimenting with cigarettes among Mexican heritage youth.

Correlates of susceptibility to smoking among Mexican origin youth residing in Houston, Texas: a cross-sectional analysis.

Bayesian variable selection for hierarchical gene-environment and gene-gene interactions.

A LASSO FOR HIERARCHICAL INTERACTIONS.

Cigarette experimentation in Mexican origin youth: psychosocial and genetic determinants.

Learning interactions via hierarchical group-lasso regularization.

Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

Deterministic annealing EM algorithm.

The Bayesian Lasso

On the Convergence Properties of the EM Algorithm

P304

575-596

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1080/00949655.2017.1398255

http://www.w3.org/2001/XMLSchema#string

P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P50

Michael D. Swartz

Matthew D Koslovsky

P577

2017-11-08T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P698

29731525

http://www.w3.org/2001/XMLSchema#string

P921

logistic regression model

covariate

P932

5935273

http://www.w3.org/2001/XMLSchema#string

Using the EM algorithm for Bayesian variable selection in logistic regression models with related covariates.

about

P2860

P2860

Using the EM algorithm for Bayesian variable selection in logistic regression models with related covariates.

description

name

type

label

prefLabel

P1433

P1476

P2093

P2860

P304

P31

P356

P433

P478

P50

P577

P698

P921

P932

P356

P1433

P1476

P2093

P2860

P304

P31

P356

P433

P478

P50

P577

P698

P921

P932