Pages that link to "Item:Q3079145"

The following pages link to A Bayesian Approach for Joint Modeling of Cluster Size and Subunit-Specific Outcomes (Q3079145):

Displaying 33 items.

• Modeling individual migraine severity with autoregressive ordered probit models (Q261572) (← links)
• Lymphangiogenesis and carcinoma in the uterine cervix: joint and hierarchical models for random cluster sizes and continuous outcomes (Q262366) (← links)
• Cluster analysis, model selection, and prior distributions on models (Q899044) (← links)
• A note on misspecification in joint modeling of correlated data with informative cluster sizes (Q899352) (← links)
• A latent factor model for spatial data with informative missingness (Q977647) (← links)
• A hierarchical modeling approach for risk assessment in developmental toxicity studies (Q1010535) (← links)
• Regression analysis of clustered failure time data with informative cluster size under the additive transformation models (Q1641907) (← links)
• Risk assessment for toxicity experiments with discrete and continuous outcomes: a Bayesian nonparametric approach (Q1695275) (← links)
• Mitigating bias in generalized linear mixed models: the case for Bayesian nonparametrics (Q1790317) (← links)
• The simultaneous analysis of mixed discrete and continuous outcomes using nonlinear threshold models (Q2259636) (← links)
• Regression models for analyzing clustered binary and continuous outcomes under an assumption of exchangeability (Q2382869) (← links)
• Inference on the marginal distribution of clustered data with informative cluster size (Q2442679) (← links)
• Regression analysis of clustered interval-censored failure time data with informative cluster size (Q2445636) (← links)
• Likelihood Methods for Binary Responses of Present Components in a Cluster (Q3013994) (← links)
• Tracking the Impact of Media on Voter Choice in Real Time: A Bayesian Dynamic Joint Model (Q3121169) (← links)
• Bayesian Multivariate Logistic Regression (Q3445298) (← links)
• Association Models for Clustered Data with Binary and Continuous Responses (Q3564586) (← links)
• Clustered binary data with random cluster sizes (Q4970913) (← links)
• Semiparametric regression of clustered current status data (Q5036622) (← links)
• Analysis of mixed longitudinal (<i>k</i>,<i>l</i>)-Inflated power series, ordinal and continuous responses with sensitivity analysis to non-ignorable missing mechanism (Q5082700) (← links)
• Methods for observed‐cluster inference when cluster size is informative: A review and clarifications (Q5170217) (← links)
• A joint modeling approach for multivariate survival data with random length (Q5283330) (← links)
• Analyzing Binary Outcome Data with Small Clusters: A Simulation Study (Q5418906) (← links)
• Test of Marginal Compatibility and Smoothing Methods for Exchangeable Binary Data with Unequal Cluster Sizes (Q5427420) (← links)
• Bayesian Modeling of Multiple Episode Occurrence and Severity with a Terminating Event (Q5459579) (← links)
• A Potential Outcomes Approach to Developmental Toxicity Analyses (Q5492072) (← links)
• Cluster adjusted regression for displaced subject data (<b>CARDS</b>): Marginal inference under potentially informative temporal cluster size profiles (Q5739269) (← links)
• A semiparametric joint model for cluster size and subunit‐specific interval‐censored outcomes (Q6079684) (← links)
• Semiparametric marginal methods for clustered data adjusting for informative cluster size with nonignorable zeros (Q6089916) (← links)
• Two-part models for repeatedly measured ordinal data with ``don't know'' category (Q6617405) (← links)
• Semiparametric Bayesian joint modeling of clustered binary and continuous outcomes with informative cluster size in developmental toxicity assessment (Q6626011) (← links)
• A joint Poisson state-space modelling approach to analysis of binomial series with random cluster sizes (Q6637195) (← links)
• Semiparametric regression analysis of clustered interval-censored failure time data with informative cluster size (Q6637425) (← links)