Amanote Research

Amanote Research

    RegisterSign In

Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models

Journal of the American Statistical Association - United Kingdom
doi 10.1080/01621459.2013.830071
Full Text
Open PDF
Abstract

Available in full text

Categories
UncertaintyStatisticsProbability
Date

January 2, 2014

Authors
Ziyue LiuAnne R. CappolaLeslie J. CroffordWensheng Guo
Publisher

Informa UK Limited


Related search

Bivariate Random Change Point Models for Longitudinal Outcomes

Statistics in Medicine
EpidemiologyStatisticsProbability
2012English

Mixed-Effects State-Space Models for Analysis of Longitudinal Dynamic Systems

Biometrics
StatisticsGeneticsProbabilityMolecular BiologyBiochemistryApplied MathematicsMicrobiology ImmunologyBiological SciencesMedicineAgricultural
2010English

Integrating Population Dynamics Models and Distance Sampling Data: A Spatial Hierarchical State-Space Approach

Ecology
EvolutionEcologySystematicsBehavior
2016English

Distributed State Space Generation of Discrete-State Stochastic Models

INFORMS Journal on Computing
Management ScienceComputer Science ApplicationsInformation SystemsOperations ResearchSoftware
1998English

Multimedia Mapping Using Continuous State Space Models

English

Hierarchical Models

English

Recursive Image Sequence Segmentation by Hierarchical Models

English

Latent Variable Modeling in the Hierarchical Modeling Framework: Exploring Initial Status X Treatment Interactions in Longitudinal Studies

2002English

Estimating State and Parameters in State Space Models of Spike Trains

English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2025 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy