Results 241 to 250 of about 1,202,565 (290)

Incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease in patients with multiple sclerosis initiating disease-modifying therapies: Retrospective cohort study using a frequentist model averaging statistical framework. [PDF]

PLoS One
Brnabic AJM, Curtis SE, Johnston JA, Lo A, Zagar AJ, Lipkovich I, Kadziola Z, Murray MH, Ryan T.   +8 more
europepmc   +1 more source

Prediction of Myasthenia Gravis Worsening: A Machine Learning Algorithm Using Wearables and Patient‐Reported Measures

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Myasthenia gravis (MG) is a rare disorder characterized by fluctuating muscle weakness with potential life‐threatening crises. Timely interventions may be delayed by limited access to care and fragmented documentation. Our objective was to develop predictive algorithms for MG deterioration using multimodal telemedicine data ...
Maike Stein, Haoqi Sun, Sophie Lehnerer, Lea Gerischer, Maximilian Mönch, Christian Meisel, Andreas Meisel, Pushpa Narayanaswami   +7 more
wiley   +1 more source

Chronological and Spatial Distribution of Skeletal Muscle Fat Replacement in FHL1‐Related Myopathies

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objectives Variants in the FHL1 gene cause FHL1‐related myopathies (FHL1‐RMs), a group of neuromuscular disorders with diverse clinical presentations. This study aimed to comprehensively characterize the spatial and temporal patterns of skeletal muscle fat replacement throughout the whole body in FHL1‐RMs, to examine disease progression over ...
Rui Shimazaki, Satoru Noguchi, Hotake Takizawa, Yasushi Oya, Yuji Takahashi, Hirofumi Komaki, Hajime Arahata, Shinichiro Hayashi, Ichizo Nishino   +8 more
wiley   +1 more source

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Model averaging with averaging covariance matrix

Economics Letters, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhao, Shangwei, Zhang, Xinyu, Gao, Yichen   +2 more
openaire   +1 more source

Frequentist Model Averaging in Structural Equation Modelling

Psychometrika, 2019
Model selection from a set of candidate models plays an important role in many structural equation modelling applications. However, traditional model selection methods introduce extra randomness that is not accounted for by post-model selection inference.
Jin, Shaobo, Ankargren, Sebastian
openaire   +2 more sources

Ranking Model Averaging: Ranking Based on Model Averaging

INFORMS Journal on Computing
Ranking problems are commonly encountered in practical applications, including order priority ranking, wine quality ranking, and piston slap noise performance ranking. The responses of these ranking applications are often considered as continuous responses, and there is uncertainty on which scoring function is used to model the responses.
Ziheng Feng, Baihua He, Tianfa Xie, Xinyu Zhang, Xianpeng Zong   +4 more
openaire   +1 more source

Bayesian Model Selection and Model Averaging

Journal of Mathematical Psychology, 2000
This paper reviews the Bayesian approach to model selection and model averaging. In this review, I emphasize objective Bayesian methods based on noninformative priors. I will also discuss implementation details, approximations, and relationships to other methods. Copyright 2000 Academic Press.
openaire   +3 more sources

Bayesian model averaging in R [PDF]

Journal of Economic and Social Measurement, 2011
Bayesian model averaging has increasingly witnessed applications across an array of empirical contexts. However, the dearth of available statistical software which allows one to engage in a model averaging exercise is limited. It is common for consumers of these methods to develop their own code, which has obvious appeal.
Shahram Amini, Christopher F. Parmeter
openaire   +1 more source

Consistency of BIC Model Averaging

Statistica Sinica, 2023
Summary: BIC weighting is frequently applied to high-dimensional linear regressions when model averaging is used to address model selection uncertainty. It also plays a central role in model selection diagnostics. However, little research has been done on its consistency or weak consistency, which are crucial properties of model averaging methods.
Chen, Ze, Zhang, Jianqiang, Xu, Wangli, Yang, Yuhong   +3 more
openaire   +2 more sources

Bayesian Model Averaging

2019
Bayesian model averaging (BMA) is a statistical method to rigorously take model uncertainty into account. This chapter gives a coherent overview on the statistical foundations and methods of BMA and its usefulness for forecasting, but also for the identification of robust determinants. The focus is given on economic applications.
Mevin B. Hooten, Trevor J. Hefley
openaire   +3 more sources