Results 61 to 70 of about 539,449 (277)
Model Selection Principles in Misspecified Models [PDF]
, 2016 Model selection is of fundamental importance to high dimensional modeling featured in many contemporary applications. Classical principles of model selection include the Kullback-Leibler divergence principle and the Bayesian principle, which lead to the ...
Akaike +35 more
core +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Poststroke fatigue (PSF) and frailty share substantial overlap in their manifestations, yet previous research has yielded conflicting results due to the use of heterogeneous frailty assessment tools. Objective To evaluate the independent impact of frailty on PSF using a unified measurement system (Tilburg Frailty Indicator, TFI ...
Chuan‐Bang Chen +6 more
wiley +1 more source
Statistical Theory and Related Fields, 2020 The power-expected-posterior prior is used in this paper for comparing nested linear models. The asymptotic behaviour of the method is investigated for different values of the power parameter of the prior.
D. Fouskakis +2 more
doaj +1 more source
Model Selection in Historical Research Using Approximate Bayesian Computation. [PDF]
PLoS ONE, 2016 FORMAL MODELS AND HISTORY:Computational models are increasingly being used to study historical dynamics. This new trend, which could be named Model-Based History, makes use of recently published datasets and innovative quantitative methods to improve our
Xavier Rubio-Campillo
doaj +1 more source
, 2009 Approximate Bayesian computation methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper we discuss and apply an approximate Bayesian computation (ABC) method based on sequential Monte Carlo (SMC) to
Andreas Ipsen +8 more
core +2 more sources
Bayesian model selection using encompassing priors
Statistica Neerlandica, 2005 This paper deals with Bayesian selection of models that can be specified using inequality constraints among the model parameters. The concept of encompassing priors is introduced, that is, a prior distribution for an unconstrained model from which the prior distributions of the constrained models can be derived.
Klugkist, I.G. +2 more
openaire +4 more sources
Hospital Readmission After Traumatic Brain Injury Hospitalization in Community‐Dwelling Older Adults
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To examine the risk of hospital readmission after an index hospitalization for TBI in older adults. Methods Using data from the Atherosclerosis Risk in Communities (ARIC) study, we used propensity score matching of individuals with an index TBI‐related hospitalization to individuals with (1) non‐TBI hospitalizations (primary analysis)
Rachel Thomas +7 more
wiley +1 more source
, 2018 When model uncertainty is handled by Bayesian model averaging (BMA) or Bayesian model selection (BMS), the posterior distribution possesses a desirable "oracle property" for parametric inference, if for large enough data it is nearly as good as the ...
Jiang, Wenxin, Li, Cheng
core +1 more source
Exact Dimensionality Selection for Bayesian PCA [PDF]
, 2019 We present a Bayesian model selection approach to estimate the intrinsic dimensionality of a high-dimensional dataset. To this end, we introduce a novel formulation of the probabilisitic principal component analysis model based on a normal-gamma prior ...
Bouveyron, Charles +2 more
core +5 more sources
Shared Genetic Effects and Antagonistic Pleiotropy Between Multiple Sclerosis and Common Cancers
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Epidemiologic studies have reported inconsistent altered cancer risk in individuals with multiple sclerosis (MS). Factors such as immune dysregulation, comorbidities, and disease‐modifying therapies may contribute to this variability.
Asli Buyukkurt +5 more
wiley +1 more source