Results 21 to 30 of about 44,560 (263)

Comparison of Parametric Models: Appication to Hypertensive Patients in a Teaching Hospital, Awka

open access: yesJournal of Biostatistics and Epidemiology, 2020
Introduction: In Nigeria, hypertension is a common sickness among grownups. This research was carried out to determine the best model for predicting survival of hypertensive patients using goodness of fit criteria, Standard Error (SE), Akaike Information
Amuche Henrietta Ibenegbu   +2 more
doaj   +1 more source

Beta Inflated Regression Models on the Physical and Mental Health of Nigerian Stroke Survivors

open access: yesAnnals of Health Research, 2021
Background: Stroke is one of the major public health problems worldwide. Physical and mental health data of stroke survivors are often expressed in proportions.
Oritogun KS, Oyewole OO
doaj   +1 more source

Model selection criteria for dynamic brain PET studies

open access: yesEJNMMI Physics, 2017
Background  Several criteria exist to identify the optimal model for quantification of tracer kinetics. The purpose of this study was to evaluate the correspondence in kinetic model preference identification for brain PET studies among five model ...
Sandeep S. V. Golla   +6 more
doaj   +1 more source

A Family of Bayesian Estimators for the Two-Parametric Burr Type II Distribution

open access: yesJournal of Function Spaces, 2022
This study discusses the posterior estimation for the parameters of the Burr type II distribution (BIID). The informative and noninformative priors along with different loss functions have also been assumed for the posterior estimation. The applicability
R. Alshenawy   +4 more
doaj   +1 more source

List of correlates included in the optimal models selected using the lowest value of Bayesian Information Criteria (BIC) and Akaike Information Criteria (AIC), after fitting–gvselect- command for all outcomes (continuous outcomes).

open access: yes, 2023
List of correlates included in the optimal models selected using the lowest value of Bayesian Information Criteria (BIC) and Akaike Information Criteria (AIC), after fitting–gvselect- command for all outcomes (continuous outcomes).
Fred M. Ssewamala (8207205)   +6 more
core   +1 more source

Bayesian decision tree for the classification of the mode of motion in single-molecule trajectories. [PDF]

open access: yesPLoS ONE, 2013
Membrane proteins move in heterogeneous environments with spatially (sometimes temporally) varying friction and with biochemical interactions with various partners.
Silvan Türkcan, Jean-Baptiste Masson
doaj   +1 more source

List of correlates included in the optimal model for high stress selected using the lowest value of Bayesian Information Criteria BIC and Akaike Information Criterion AIC, after fitting–gvselect- command for binary outcomes.

open access: yes, 2023
List of correlates included in the optimal model for high stress selected using the lowest value of Bayesian Information Criteria BIC and Akaike Information Criterion AIC, after fitting–gvselect- command for binary outcomes.
Fred M. Ssewamala (8207205)   +6 more
core   +1 more source

Effect of Signal Features and Model Variables on Energy-Traced Arrival Time Picking of Acoustic Signals Used for Structural Damage Detection

open access: yesSakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi
To monitor damage developments in structures, various structural health monitoring methods based on different principles are used. The common aspect of elastic wave-based methods is to place appropriate sensors on the structure, to detect acoustic wave ...
Sena Tayfur
doaj   +1 more source

Model selection based on Akaike information criteria (AIC).

open access: yes, 2015
The model with smallest AIC value estimating the morbidity risk of the host (rainbow trout or zebra fish) within time is underlined.The degrees of freedom (df) and significance levels (P) are given for the goodness of fit compared to the next higher ...
Hanna Kinnula (801724)   +3 more
core   +1 more source

Comparative evaluation of score criteria for dynamic Bayesian Network structure learning.

open access: yesPLoS ONE
Dynamic Bayesian Networks (DBNs) are probabilistic models with a directional structure employed to model temporal processes. Three approaches to DBN structure learning are constraint-based, score-based, and hybrid.
Aslı Yaman, Mehmet Ali Cengiz
doaj   +1 more source

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