Results 1 to 10 of about 7,325 (196)
Semiparametric Bayesian networks
Information Sciences, 2022 We introduce semiparametric Bayesian networks that combine parametric and nonparametric conditional probability distributions. Their aim is to incorporate the advantages of both components: the bounded complexity of parametric models and the flexibility of nonparametric ones.
David Atienza +2 more
exaly +4 more sources
Semiparametric Duration Models [PDF]
Journal of Business and Economic Statistics, 2004 In this article we consider semiparametric duration models and efficient estimation of the parameters in a non-iid environment. In contrast to classical time series models where innovations are assumed to be iid we show that in, for example, the often-used autoregressive conditional duration (ACD) model, the assumption of independent innovations is too
Feike C Drost, Bas J M Werker
exaly +7 more sources
Navigating challenges in pediatric trial conduct: integrating bayesian sequential design with semiparametric elicitation for handling primary and secondary endpoints [PDF]
BMC Medical Research MethodologyBackground This study presents a Bayesian Adaptive Semiparametric approach designed to address the challenges of pediatric randomized controlled trials (RCTs).
Danila Azzolina +6 more
doaj +2 more sources
Evaluation of goodness of fit of semiparametric and parametric models in analysis of factors associated with length of stay in neonatal intensive care unit [PDF]
Clinical and Experimental Pediatrics, 2020 Background Length of stay is a significant indicator of care effectiveness and hospital performance. Owing to the limited number of healthcare centers and facilities, it is important to optimize length of stay and associated factors.
Fatemeh Kheiry +6 more
doaj +1 more source
Characterizing heterogeneity in Alzheimer’s disease progression: a semiparametric model [PDF]
Scientific ReportsThe progression of Alzheimer’s disease (AD), a leading cause of dementia worldwide, is known for its variability and complexity, challenging the conventional methods of monitoring and predicting disease trajectories.
Fatih Gelir +8 more
doaj +2 more sources
Investigation of Parametric, Non-Parametric and Semiparametric Methods in Regression Analysis
Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2022 Regression analysis is known as statistical methods applied to model and analyze the relationship between variables. Regression method can be examined as parametric, non-parametric and semiparametric regression methods.The parametric regression method ...
Esra Yavuz, Mustafa Şahin
doaj +1 more source
pexm: A JAGS Module for Applications Involving the Piecewise Exponential Distribution
Journal of Statistical Software, 2021 In this study, we present a new module built for users interested in a programming language similar to BUGS to fit a Bayesian model based on the piecewise exponential (PE) distribution.
Vinícius D. Mayrink +2 more
doaj +1 more source
Semiparametric Vector MEM [PDF]
SSRN Electronic Journal, 2008 SUMMARYFinancial time series are often non‐negative‐valued (volumes, trades, durations, realized volatility, daily range) and exhibit clustering. When joint dynamics is of interest, the vector multiplicative error model (vMEM; the element‐by‐element product of a vector of conditionally autoregressive scale factors and a multivariate i.i.d.
CIPOLLINI, FABRIZIO +2 more
openaire +3 more sources
Mathematics, 2023 Multivariate “Bayesian” regression via a shared component model has gained popularity in recent years, particularly in modeling and mapping the risks associated with multiple diseases.
I. Gede Nyoman Mindra Jaya +5 more
doaj +1 more source
Applied Sciences, 2023 In the present work, we analyze the spatiotemporal dynamics of the kinetic wind energy with and without allowance for the kinetic energy of outliers. We first separated the contributions of the mean kinetic energy and the kinetic energy of the outliers ...
Valerii Anan’evich Simakhin +3 more
doaj +1 more source