Results 81 to 90 of about 221,738 (314)
Time Series of Count Data : Modelling and Estimation [PDF]
This paper compares various models for time series of counts which can account for discreetness, overdispersion and serial correlation. Besides observation- and parameter-driven models based upon corresponding conditional Poisson distributions, we also ...
Jung, Robert +2 more
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JCPP Advances, EarlyView.Abstract Background Adolescence is marked by increased vulnerability to sleep disturbances and mood disorders. Understanding how day‐to‐day changes in sleep and mood are linked within the same individual is crucial for clarifying sleep's role in emerging internalizing disorders. However, the extent to which an adolescent's fluctuations in sleep predict
Konstantin Drexl +4 more
wiley +1 more source
Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models [PDF]
In this paper Efficient Importance Sampling (EIS) is used to perform a classical and Bayesian analysis of univariate and multivariate Stochastic Volatility (SV) models for financial return series. EIS provides a highly generic and very accurate procedure
Liesenfeld, Roman +1 more
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Journal of Magnetic Resonance Imaging, EarlyView.ABSTRACT Background Intravoxel incoherent motion (IVIM) analysis of diffusion‐weighted MRI (DWI) provides microvascular perfusion and diffusion information. However, parameter estimation is limited by noise sensitivity, variability across fitting methods, and lack of standardization.
Misha P. T. Kaandorp +3 more
wiley +1 more source
Efficient Markov chain Monte Carlo sampling for electrical impedance tomography
Computer Assisted Methods in Engineering and Science, 2017 This paper studies electrical impedance tomography (EIT) using Bayesian inference [1]. The resulting posterior distribution is sampled by Markov chain Monte Carlo (MCMC) [2]. This paper studies a toy model of EIT as the one presented in [3], and focuses
Erfang Ma
doaj +1 more source
Bayesian Stochastic Frontier Analysis Using WinBUGS [PDF]
Markov chain Monte Carlo (MCMC) methods have become a ubiquitous tool in Bayesian analysis. This paper implements MCMC methods for Bayesian analysis of stochastic frontier models using the WinBUGS package, a freely available software.
Jim Griffin, Mark Steel
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Embracing Creative Nonconformists and Promoting Them May Require Leaders' High Control Appraisals
Journal of Organizational Behavior, EarlyView.ABSTRACT Promoting creative employees is essential to innovation and organizational success. However, leaders do not always embrace the nonconformist nature of creative behaviors. This study examines how leaders' control appraisals—a personal orientation reflecting their belief in their own ability to control situations—influence their receptiveness ...
Xue Peng, Wen Cheng, Man‐Nok Wong
wiley +1 more source
MCMC-ODPR: Primer design optimization using Markov Chain Monte Carlo sampling
BMC Bioinformatics, 2012 Background Next generation sequencing technologies often require numerous primer designs that require good target coverage that can be financially costly.
Kitchen James L +3 more
doaj +1 more source
Inference for stochastic volatility model using time change transformations [PDF]
We address the problem of parameter estimation for diffusion driven stochastic volatility models through Markov chain Monte Carlo (MCMC). To avoid degeneracy issues we introduce an innovative reparametrisation defined through transformations that operate
Dellaportas, Petros +2 more
core +1 more source
Hidden Markov Quantile Models With Trends for Analysing Air Temperature Data
International Journal of Climatology, EarlyView.There is the question of whether climate change, expressed by time‐trends in temperature, is of a heterogeneous nature or not. Here, the time‐trend heterogeneity argument has been investigated using Hidden Markov (HM) quantile time‐trends models in temperature time series.
Georgios Tsiotas +2 more
wiley +1 more source