Results 91 to 100 of about 66,786 (305)
Credit risk clustering in a business group: Which matters more, systematic or idiosyncratic risk?
Cogent Economics & Finance, 2019 Understanding how defaults correlate across firms is a persistent concern in risk management. In this paper, we apply covariate-dependent copula models to assess the dynamic nature of credit risk dependence, which we define as “credit risk clustering ...
Feng Li, Zhuojing He
doaj +1 more source
DSGE Model Forecasting: Rational Expectations Versus Adaptive Learning
Journal of Forecasting, EarlyView.ABSTRACT This paper compares within‐sample and out‐of‐sample fit of a DSGE model with rational expectations to a model with adaptive learning. The Galí, Smets, and Wouters model is the chosen laboratory using quarterly real‐time euro area data vintages, covering 2001Q1–2019Q4.
Anders Warne
wiley +1 more source
Bayesian parameter inference by Markov chain Monte Carlo with hybrid fitness measures: theory and test in apoptosis signal transduction network. [PDF]
PLoS ONE, 2013 When model parameters in systems biology are not available from experiments, they need to be inferred so that the resulting simulation reproduces the experimentally known phenomena. For the purpose, Bayesian statistics with Markov chain Monte Carlo (MCMC)
Yohei Murakami, Shoji Takada
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Parallel hierarchical sampling : a general-purpose class of multiple-chains MCMC algorithms [PDF]
, 2009 This paper introduces the Parallel Hierarchical Sampler (PHS), a class of Markov chain Monte Carlo algorithms using several interacting chains having the same target distribution but different mixing properties. Unlike any single-chain MCMC algorithm,
Mira, Antonietta +3 more
core
Industry Portfolio Volatility Connections and Industry Portfolio Returns
International Journal of Finance &Economics, EarlyView.ABSTRACT This paper tracks dynamic connections that form among daily US industry portfolio return volatilities using a Bayesian time‐varying parameter VAR model. Market participants often focus on sectors to filter vast amounts of information, and this focus results in cross‐industry return predictability. We characterise connections that form over the
Michael Ellington +2 more
wiley +1 more source
Why Bayesian Ideas Should Be Introduced in the Statistics Curricula and How to Do So
Journal of Statistics Education, 2020 While computing has become an important part of the statistics field, course offerings are still influenced by a legacy of mathematically centric thinking.
Andrew Hoegh
doaj +1 more source
International Journal of Gynecology &Obstetrics, EarlyView.Abstract Objective This study identifies factors associated with not proceeding to clitoral reconstructive surgery among women with female genital mutilation (FGM) enrolled in a specialized surgical pathway. Methods A retrospective cohort study was conducted at a multidisciplinary referral center in Montreuil, France, between January 2021 and December ...
Félicia Joinau‐Zoulovits +3 more
wiley +1 more source
To MCMC or not to MCMC: Evaluating non-MCMC methods for Bayesian penalized regression
Markov Chain Monte Carlo (MCMC) sampling is computationally expensive, especially for complex models. Alternative methods make simplifying assumptions about the posterior to reduce computational burden, but their impact on predictive performance remains unclear.
van Leeuwen, Florian D., van Erp, Sara
openaire +2 more sources
Rare vasculitis types and obstetric and neonatal outcomes – A population‐based study
International Journal of Gynecology &Obstetrics, EarlyView.Abstract Objective Vasculitis is an infrequent pathology among reproductive‐aged women. While data exists regarding pregnancy outcomes in the more common vasculitis subtypes, data is limited regarding these outcomes in rare vasculitis subtypes. We aimed to compare pregnancy and perinatal outcomes between women who suffered from rare types of vasculitis
Uri Amikam +4 more
wiley +1 more source
Model Probit Spasial pada Faktor-Faktor yang Mempengaruhi Klasifikasi IPM di Pulau Jawa
Cauchy: Jurnal Matematika Murni dan Aplikasi, 2013 Human pembagunan Index (HDI) is a composite index that includes three basic dimensions of human development is considered to reflect the status of the population's basic abilities of health, educational attainment, and purchasing power.
Feni Ira Puspita +2 more
doaj +1 more source