نتایج جستجو برای: winbugs
تعداد نتایج: 249 فیلتر نتایج به سال:
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferenti...
WinBUGS code to fit the multivariate Bayesian relative risk model: model{ for( i in 1 : N ) { for( j in 1 : K ) { Y[i, j] ~ dpois(lambda[i, j]) # distribution of observations lambda[i, j] E[i, j] * theta[i, j] theta[i, j] exp(phi[ i, j]) # log parametrization } phi[i, 1:K ] ~ dmnorm(mu[ ], Omega[, ]) } for(j in 1:K){ mu[j] ~ dunif( 2,2) } Omega[1:K, 1:K ] ~ dwish(R[, ], 12) # Wishart on prec. m...
Distributed lag models are of importance when it is believed that a covariate at time t, say Xt, causes an impact in the mean value of the response variable, Yt. Moreover, it is believed that the effect of X on Y persists for a period and decays to zero as time passes by. There are in the literature many different models that deal with this kind of situation. This paper aims to review some of t...
This dissertation focuses on the stochastic modelling of outstanding liabilities in non-life insurance, using Bayesian Statistics. Credible intervals and various statistical estimates can then be derived, whereas this is not the case with the numerical methods commonly used in the industry, such as the Chain Ladder, which only give point estimates. We ignore the claim numbers and concentrate on...
Airport air traffic is one of the most important and hardest one among all the airport data forecasts. In this paper, the Markov Chain Monte Carlo (MCMC) method of applied theory of statistics has been introduced into the aviation sector, and the discussion on airport air traffic forecast has been conducted taking Shanghai airport as the application background. MCMC method is designed to solve ...
The one-way random-effect ANOVA model is presented, and two simulated datasets are analyzed. and discussed from three points of view: (1) The standard ANOVA table, F test, and method-of-moments estimates of variance components, which can lead to negative estimates. (2) Maximum likelihood estimates of variance components. (3) Bayesian probability intervals for variance components based on flat p...
Jointly modelling related longitudinal and time-to-event data can offer advantages over separate modelling. We consider three models for longitudinal/time-to-event data: 1. random slopes and intercepts/constant hazard 2. random slopes and intercepts/step function hazard 3. random intercepts, fixed slope and IOU errors/constant hazard. Methods for simulating data from these models are outlined. ...
• Initialisation of the R console • Exploratory graphical analysis • Parameter estimation with Monolix using SAEM and model evaluation in Xpose • Parameter estimation in NONMEM using FOCE and model evaluation in Xpose • Parameter estimation in WinBUGS using MCMC • Comparison of parameter estimates • Updating parameter estimates in the MDL Parameter Object using MLE values from NONMEM • Performi...
BACKGROUND Meta-regression is becoming increasingly used to model study level covariate effects. However this type of statistical analysis presents many difficulties and challenges. Here two methods for calculating confidence intervals for the magnitude of the residual between-study variance in random effects meta-regression models are developed. A further suggestion for calculating credible in...
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