Marginal Likelihoods for Bayesian Poisson Regression Models

Bayesian Composite Marginal Likelihoods

This paper proposes and discusses the use of composite marginal likelihoods for Bayesian inference. This approach allows one to deal with complex statistical models in the Bayesian framework, when the full likelihood and thus the full posterior distribution is impractical to compute or even analytically unknown. The procedure is based on a suitable calibration of the composite likelihood that y...

Bayesian causality measures for multiple ARCH models using marginal likelihoods

Bayesian Inference for Zero-inflated Poisson Regression Models

Count data with excess zeros are common in social science research and can be considered as a special case of mixture structured data. We exploit the flexibility of the Bayesian analytic approach to model the mixture data structure inherent in zero-inflated count data by using the zero-inflated Poisson (ZIP) model. We discuss the importance of modelling excess-zero count data in social sciences...

Marginal likelihoods for non-Gaussian models using auxiliary mixture sampling

Several new estimators of the marginal likelihood for complex non-Gaussian models are developed. These estimators make use of the output of auxiliary mixture sampling for count data and for binary and multinomial data. One of these estimators is based on combiningChib’s estimatorwith data augmentation as in auxiliarymixture sampling,while the other estimators are importance sampling and bridge ...

a new approach to credibility premium for zero-inflated poisson models for panel data

هدف اصلی از این تحقیق به دست آوردن و مقایسه حق بیمه باورمندی در مدل های شمارشی گزارش نشده برای داده های طولی می باشد. در این تحقیق حق بیمه های پبش گویی بر اساس توابع ضرر مربع خطا و نمایی محاسبه شده و با هم مقایسه می شود. تمایل به گرفتن پاداش و جایزه یکی از دلایل مهم برای گزارش ندادن تصادفات می باشد و افراد برای استفاده از تخفیف اغلب از گزارش تصادفات با هزینه پائین خودداری می کنند، در این تحقیق ...