We introduce a semi-parametric Bayesian framework for a simultaneous analysis of linear quantile regression models. A simultaneous analysis is essential to attain the true potential of the quantile regression framework, but is computationally challenging due to the associated monotonicity constraint on the quantile curves. For a univariate covariate, we present a simpler equivalent characteriza...

Extremum estimation is typically an ad hoc semi-parametric estimation procedure which is only justiied on the basis of the asymptotic properties of the estimators. For a xed nite data set, consider a large number of investigations using diierent extremum estima-tors to estimate the same parameter vector. The resulting empirical distribution of point estimates can be shown to coincide with a Bay...

Consider the semi-parametric linear regression model Y = ′X+ , where has an unknown distribution F0. The semi-parametric MLE
̃ of under this set-up is called the generalized semi-parametric MLE(GSMLE).Although the GSML estimation of the linear regression model is statistically appealing, it has never been attempted due to difficulties with obtaining the GSML estimates of and F until recent work...

We consider the classical problem of missing-mass estimation, which deals with estimating total probability unseen elements in a sample. The estimation has various applications machine learning, statistics, language processing, ecology, sensor networks, and others. naive, constrained maximum likelihood (CML) estimator is inappropriate for this since it tends to overestimate observed elements. S...

We discuss a Bayesian discovery procedure for multiple comparison problems. We show that under a coherent decision theoretic framework, a loss function combining true positive and false positive counts leads to a decision rule based on a threshold of the posterior probability of the alternative. Under a semi-parametric model for the data, we show that the Bayes rule can be approximated by the o...

Approximate Bayesian computation (ABC) methods perform inference on model-specific parameters of mechanistically motivated parametric models when evaluating likelihoods is difficult. Central to the success of ABC methods, which have been used frequently in biology, is computationally inexpensive simulation of data sets from the parametric model of interest. However, when simulating data sets fr...

BACKGROUND A significant interest in spatial epidemiology lies in identifying associated risk factors which enhances the risk of infection. Most studies, however, make no, or limited use of the spatial structure of the data, as well as possible nonlinear effects of the risk factors. METHODS We develop a Bayesian Structured Additive Regression model for cholera epidemic data. Model estimation ...

Empirical studies on farmland rental rates have predominantly concentrated on modelling conditional means using spatial autoregressive models, where a linear functional form between the response and the covariates is usually assumed. However, if it is in fact non-linear, misspecifying the functional form can adversely affect inference. While mean regression models only allow limited insights in...

basically, medical diagnosis problems are the most effective component of treatment policies. recently, significant advances have been formed in medical diagnosis fields using data mining techniques. data mining or knowledge discovery is searching large databases to discover patterns and evaluate the probability of next occurrences. in this paper, bayesian classifier is used as a non-linear dat...