This paper identifies the need for politicians, planners and social scientist to be provided the tools to clarify and manipulate spatial distributions to predict future developments. Bayesian statistics offers a way to estimate values of a variable at locations that are not sampled. The paper tries to address a case where Tobler’s law is not applicable. They are using Marcov Chain Monte Carlo a...

The purpose of this study is estimation of daily Value at Risk (VaR) for total index of Tehran Stock Exchange using parametric, nonparametric and semi-parametric approaches. Conditional and unconditional coverage backtesting are used for evaluating the accuracy of calculated VaR and also to compare the performance of mentioned approaches. In most cases, based on backtesting statistics Results, ...

Within a Bayesian framework, comprehensive investigation of mixtures finite (MFMs), i.e., with prior on the number components, is performed. This model class has applications in model-based clustering as well for semi-parametric density estimation and requires suitable specifications inference methods to exploit its full potential. We contribute by considering generalized MFMs where hyperparame...

In this paper, the author designs an efficient method for imputing iteratively missing target values with semiparametric kernel regression imputation, known as the semi-parametric iterative imputation algorithm (SIIA). While there is little prior knowledge on the datasets, the proposed iterative imputation method, which impute each missing value several times until the algorithms converges in e...

We are taking a peek “under the hood” of constraint-based learning of graphical models such as Bayesian Networks. This mainstream approach to learning is founded on performing statistical tests of conditional independence. In all prior work however, the tests employed for categorical data are only asymptotically-correct, i.e., they converge to the exact p-value in the sample limit. In the prese...

This paper studies the identification and estimation of a basic model of technology adoption using specifically collected information on subjective beliefs and expectations to identify key model parameters. We discuss identification with both non-parametrically and parametrically specified utility as well as parametric and semi-parametric specifications for unobserved heterogeneity. We propose ...

Bayesian and frequentist methods differ in many aspects, but share some basic optimality properties. In practice, there are situations in which one of the methods is more preferred by some criteria. We consider the case of inference about a set of multiple parameters, which can be divided into two disjoint subsets. On one set, a frequentist method may be favored and on the other, the Bayesian. ...

In Bayesian paradigm of survival analysis, we can combine a nonparametric estimator and a parametric model by putting a prior distribution nonparametrically around the entire parametric family. This method can avoids the ineeciency of the nonparametric estimator due to ignoring partial information about a parametric model and at the same time avoids the pitfalls connected with an incorrectly sp...