We show that the value of a zero-sum Bayesian game is a Lipschitz continuous function of the players’common prior belief, with respect to the total variation metric on beliefs. This is unlike the case of general Bayesian games, where lower semi-continuity of Bayesian equilibrium (BE) payo¤s rests on the "almost uniform" convergence of conditional beliefs. We also show upper semi-continuity (USC...

In this paper, the author designs an efficient method for imputing iteratively missing target values with semi-parametric 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 ...

We evaluate event studies of single firms using Bayesian methodologies. Event studies determine the effect of a public event on firms’ stock returns. Securities litigation and antitrust investigations frequently use single-firm event studies to respectively determine loss causation\damages and anticompetitive behavior. The standard approach for inference assumes normally distributed returns; ho...

In bio-statistical applications non-parametric and semi-parametric methods have been preferred over parametric ones for assessing the prognostic role of clinical/biological variables: in fact parametric distributional models for times to event are usually restrictive. In particular Proportional Hazard Models are widely adopted due to their simplicity and flexibility. In PH models the effect of ...

b a c k g r o u n d & aim: the aim of the current study was to investigate the advantages of bayesian method in comparison to traditional methods to detect best antioxidant in freezing of human male gametes. methods  &  materials:   semen  samples  were  obtained  from  40  men  whose  sperm  had  normal criteria. a part of each sample was separated without antioxidant  as fresh and the remaini...

We introduce the underlying concepts which give rise to some of commonly used machine learning methods, excluding deep-learning machines and neural networks. point their advantages, limitations potential use in various areas photonics. The main methods covered include parametric non-parametric regression classification techniques, kernel-based support vector machines, decision trees, probabilis...

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...

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We discuss a parametric minimax problem which arises from the theoretical foundation of certain decomposition methods in global optimization. Specifically, conditions for lower semi-continuity of the saddle value of a quasiconvex-quasiconcave function depending on a parameter are developed and a general parametric minimax theorem is proved.