This paper uses a semi-parametric Poisson-gamma model to estimate the relationships between crash counts and various roadway characteristics, including curvature, traffic levels, speed limit and surface width. A Bayesian nonparametric estimation procedure is employed for the model’s link function, substantially reducing the risk of a mis-specified model. It is shown via simulation that little i...

In this paper, we show that the parametric simplex method is an efficient algorithm for solving various statistical learning problems that can be written as linear programs parametrized by a so-called regularization parameter. The parametric simplex method offers significant advantages over other methods: (1) it finds the complete solution path for all values of the regularization parameter by ...

In this paper, we analyze the returns of stocks comprising the German stock index DAX with respect to the α-stable distribution. We apply nonparametric estimation methods such as the Hill estimator as well as parametric estimation methods conditional on the α-stable distribution. We find for both the nonparametric and parametric estimation methods that the α-stable hypothesis cannot be rejected...

Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods, and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier det...

In this paper, we consider the non-parametric estimation of the analysis of variance (ANOVA) decomposition, which is useful for applications in sensitivity analysis (SA) and in the more general emulation framework. Pursuing the point of view of the state-dependent parameter (SDP) estimation, the non-parametric kernel estimation (including high order kernel estimator) is built for those purposes...

INTERVAL ESTIMATION OF EXCESS RISK RELATED EFFECTIVE DOSES IN TOBIT MODELS by Jia Wang ADVISOR: Professor Nan Lin December 2009 Saint Louis, Missouri In this thesis we consider interval estimation of excess risk related effective dose (ERED) in dose-response studies using tobit model. Let P (x) be the probability of response at dose level x. Considering the background probability P (0), excess ...

Networks-on-Chip (NoCs) are increasingly used in many-core architectures. ORION2.0 [18] is a widely adopted NoC power and area estimation tool that is based on circuit-level templates, which use specific logic structures to model implementation of different router components. ORION2.0 estimation models can have large errors (up to 185%) versus actual implementation, often due to a mismatch betw...

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, ...

Parameter estimation with non-ignorable missing data is a challenging problem in statistics. The fully parametric approach for joint modeling of the response model and the population model can produce results that are quite sensitive to the failure of the assumed model. We propose a more robust modeling approach by considering the model for the nonresponding part as an exponential tilting of th...