We propose an estimator for the conditional density p(Y |X) that can adapt for asymmetric heavy tails which might depend on X. Such estimators have important applications in nance and insurance. We draw from Extreme Value Theory the tools to build a hybrid unimodal density having a parameter controlling the heaviness of the upper tail. This hybrid is a Gaussian whose upper tail has been replace...

Most of the existing large empirical literature on interest rate modeling focus on the in-sample performance of different models, in spite of the fact that the evolution of interest rates in the future not in the past is most relevant in many financial applications, such as pricing, hedging and risk management. In this paper, we provide probably the first comprehensive empirical study (to our k...

Automatically recognizing the e-learning activities is an important task for improving the online learning process. Probabilistic graphical models such as Hidden Markov Models and Conditional Random Fields have been successfully used in order to identify a web user activity. For such models, the sequences of observation are crucial for training and inference processes. Despite the efficiency of...

Various volatility estimators and models have been proposed in the literature to measure volatility of asset returns. In this paper, we compare empirical performance of various unconditional volatility estimators and conditional volatility models (GARCH and EGARCH) using time-series data of S&PCNX Nifty, a value-weighted index of 50 stocks traded on the National Stock Exchange (NSE), Mumbai. Th...

Science-based models often involve substantial uncertainty that must be quantified in a defendable way. Shortage of empirical data inevitably requires input from expert judgment. How this uncertainty is best elicited can be critical to a decision process, as differences in efficacy and robustness of the elicitation methods can be substantial. When performed rigorously, expert elicitation and po...

Latent Gaussian models are flexible models that are applied in several statistical applications. When posterior marginals or full conditional distributions in hierarchical Bayesian inference from these models are not available in closed form, Markov chain Monte Carlo methods are implemented. The component dependence of the latent field usually causes increase in computational time and divergenc...

In this paper, we discuss dierent predictors of times to failure of units censored in multiple stages in a progressively censored  sample from proportional hazard rate models. The maximum likelihood predictors, best unbiased predictors and conditional median predictors are considered. We also consider Bayesian point predictors for the times to failure of units. A numerical example and a Monte C...

The representation of conditional independence models by perfect sequences provides an alternative to Bayesian networks and essential graphs. The paper discusses properties of perfect sequences that are relevant with respect to different structures of conditional independence models. Boundary variables (related to terminal nodes in a directed graph representation) are used to find the number of...