For the first time, we give an asymptotic formula of order n-2, where n is the sample size, for the covariance matrix of the maximum likelihood estimators of the regression parameters in regular dispersion models. The covariance matrix formula does not involve cumulants of log-likelihood...

In this note, we prove a sharp Lp-rate of convergence of the number of upcrossings to the local time of the Brownian motion. In particular, it provides novel p-variation estimates (2p∞) for the number of upcrossings of the Brownian motion. Our result complements the fundamental work of...

Fix a function such that , where d=1, and each function is strictly increasing, right continuous with left limits. We prove the equilibrium fluctuations for exclusion processes with conductances, induced by W, in random environments, when the system starts from an equilibrium measure. The...

In this article, we extend the beta regression model proposed by Ferrari and Cribari-Neto (2004), which is generally useful in situations where the response is restricted to the standard unit interval in two different ways: we let the regression structure to be nonlinear, and we allow a...

We introduce the log-odd Weibull regression model based on the odd Weibull distribution (Cooray, 2006). We derive some mathematical properties of the log-transformed distribution. The new regression model represents a parametric family of models that includes as sub-models some widely known...

In this paper, we formulate a very flexible family of models which unifies most recent lifetime distributions. The main idea is to obtain a cumulative distribution function to transform the baseline distribution with an activation mechanism characterized by a latent threshold variable. The new...

In this paper we discuss the issue of improving score statistics for exponential censored data. We give a general formula to compute corrected score statistics in exponential regression models. The formula derived is simple enough to be used analytically in order to obtain closed-form...

In this paper we derive second- and third-order bias-corrected maximum likelihood estimates in general uniparametric models. We compare the corrected estimates and the usual maximum likelihood estimate in terms of their mean squared errors. We also obtain closed-form expressions for...

In this paper we derive general formulae for second-order biases of maximum-likelihood estimates in a class of symmetric nonlinear regression models. This class of models is commonly used for the analysis of data containing extreme or outlying observations in samples from a supposedly normal...