Top-tier customers -that is, those 20% of customers that typically bring in 80% of all profits- are extremely valuable to companies. In the many instances ...

Using random effects in the modeling of homogeneous Poisson processes (HPP) has proved effective (Cook and Lawless (2007) and Gongjun et al. (2015)). We (Kh...

Les inférences sur la prédominance d'une maladie ou d'une condition donnée sont déduites parfois des résultats de tests de diagnostic continus. Par exemple,...

Dans la recherche biomédicale, il y a un intérêt croissant pour l'utilisation de modèles hiérarchiques de régression de Poisson. Nous proposons d'utiliser de...

Wavelets have been successfully used for nonparametric function estimation, but a major challenge in density and hazard estimation is that the function must ...

Our main interest is the prediction of future events for recurrent event processes. We intend to build a Bayesian model, based on sound philosophical princip...

Although sample size calculations for testing a parameter in the Poisson regression model have been previously done, very little attention has been given to ...

In this study, we compare various computational approaches to Bayesian small area estimation of proportions in logistic regression models. The basic idea con...

A challenge in many applications of non-parametric curve estimation is that the function must satisfy some (lower and/or upper) variable order constraints (f...

One of the most important aspects of profiling health care providers or services is constructing a model that is flexible enough to allow for random variatio...

Here we study hierarchical Bayesian estimation of a monotone hazard rate for both complete and randomly right censored data. We propose two methods of comp...

We develop a nonparametric Bayesian functional estimation method, using monotone wavelet approximation, for hazard estimation from randomly right censored d...

In this paper, we are concerned with the statistical methodology of epidemiological surveillance; that is, the ongoing procedure of analyzing and interpreti...

The Cox proportional hazards model has become the model of choice to use in analyzing the effects of covariates on survival data. However, this assumption ha...

In this paper, we study the quasi-likelihood estimator of the drift parameter <img src="/cgi-bin/mimetex.cgi?\theta"> in the Ornstein-Uhlenbeck diffusion pr...

The authors study the application of the bootstrap to a class of estimators which converge at a nonstandard rate to a nonstandard asymptotic distribution. T...

This paper discusses a Bayesian functional estimation method, based on Fourier series, for the estimation of the hazard rate from randomly right-censored dat...

In this study, we present a unified Bayesian approach to small area estimation of mean parameters in generalized linear models. The basic idea consists of in...

There is increased interest in rating types of hospitals or geographical regions containing hospitals on the basis of their performance in the provision of ...

The problem of estimating a binomial proportion constrained to lie in an interval of the form [<i>a,b</i>] "not equal to" [0,1] is considered. The minimax ...

A data set on categories of congenital heart malformations for sibling pairs (with different malformations) of Fraser and Hunter (1975) is analyzed exactly ...

The spatially inhomogeneous smoothness of nonparametric methods is often modelled by Besov and Triebel-type smoothness constraints. For such problems, Donoh...

Inspired by previous results on asymptotic minimax estimation for a ball of increasing radius in R^<i>n</i>, we study the analogous problem for do...

The goal in many data analyses is to produce brief summaries which convey the key conclusions. This will be particularly important in survival analysis in m...

Since Hinkley's original work on exact inference for a change in a sequence of random variables, many authors have proposed different methods based either o...

The search for optimal non-parametric estimates of the cumulative distribution and hazard functions under order constraints inspired at least two earlier cl...

One important challenge in nonparametric density and regression-function estimation is spatially inhomogeneous smoothness. This is often modelled by Besov-t...

We present a method for the integral representation of local and global diffeomorphisms between subsets of <img src="G9942-1.gif" align=bottom> and <img src...

The spatially inhomogeneous smoothness of the non-parametric density or regression-function to be estimated by non-parametric methods is often modelled by B...

Non-parametric estimation of smooth functions belonging to Sobolev classes is closely related to the problem of estimating the (infinite dimensional) mean o...

The minimax global asymptotic rate of convergence for the estimation of a hazard function in the presence of random right censoring is obtained using the li...

Here we propose a general class of generalized linear models to describe time series of counts <i>Y</i>₁, ... ,<i>Y_n</i>. Following Ze...

In the modeling of biological phenomena, in living organisms whether the measurements are of blood pressure, enzyme levels, biomechanical movements or heart...

One of the main advantages of the discrete wavelet representation is the near-optimal estimation of signals corrupted with noise. After the seminal work of ...

In many medical experiments data are collected across time, over a number of similar trials or a number of experimental units. As is the case of neural spik...

In this paper we derive a law of the logarithm establishing the exact rate of uniform convergence for nearest neighbor kernel estimators of the density and ...

The utility of Bayes and empirical Bayes techniques is illustrated on an important problem often encountered in small area estimation. We propose various Ba...

Estimation of the mean of a multivariate normal distribution is considered. The components of the mean vector are assumed to be intra-block exchangeable; t...

The use of nonparametric regression techniques for binary regression is a promising alternative to parametric methods. As in other nonparametric smoothing ...

There has been much recent statistical research in the area of inference under constraints. Here we consider the problem of bounded parameter estimation, in...

Given a two-way contingency table in which the rows and columns both define ordinal variables, the parameters are estimated by maximizing the likelihood fun...