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## Nonparametric Bayes Estimation

### Bayesian Theory and Methods with Applications (2011-01-01) 1: 79-121 , January 01, 2011

For a long time there were a lot of unsuccessful efforts directed toward the solution of many nonparametric problems with the help of the Bayes approach. This can be explained mainly by difficulties a researcher encounter, when he attempts to find a suitable prior distribution, determined on a sample space. Such a distribution in nonparametric problems is chosen in the form of a set of probability distributions on the given sample space. The first work in this field where some progress has been achieved belongs to Ferguson. Ferguson formulated the requirements which must be imposed on a prior distribution

## A semiparametric regression cure model for doubly censored data

### Lifetime Data Analysis (2017-09-01): 1-17 , September 01, 2017

This paper discusses regression analysis of doubly censored failure time data when there may exist a cured subgroup. By doubly censored data, we mean that the failure time of interest denotes the elapsed time between two related events and the observations on both event times can suffer censoring (Sun in The statistical analysis of interval-censored failure time data. Springer, New York, 2006). One typical example of such data is given by an acquired immune deficiency syndrome cohort study. Although many methods have been developed for their analysis (De Gruttola and Lagakos in Biometrics 45:1–12, 1989; Sun et al. in Biometrics 55:909–914, 1999; 60:637–643, 2004; Pan in Biometrics 57:1245–1250, 2001), it does not seem to exist an established method for the situation with a cured subgroup. This paper discusses this later problem and presents a sieve approximation maximum likelihood approach. In addition, the asymptotic properties of the resulting estimators are established and an extensive simulation study indicates that the method seems to work well for practical situations. An application is also provided.

## Multivariate Time Series

### Introduction to Time Series and Forecasting (2016-01-01): 227-257 , January 01, 2016

Many time series arising in practice are best considered as components of some vector- valued (multivariate) time series {*X*_{t}} having not only serial dependence within each component series {*X*_{ti}} but also interdependence between the different component series {*X*_{ti}} and {*X*_{tj}}, *i* ≠ *j*. Much of the theory of univariate time series extends in a natural way to the multivariate case; however, new problems arise.

## An Item Response Theory Model for Student Ability Evaluation Using Computer-Automated Test Results

### New Developments in Classification and Data Analysis (2005-01-01): 325-332 , January 01, 2005

The aim of this paper is to evaluate the student learning about Computer Science subjects. A questionnaire based on ordinal scored items has been submitted to the students through a computer automated system. The data collected have been analyzed by using a latent variable model for ordinal data within the Item Response Theory framework. The scores obtained from the model allow to classify the students according to the reached competence.

## Gröbner Basis Methods in Mixture Experiments and Generalisations

### Optimum Design 2000 (2001-01-01) 51: 33-44 , January 01, 2001

The theory of mixture designs has a considerable history. We address here the important issue of the analysis of an experiment having in mind the algebraic interpretation of the structural restriction Σ*x*_{i} = 1. We present an approach for rewriting models for mixture experiments, based on constructing homogeneous orthogonal polynomials using Gröbner bases. Examples are given utilising the approach.

## Einführung in SAS

### Statistik mit SAS (2002-01-01): 13-22 , January 01, 2002

### Zusammenfassung

SAS (Statistical Analysis System) — in den Jahren von 1975 bis 1980 noch eine reine Statistiksoftware — ist inzwischen zu einem umfassenden Softwaresystem zur Verwaltung, Analyse und Darstellung von Daten ausgebaut worden.

## Components of Statistics

### International Encyclopedia of Statistical Science (2011-01-01): 274-275 , January 01, 2011

## Completing the Ecological Jigsaw

### Modeling Demographic Processes In Marked Populations (2009-01-01) 3: 513-539 , January 01, 2009

A challenge for integrated population methods is to examine the extent to which different surveys that measure different demographic features for a given species are compatible. Do the different pieces of the jigsaw fit together? One convenient way of proceeding is to generate a likelihood for census data using the Kalman filter, which is then suitably combined with other likelihoods that might arise from independent studies of mortality, fecundity, and so forth. The combined likelihood may then be used for inference. Typically the underlying model for the census data is a state-space model, and capture–recapture methods of various kinds are used to construct the additional likelihoods. In this paper we provide a brief review of the approach; we present a new way to start the Kalman filter, designed specifically for ecological processes; we investigate the effect of break-down of the independence assumption; we show how the Kalman filter may be used to incorporate density-dependence, and we consider the effect of introducing heterogeneity in the state-space model.

## The Parallel Between Clinical Trials and Diagnostic Tests

### Quantitative Decisions in Drug Development (2017-01-01): 41-51 , January 01, 2017

In this chapter, we compare successive trials designed and conducted to assess the efficacy of a new drug to a series of diagnostic tests. The condition to diagnose is whether the new drug has a clinically meaningful efficacious effect. This comparison offers us the opportunity to apply properties pertaining to diagnostic tests discussed in Chap. 3 to clinical trials. Building on the results in Chap. 3, we discuss why replication is such a critically important concept in drug development and show why replication is not as easy as some might have hoped. We end the chapter by highlighting the difference between statistical power and the probability of a positive trial. This last point becomes more important as a new drug moves through the various development stages as will be illustrated in Chap. 9.