Journal•ISSN: 0035-9254
Journal of The Royal Statistical Society Series C-applied Statistics
Wiley-Blackwell
About: Journal of The Royal Statistical Society Series C-applied Statistics is an academic journal published by Wiley-Blackwell. The journal publishes majorly in the area(s): Population & Regression analysis. It has an ISSN identifier of 0035-9254. Over the lifetime, 1860 publications have been published receiving 118246 citations. The journal is also known as: Applied statistics.
Papers published on a yearly basis
Papers
More filters
10,702 citations
TL;DR: Convergence of Probability Measures as mentioned in this paper is a well-known convergence of probability measures. But it does not consider the relationship between probability measures and the probability distribution of probabilities.
Abstract: Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.
5,689 citations
TL;DR: Generalized linear models, 2nd edn By P McCullagh and J A Nelder as mentioned in this paper, 2nd edition, New York: Manning and Hall, 1989 xx + 512 pp £30
Abstract: Generalized Linear Models, 2nd edn By P McCullagh and J A Nelder ISBN 0 412 31760 5 Chapman and Hall, London, 1989 xx + 512 pp £30
5,146 citations
TL;DR: In this paper, nonparametric techniques are introduced for the change point problem and exact and approximate results are obtained for testing the null hypothesis of no change for zero-one observations, Binomial observations, and continuous observations.
Abstract: Non‐parametric techniques are introduced for the change‐point problem. Exact and approximate results are obtained for testing the null hypothesis of no change. The methods are illustrated by the analysis of three sets of data illustrating the techniques for zero–one observations, Binomial observations and continuous observations. Some comparisons are made with methods based on cusums.
2,671 citations
TL;DR: The generalized additive model for location, scale and shape (GAMLSS) as mentioned in this paper is a general class of statistical models for a univariate response variable, which assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects.
Abstract: Summary. A general class of statistical models for a univariate response variable is presented which we call the generalized additive model for location, scale and shape (GAMLSS). The model assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects. The distribution for the response variable in the GAMLSS can be selected from a very general family of distributions including highly skew or kurtotic continuous and discrete distributions. The systematic part of the model is expanded to allow modelling not only of the mean (or location) but also of the other parameters of the distribution of y, as parametric and/or additive nonparametric (smooth) functions of explanatory variables and/or random-effects terms. Maximum (penalized) likelihood estimation is used to fit the (non)parametric models. A Newton–Raphson or Fisher scoring algorithm is used to maximize the (penalized) likelihood. The additive terms in the model are fitted by using a backfitting algorithm. Censored data are easily incorporated into the framework. Five data sets from different fields of application are analysed to emphasize the generality of the GAMLSS class of models.
2,386 citations