Author

# Adrienne W. Kemp

Other affiliations: Queen's University Belfast, University of Bradford

Bio: Adrienne W. Kemp is an academic researcher from University of St Andrews. The author has contributed to research in topics: Poisson distribution & Negative binomial distribution. The author has an hindex of 23, co-authored 72 publications receiving 6859 citations. Previous affiliations of Adrienne W. Kemp include Queen's University Belfast & University of Bradford.

##### Papers published on a yearly basis

##### Papers

More filters

•

01 Jan 1992

TL;DR: In this paper, the authors propose a family of Discrete Distributions, which includes Hypergeometric, Mixture, and Stopped-Sum Distributions (see Section 2.1).

Abstract: Preface. 1. Preliminary Information. 2. Families of Discrete Distributions. 3. Binomial Distributions. 4. Poisson Distributions. 5. Neggative Binomial Distributions. 6. Hypergeometric Distributions. 7. Logarithmic and Lagrangian Distributions. 8. Mixture Distributions. 9. Stopped-Sum Distributions. 10. Matching, Occupancy, Runs, and q-Series Distributions. 11. Parametric Regression Models and Miscellanea. Bibliography. Abbreviations. Index.

2,106 citations

••

1,269 citations

••

19 Aug 2005

811 citations

••

TL;DR: The Hermite distribution is the generalized Poisson distribution whose probability generating function (P.G.F.) is exp [a1(s - 1) + a2(s2 - 1)] and the probabilities (and factorial moments) can be conveniently expressed in terms of modified Hermite polynomials (hence the proposed name).

Abstract: SUMMARY The Hermite distribution is the generalized Poisson distribution whose probability generating function (P.G.F.) is exp [a1(s - 1) + a2(s2 - 1)]. The probabilities (and factorial moments) can be conveniently expressed in terms of modified Hermite polynomials (hence the proposed name). Writing these in confluent hypergeometric form leads to two quite different representations of any particular probability-one a finite series, the other an infinite series. The cumulants and moments are given. Necessary conditions on the parameters and their maximum-likelihood estimation are discussed. It is shown, with examples, that the Hermite distribution is a special case of the Poisson Binomial distribution (n = 2) and may be regarded as either the distribution of the sum of two correlated Poisson variables or the distribution of the sum of an ordinary Poisson variable and an (independent) Poisson 'doublet' variable (i.e. the occurrence of pairs of events is distributed as a Poisson). Lastly its use as a penultimate limiting form of distributions with P.G.F.

121 citations

##### Cited by

More filters

••

TL;DR: The general principles behind the macromolecular crystal structure refinement program REFMAC5 are described.

Abstract: This paper describes various components of the macromolecular crystallographic refinement program REFMAC5, which is distributed as part of the CCP4 suite. REFMAC5 utilizes different likelihood functions depending on the diffraction data employed (amplitudes or intensities), the presence of twinning and the availability of SAD/SIRAS experimental diffraction data. To ensure chemical and structural integrity of the refined model, REFMAC5 offers several classes of restraints and choices of model parameterization. Reliable models at resolutions at least as low as 4 A can be achieved thanks to low-resolution refinement tools such as secondary-structure restraints, restraints to known homologous structures, automatic global and local NCS restraints, `jelly-body' restraints and the use of novel long-range restraints on atomic displacement parameters (ADPs) based on the Kullback–Leibler divergence. REFMAC5 additionally offers TLS parameterization and, when high-resolution data are available, fast refinement of anisotropic ADPs. Refinement in the presence of twinning is performed in a fully automated fashion. REFMAC5 is a flexible and highly optimized refinement package that is ideally suited for refinement across the entire resolution spectrum encountered in macromolecular crystallography.

7,134 citations

•

01 Jan 2007TL;DR: In this article, the authors introduce the concept of risk in count response models and assess the performance of count models, including Poisson regression, negative binomial regression, and truncated count models.

Abstract: Preface 1. Introduction 2. The concept of risk 3. Overview of count response models 4. Methods of estimation and assessment 5. Assessment of count models 6. Poisson regression 7. Overdispersion 8. Negative binomial regression 9. Negative binomial regression: modeling 10. Alternative variance parameterizations 11. Problems with zero counts 12. Censored and truncated count models 13. Handling endogeneity and latent class models 14. Count panel models 15. Bayesian negative binomial models Appendix A. Constructing and interpreting interactions Appendix B. Data sets and Stata files References Index.

2,967 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

••

TL;DR: DAMBE (data analysis in molecular biology and evolution) is an integrated software package for converting, manipulating, statistically and graphically describing, and analyzing molecular sequence data with a user-friendly Windows 95/98/2000/NT interface.

Abstract: DAMBE (data analysis in molecular biology and evolution) is an integrated software package for converting, manipulating, statistically and graphically describing, and analyzing molecular sequence data with a user-friendly Windows 95/98/2000/NT interface. DAMBE is free and can be downloaded from http://web.hku.hk/~xxia/software/software.htm. The current version is 4.0.36.

2,259 citations

••

TL;DR: In this article, the authors developed new methods for analyzing the large sample properties of matching estimators and established a number of new results, such as the following: Matching estimators with replacement with a fixed number of matches are not N 1/2 -consistent.

Abstract: Matching estimators for average treatment effects are widely used in evaluation research despite the fact that their large sample properties have not been established in many cases. The absence of formal results in this area may be partly due to the fact that standard asymptotic expansions do not apply to matching estimators with a fixed number of matches because such estimators are highly nonsmooth functionals of the data. In this article we develop new methods for analyzing the large sample properties of matching estimators and establish a number of new results. We focus on matching with replacement with a fixed number of matches. First, we show that matching estimators are not N 1/2 -consistent in general and describe conditions under which matching estimators do attain N 1/2 -consistency. Second, we show that even in settings where matching estimators are N 1/2 -consistent, simple matching estimators with a fixed number of matches do not attain the semiparametric efficiency bound. Third, we provide a consistent estimator for the large sample variance that does not require consistent nonparametric estimation of unknown functions. Software for implementing these methods is available in Matlab, Stata, and R.

2,207 citations