Some challenges for statistics

The paper gives a highly personal sketch of some current trends in statistical inference. After an account of the challenges that new forms of data bring, there is a brief overview of some topics in stochastic modelling. The paper then turns to sparsity, illustrated using Bayesian wavelet analysis based on a mixture model and metabolite profiling. Modern likelihood methods including higher order approximation and composite likelihood inference are then discussed, followed by some thoughts on statistical educatio

A computer algebra package for approximate conditional inference

This paper presents a set of REDUCE procedures that make a number of existing higher-order asymptotic results available for both theoretical and practical research. Attention has been restricted to the context of exact and approximate inference for a parameter of interest conditionally either on an ancillary statistic or on a statistic partially sufficient for the nuisance parameter. In particular, the procedures apply to regression-scale models and multiparameter exponential families. Most of them support algebraic computation as well as numerical calculation for a given data set. Examples illustrate the code

A computer algebra package for approximate conditional inference

This paper presents a set of REDUCE procedures that make a number of existing higher-order asymptotic results available for both theoretical and practical research. Attention has been restricted to the context of exact and approximate inference for a parameter of interest conditionally either on an ancillary statistic or on a statistic partially suf\ufb01cient for the nuisance parameter. In particular, the procedures apply to regression-scale models and multiparameter exponential families. Most of them support algebraic computation as well as numerical calculation for a given data set. Examples illustrate the code

A computer algebra package for approximate conditional inference

none2This paper presents a set of REDUCE procedures that make a number of existing higher-order asymptotic results available for both theoretical and practical research. Attention has been restricted to the context of exact and approximate inference for a parameter of interest conditionally either on an ancillary statistic or on a statistic partially sufficient for the nuisance parameter. In particular, the procedures apply to regression-scale models and multiparameter exponential families. Most of them support algebraic computation as well as numerical calculation for a given data set. Examples illustrate the code.noneRUGGERO BELLIO; ALESSANDRA R. BRAZZALERuggero, Bellio; Brazzale, ALESSANDRA ROSALB

AMCA: Asymptotic Methods by Computer Algebra

A REDUCE v 3.36 package written by Ruggero Bellio and Alessandra R. Brazzale that implements approximate conditional inference for several parametric models. The code is described in Bellio and Brazzale (2001) "A computer algebra package for approximate conditional inference" (Statistics and Computing, 11, 17-24)

A Computer Algebra Package for Approximate Conditional Inference in Multiparameter Exponential Families

This paper presents a set of REDUCE procedures making a number of existing higher-order asymptotic results available for both theoretical and practical research. Attention has been restricted to the context of approximate conditional inference for multiparameter full rank exponential families. Most of the procedures support algebraic computation as well as numerical calculation for a given data set. Examples are given for both kinds of applications. All numerical results involve discrete data following log-linear models

A computer algebra package for approximate conditional inference in multiparameter exponential families

This paper presents a set of REDUCE procedures making a number of existing higher-order asymptotic results available for both theoretical and practical research. Attention has been restricted to the context of approximate conditional inference for multiparameter full rank exponential families. Most of the procedures support algebraic computation as well as numerical calculation for a given data set. Examples are given for both kinds of applications. All numerical results involve discrete data following log-linear models