A NORMAL APPROXIMATION TO THE FISHER DISTRIBUTION

A simple normal approximation is given for the joint probability density function of the polar co-ordinates (θ, ψ) of a random vector following the Fisher distribution with arbitrary mean direction (θ₀, ψ₀). The approximation leads to simple inference procedures which are particularly useful in regression models. Conditions for the adequacy of the approximation are investigated and summarized in tabular form.     

ESTIMATION OF PARAMETERS IN THE MARGINAL FISHER DISTRIBUTION1

The Fisher distribution is frequently used as a model for the probability distribution of directional data, which may be specified either in terms of unit vectors or angular co-ordinates (co-latitude and azimuth). If, in practical situations, only the co-latitudes can be observed, the available data must be regarded as a sample from the corresponding marginal distribution. This paper discusses the estimation by Maximum Likelihood (ML) and the Method of Moments of the two parameters of this marginal Fisher distribution. The moment estimators are generally simpler to compute than the ML estimators, and have high asymptotic efficiency.     

ON THE BEHRENS–FISHER PROBLEM1

In this article the Behrens-Fisher problem is reformulated in terms of a structural model of inference. For this version of the problem a solution is obtained which is valid for arbitrary absolutely continuous error distributions. These results are further discussed for the standard normal distribution and for some other special cases with not normally distributed populations.     

CONFIDENCE INTERVALS UTILIZING PRIOR INFORMATION IN THE BEHRENS–FISHER PROBLEM

Consider two independent random samples of size f + 1 , one from an N (μ₁, σ²₁) distribution and the other from an N (μ₂, σ²₂) distribution, where σ²₁/σ²₂∈ (0, ∞) . The Welch ‘approximate degrees of freedom’ (‘approximate t‐solution’) confidence interval for μ₁?μ₂ is commonly used when it cannot be guaranteed that σ²₁/σ²₂= 1 . Kabaila (2005, Comm. Statist. Theory and Methods 34 , 291–302) multiplied the half‐width of this interval by a positive constant so that the resulting interval, denoted by J₀, has minimum coverage probability 1 ?α. Now suppose that we have uncertain prior information that σ²₁/σ²₂= 1. We consider a broad class of confidence intervals for μ₁?μ₂ with minimum coverage probability 1 ?α. This class includes the interval J₀, which we use as the standard against which other members of will be judged. A confidence interval utilizes the prior information substantially better than J₀ if (expected length of J)/(expected length of J₀) is (a) substantially less than 1 (less than 0.96, say) for σ²₁/σ²₂= 1 , and (b) not too much larger than 1 for all other values of σ²₁/σ²₂ . For a given f, does there exist a confidence interval that satisfies these conditions? We focus on the question of whether condition (a) can be satisfied. For each given f, we compute a lower bound to the minimum over of (expected length of J)/(expected length of J₀) when σ²₁/σ²₂= 1 . For 1 ?α= 0.95 , this lower bound is not substantially less than 1. Thus, there does not exist any confidence interval belonging to that utilizes the prior information substantially better than J₀.     

THE ASYMPTOTIC EQUIVALENCE OF THE FISHER INFORMATION MATRICES FOR TYPE I AND TYPE II CENSORED DATA FROM LOCATION-SCALE FAMILIES

Type I and Type II censored data arise frequently in controlled laboratory studies concerning time to a particular event (e.g., death of an animal or failure of a physical device). Log-location-scale distributions (e.g., Weibull, lognormal, and loglogistic) are commonly used to model the resulting data. Maximum likelihood (ML) is generally used to obtain parameter estimates when the data are censored. The Fisher information matrix can be used to obtain large-sample approximate variances and covariances of the ML estimates or to estimate these variances and covariances from data. The derivations of the Fisher information matrix proceed differently for Type I (time censoring) and Type II (failure censoring) because the number of failures is random in Type I censoring, but length of the data collection period is random in Type II censoring. Under regularity conditions (met with the above-mentioned log-location-scale distributions), we outline the different derivations and show that the Fisher information matrices for Type I and Type II censoring are asymptotically equivalent.     

ESTIMATION OF THE CONCENTRATION PARAMETERS OF THE FISHER MATRIX DISTRIBUTION ON 50(3) AND THE BINGHAM DISTRIBUTION ON Sq, q≥ 2

Two procedures are considered for estimating the concentration parameters of the Fisher matrix distribution for rotations or orientations in three dimensions. The first is maximum likelihood. The use of a convenient 1-dimensional integral representation of the normalising constant, which greatly simplifies the computation, is suggested. The second approach exploits the equivalence of the Fisher distribution for rotations in three dimensions, and the Bingham distribution for axes in four dimensions. We describe a pseudo likelihood procedure which works for the Bingham distribution in any dimension. This alternative approach does not require numerical integration. Results on the asymptotic efficiency of the pseudo likelihood estimator relative to the maximum likelihood estimator are given, and the two estimators are compared in the analysis of a well-known vectorcardiography dataset.     

磋商·交流·分享·对话--中加统计政策会议扫描

享誉天下的苏州是一座既古老而又充满活力的城市；建设中的苏州工业园区，更是我国改革开放政策所催生的全新发展模式。五月的苏州，春风拂面，细雨绵绵。中加统计政策会议之所以选择在苏州召开，正如国家统计局局长朱之鑫所言，不但合适，而且更有其特殊的意义。因为把现代统计理论引入我国的先行者，著名统计学家——金国宝先生就诞生于苏州。无庸多言，我们的前辈也非常愿意看到一个现代高效率的国家统计体系早日在中国建立。 中加统计政策会议是根据中国国家统计局和加拿大统计局统计信息管理合作项目安排的。加方出席会议的除统计局…     

Memorial Statements by Anderson,Judge, Press,Aigner, Allenby,and Palm

This collection presents memorial statements by Theodore W. Anderson, George G. Judge, S. James Press, Dennis J. Aigner, Greg M. Allenby, and Franz C. Palm.     

Purposes, methods, philosophies

Peter Armitage has enjoyed a long and distinguished career in biostatistics, and has also given considerable thought to the way in which statistics is taught to non-mathematicians, perhaps because, he says jokingly, he always found maths a bit difficult himself. Now in retirement, he is still communicating statistical ideas in his rôle as Editor of the Encyclopedia of Biostatistics .     

Sample surveys versus experiments,controlled observations,censuses, registers,and local studies

The relative merits and drawbacks of data obtained from the following sources are compared: sample surveys, experimental projects, controlled observations, censuses, registers, and local studies. The importance of representation as the criterion for comparing the value of the data obtained by the various methods is stressed.