Some results on the regularity in a single replicate design

A necessary and sufficient condition is given for a single replicate design to be regular with respect to a given factorial effect. This condition is applied in establishing necessary and sufficient condition for the regularity of an i order factorial effectl > 1, when a subset of factorial effects are known to be regular.     

Some properties of a scaled Burr type X distribution

Properties of a scaled Burr type X distribution are given. Closed-form expressions for the moments only exist for certain special cases, so upper and lower bounds for the first moment are given, as well as an approximation based on these bounds. Maximum likelihood estimation is considered, and the asymptotic properties of these estimators are discussed for i.i.d. samples, as well as for Types I and II censoring. Finally, an extension to a multivariate Burr type X distribution is introduced.     

Some sufficient conditions for the type 1 optimality of block designs

In this paper, we investigate the problem of determining block designs which are optimal under type 1 optimality criteria within various classes of designs having υ treatments arranged in b blocks of size k. The solutions to two optimization problems are given which are related to a general result obtained by Cheng (1978) and which are useful in this investigation. As one application of the solutions obtained, the definition of a regular graph design given in Mitchell and John (1977) is extended to that of a semi-regular graph design and some sufficient conditions are derived for the existence of a semi-regular graph design which is optimal under a given type 1 criterion. A result is also given which shows how the sufficient conditions derived can be used to establish the optimality under a specific type 1 criterion of some particular types of semi- regular graph designs having both equal and unequal numbers of replicates. Finally,some sufficient conditions are obtained for the dual of an A- or D-optimal design to be A- or D-optimal within an appropriate class of dual designs.     

Generating random variates from D-distributions via substitution sampling

Laud et al. (1993) describe a method for random variate generation from D-distributions. In this paper an alternative method using substitution sampling is given. An algorithm for the random variate generation from SD-distributions is also given.     

On some expressions for variance,covariance, skewness and L-moments

Some results concerning expressions for moments and L-moments of continuous distributions are given. These include: some decompositions of variance and covariance closely related to decompositions recently given by Yatracos; a similar expression for the population third central moment and a simple proof thereof for nonnegative random variables; an alternative proof of a general expression for L-moments due to Hosking, and some straightforward consequences for inequalities concerning L-moments. Simplicity is a key feature of all results considered in this paper.     

Asymptotically pointwise optimal and asymptotically optimal stopping times in the Bayesian inference

The paper deals with the problem of determining asymptotically pointwise optimal and asymptotically optimal stopping times in the Bayesian inference. The sufficient conditions are given for a family of stopping times to be asymptotically pointwise optimal and asymptotically optimal with respect to a continuous time process. As an example a sequential estimation of the intensity of the Poisson process is considered. Under a gamma prior distribution, an asymptotically pointwise optimal and asymptotically optimal rule is given using a LINEX loss function and the cost c per unit time.     

Inverse moments of discrete distributions

In this paper an expression for the inverse moment of order r is given for the truncated binomial and Poisson distributions. This enables one to obtain inverse moments in a finite series. Some applications and multivariate generalizations are also given. The method also enables one to obtain relations between inverse moments and factorial moments and distributions of sums of variables.     

Unbiased estimation of a nonlinear function a normal mean with application to measurement err oorf models

Let W be a normal random variable with mean μand known variance σ². Conditions on the function f(·) are given under which there exists an unbiased estimator, f(W), of f(μ) for all real μ. In particular it is shown that f(·) must be an entire function over the complex plane. Infinite series solutions for F(·) are obtained which are shown to be valid under growth conditions of the derivatives, f^k( ·), of f(·). Approximate solutions are given for the cases in which no exact solution exists. The theory is applied to nonlinear measurement-error models as a means of finding unbiased score functions when measurement error is normally distributed. Relative efficiencies comparing the proposed method to the use of conditional scores (Stefanski and Carroll, 1987) are given for the Poisson regression model with canonical link.     

Balanced Treatment incomplete block (BTIB) designs for comparing treatments with a control: minimal complete sets of generator designs for k = 3, p = 3(1)10

Bechhofer and Tamhane (1981) proposed a new class of incomplete block designs called BTIB designs for comparing p ≥ 2 test treatments with a control treatment in blocks of equal size k < p + 1. All BTIB designs for given (p,k) can be constructed by forming unions of replications of a set of elementary BTIB designs called generator designs for that (p,k). In general, there are many generator designs for given (p,k) but only a small subset (called the minimal complete set) of these suffices to obtain all admissible BTIB designs (except possibly any equivalent ones). Determination of the minimal complete set of generator designs for given (p,k) was stated as an open problem in Bechhofer and Tamhane (1981). In this paper we solve this problem for k = 3. More specifically, we give the minimal complete sets of generator designs for k = 3, p = 3(1)10; the relevant proofs are given only for the cases p = 3(1)6. Some additional combinatorial results concerning BTIB designs are also given.     

Conditional multivariatk calibration

The problem of simple linear calibration is not new and dates back to the late 1930's. In 1982 Brown presented a number of important results for the multivariate case. In this paper we extend Brown's work to cover the situation where one is interested in calibrating for an unknown q-vector X on the basis of an observed p-vector Y given that k≥l components of X are fixed in advance.

An outline of the theoretical development in the multivariate normal case will be given and the procedure illustrated with the application to previously published data.     

Reference priors for prediction

This article discusses the reference decision method for developing noninformative priors for prediction analyses. An information-theoretic criterion is advocated for choosing priors. Reference priors for prediction are defined to be priors which maximize the criterion in some asymptotic sense. These priors satisfy Jeffreys' original requirement of invariance under reparametrization. In the regular case, an explicit form of reference priors for prediction is given. Typically, it reduces to the Jeffreys prior. However, an example is given to illustrate how it produces a different prior than the ordinary noninformative priors.     

Some MV-optimal group divisible type block designs for comparing a set of test treatments with a set of standard treatments

In this paper, we consider experimental situations in which it is desired to optimally compare t-test treatments to s standard treatments using a block design in which the experimental units are arranged in b blocks of size k. A method is given for generating an MV-optimal block design for such situations and sufficient conditions are derived which can often be used to establish the MV-optimality of reinforced group divisible designs which are often obtained using the process given.     

A characterization of probability distributions similar to the exponential

A concept of the lack-of-memory property at a given time point c > 0 is introduced. It is equivalent to the concept of the almost-lack-of-memory (ALM) property of the random variables. A representation theorem is given for the cumulative distribution function of such random variables as well as for corresponding decompositions in terms of independent random variables. It is shown that a periodic failure rate for a random variable is equivalent to the ALM property. In addition some properties of the service time of an unreliable server are observed.     

A hypergraph-theoretic analysis of collapsibility and decomposability for extended log-linear models

Extended log-linear models (ELMs) are the natural generalization of log-linear models when the positivity assumption is relaxed. The hypergraph language, which is currently used to specify the syntax of ELMs, both provides an insight into key notions of the theory of ELMs such as collapsibility and decomposability, and allows to work out efficient algorithms to solve some problems of inference. This is the case for the three search problems addressed in this paper and referred to as the approximation problem, the selective-reduction problem and the synthesis problem. The approximation problem consists in finding the smallest decomposable ELM that contains a given ELM and is such that the given ELM is collapsible onto each of its generators. The selective-reduction problem consists in deleting the maximum number of generators of a given ELM in such a way that the resulting ELM is a submodel and none of certain variables of interest is missing. The synthesis problem consists in finding a minimal ELM containing the intersection of ELMs specified by given independence relations. We show that each of the three search problems above can be reduced to an equivalent search problem on hypergraphs, which can be solved in polynomial time.     

A characterization of the independence - distribution - preserving covariance structure for the multivariate maximum squared - radii statistic

Necessary and sufficient conditions on the observation covariance structure and on the set of linear transformations are given for which the distribution of the multivariate maximum squared - radii statistic for detecting a single multivariate outlier is invariant from the distribution assuming the usual independence covariance structure. Thus, we extend the work of Baksalary and Puntanen (1990), who have given necessary and sufficient conditions for an independence-distribution-preserving covariance structure for Grubbs' statistic for detecting a univariate outlier. We also extend the work of Marco, Young, and Turner (1987) and Pavur and Young (1991), who have given sufficient conditions for an independence-distribution-preserving dependency structure for the multivariate squared - radii statistic.     

On a Relation Between a Family of Distributions Attaining the Bhattacharyya Bound and That of Linear Combinations of the Distributions from an Exponential Family

Abstract

An unbiased estimation problem of a function g(θ) of a real parameter is considered. A relation between a family of distributions for which an unbiased estimator of a function g(θ) attains the general order Bhattacharyya lower bound and that of linear combinations of the distributions from an exponential family is discussed. An example on a family of distributions involving an exponential and a double exponential distributions with a scale parameter is given. An example on a normal distribution with a location parameter is also given.     

Further results on the trace of a noncentral wishart matrix

In an earlier article Mathai (1980) has given compact representations for the moments and cumulants of the trace of a noncentral Wishart matrix. He has also shown that (trA-ntr;∑)/(2ntri∑²)172. is asymptotically standard normal where A is a noncentral wishart matrix with n degrees of freedom and covariance matrix [0, In the present article explicit expressions for the exact density of the trace are given in terms of confluent hypergeometric functions and in terms of zonal polynomials for the general case and as finite sums when the sample size is odd. As a consequence of some of these representations some summation formulae for zonal polynomials are also given     

On Expected Lengths of Predictive Intervals

Abstract.  We consider the problem of comparing predictive intervals for a future observation via their expected lengths at a given confidence level. A higher order asymptotic theory is developed. This yields an explicit formula for expected length comparison and associated admissibility results. Illustrative examples are given.     

THE CALCULATION OF CONFIDENCE REGIONS FOR EIGENVECTORS

An explicit algorithm is given for constructing a confidence region on the appropriate unit sphere for an eigenvector, given a large sample. It is assumed the eigenvector corresponds to the largest eigenvalue of Exx^T, a matrix with distinct eigenvalues, and that the estimation uses n^-1S?x_ix_ix^T. While the theory is generally applicable, the writer has in mind the special case where ∥x∥= 1 i.e. directional data.     

A note on D-optimal designs for models with and without an intercept

In this paper we give a sufficient condition under which theD-optimal design for a regression model without an intercept can be obtained from theD-optimal design for the corresponding model with an intercept by simply removing the origin from its support points. Examples are given to demonstrate the applications of the results.