bluuebunny/crossref_metadata_2025_split huggingface dataset:
Selecting the Better Bernoulli Treatment Using a Matched Samples Design
Authors: Tamhane Ajit C. 
Published: December 2018

Summary
The problem of selecting the better Bernoulli treatment using a matched samples design is considered in the framework of the indifference-zone approach. A single-stage procedure is proposed and its properties are studied. Tables of sample sizes for implementing the proposed procedure are given. A comparison is made with an independent samples design and the associated Sobel-Huyett selection procedure.
Characterization of the Normal Distribution Among the Continuous Symmetric Spherical Class
Authors: Ali Mir M. 
Published: December 2018

Summary
When component random variables are identically and independently distributed the independence of X and S implies normality of the components. In the present note this classical result is extended to the case when the components have a joint distribution which belong to the class of spherically symmetric distributions. Furthermore, the classic result that independence of components arising from the class of spherically symmetric distributions implies normality of components is derived in an elegant manner.
Factor Analysis for Categorical Data
Authors: Bartholomew D. J. 
Published: December 2018

Summary
The method of factor analysis is widely used as an exploratory tool to reduce the dimensionality of multivariate data. The fact that the standard model is strictly applicable only when the manifest variables are scaled is a serious limitation in social science where the variables are often categorical. In this paper we aim to provide a theoretical framework within which methods for the factor analysis of categorical data can be devised and compared. Discussion is restricted to the case of ordered categories where the latent variables are continuous. It is argued that the choice of model should be made from a restricted set which includes two existing models as special cases. A new method is proposed together with a simple approximate technique of fitting for the one-factor model. The paper concludes with an evaluation of existing methods and makes some suggestions about the direction which future research should take.
Partially Balanced Change-Over Designs Based on M-Associate Class PBIB Designs
Authors: Blaisdell E. A. , Raghavarao D. 
Published: December 2018

Summary
The authors systematically investigate the class of change-over designs that are partially balanced for the estimation of the elementary direct and residual effects contrasts, and whose constructions are based on m-associate PBIB designs. In light of the complicated mathematical structure of the analysis, there is a need to introduce restrictions on the various incidence relations. By means of these assumed constraints the concept of partially balanced change-over designs based on m-associate class PBIB designs [PBCOD(m)] is defined. Four new series of these designs are constructed and illustrated.
Variance of the Sample Space-Time Autocorrelation Function
Authors: Pfeifer Phillip E. , Deutsch Stuart Jay 
Published: December 2018

Summary
An important part of the diagnostic checking of space-time autoregressive moving average (starma) models is testing the temporal independence of the residuals. In the context of the three-stage modelling procedure, such a test is based on the sample space-time autocorrelation function. This paper developes approximate variances of the sample space-time autocorrelation function when the underlying process is white noise, which are needed to test significance of the observed autocorrelations.
Transformations and the Jackknife
Authors: Cressie Noel 
Published: December 2018

SUMMARY
Whether to jackknife a parameter estimate or some function of it has in the past relied on recommendations based on folklore rather than statistical analyses. This paper puts the jackknifed estimator into the context of finding a least squares estimator from a model involving a location parameter with additive “pseudo” errors. Obtaining and interpreting confidence intervals from the partitioning of the “total” sum of squares into “residual” sum of squares, plus a sum of squares due to a hypothesized value of the parameter, requires the error variance to be constant. This in turn can equally be interpreted as requiring the variance of the original estimator to be constant. Therefore, using this criterion of jackknifing “pivotal quantities”, it is possible in principle to find a prejackknifing transformation for any general problem.
A Note on the Comparison of Certain Sampling Strategies
Authors: Padmawar V. R. 
Published: December 2018

Summary
In this note we compare strategies H 1, H 2 and H 3 under a commonly used superpopulation model, where H1 consists of a πPS scheme and the Horvitz–Thompson (HT) estimator; H 2 consists of the Midzuno–Sen scheme and the corresponding HT estimator and H 3 is the Rao–Hartley–Cochran strategy. We first show that the sufficient condition for H 1 to be superior to H 2, due to Chaudhuri (1976), can never hold. We then suggest an alternative sufficient condition which is an improvement over the condition due to Rao (1967). Further, we disprove the claim that H 3 is superior to H 2 for g &gt; 1 (Ajgaonkar and Pedgaonkar) and then give a sufficient condition for H 3 to be superior to H 2. We conclude the note with a remark on the result due to Pedgaonkar and Ajgaonkar (1978).
On Some Simple Estimates of an Exponent of Regular Variation
Authors: Hall Peter 
Published: December 2018

Summary
We present a class of simple estimates of an exponent of regular variation. Unlike those proposed recently by de Haan and Resnick (1980), ours converge at an algebraic rather than a logarithmic rate, and unlike those considered by Teugels (1980), their form does not depend on the unknown exponent.
Minimum Distance Estimation and Components of Goodness-Of-Fit Statistics
Authors: Parr William C. , Schucany William R. 
Published: December 2018

Summary
The relationship of minimum distance (MD) estimation to components of goodness-of-fit statistics is considered. M-estimation is viewed as a special case, with interesting interpretations in terms of the defining Ψ-function as related to linear combinations of the components and modified Fourier approximations to the efficient score. The essence of the competition between efficiency and robustness is the determination of the magnitudes of the coefficients of the high frequency components. Applications to the composite and simple goodness-of-fit problems are indicated.
Multivariate Calibration
Authors: Brown P. J. 
Published: December 2018

Summary
A set of q responses Y = (Y 1 ,…, Yq)T are determined by a set of p explanatory variables x = (X 1,…, Xp)T. A set of l observed vectors Y are available at a single unknown x and it is desired to draw inferences about this unknown vector X. In order to do this calibrating data is available jointly on (Y, X) where two situations are distinguished (i), x is controlled, (ii) x is random.
Using orthodox sampling theory on the one hand and Bayesian methods on the other hand point estimators and confidence regions are derived and contrasted. A procedure for selection of a subset of responses is given. Finally a comparison is made of the methods on data from (a) a random calibration experiment of wheat quality using an infrared spectrometer and (b) a controlled experiment of point finish.
Characterizations and Goodness of Fit Tests
Authors: O'Reilly Federico J. , Stephens Michael A. 
Published: December 2018

Summary
In this article a systematic approach to providing goodness of fit tests is discussed, for the composite goodness of fit problem of testing that the distribution F of a random sample comes from a parametric family F o. Characterization procedures are emphasized, and it is shown that, at least for the exponential case, invariant characterizations appear to be better than those which are not invariant. A general technique is developed for producing invariant characterizations and for the exponential case it is shown how these are related to characterizations already in the literature. Power studies are given to examine the tests based on both invariant and non-invariant characterizations.
Minimum Distance Non-Parametric Estimation of Mixture Proportions
Authors: Titterington D. M. 
Published: December 2018

SUMMARY
Hall (1981a) develops a method of estimating mixture proportions using empirical distribution functions and a distance measure. A similar approach is suggested here based on density estimates. The method is applicable to general discrete data and multivariate data as well as to univariate continuous data and to ordered categorical data. For convenient quadratic distance measures the asymptotic theory is developed and remarks are made about practical implementation. Cases treated in detail are un-smoothed and smoothed multinomial data and univariate continuous data smoothed by the kernel method.
Estimation in Contingency Tables Using Prior Information
Authors: Albert James H. , Gupta Arjun K. 
Published: December 2018

SUMMARY
Prior beliefs of association and independence in a two-way contingency table are discussed, and estimators of the cell probabilities are proposed which can incorporate this prior information. In the 2 × 2 table where the cell counts are assumed to have a multinomial distribution, estimators are developed which can incorporate prior beliefs about the correlation coefficient and the cross-product ratio. In the I × 2 table in which one set of marginal totals is fixed, estimators are developed which reflect prior information about the similarity of a set of cell probabilities. Numerical examples are given to illustrate the use of these estimators.
A General Completeness Theorem in Sampling Theory
Authors: Liu Tzen-Ping 
Published: December 2018

Summary
Generalized symmetry of designs and estimators is defined to be invariance under a subgroup of the group of label permutations. A simple sufficient condition in terms of the design is given for the generalized order statistic, of which each generalized symmetric estimator is a function, to be complete. This condition is shown also to be necessary when the range of possible values for each characteristic is sufficiently large.
A Remark on Censoring and Surrogate Response Variables
Authors: Cox D. R. 
Published: December 2018

Summary
Survival data are often subject to right censoring and to a consequent loss of information about the effect of explanatory variables. Sometimes it is possible to recover some of this lost information via a surrogate response variable measured on censored individuals and intended to predict the remaining life-time. This possibility is studied theoretically under idealized assumptions.
On Cumulative Sum Procedures and the Sprt with Applications
Authors: Khan Rasul A. 
Published: December 2018

SUMMARY
Both one-sided and two-sided cumulative sum procedures of Page (1954) are shown to be closely related to a sequential probability ratio test (SPRT). The generating functions (Laplace transforms) of cumulative sum procedures are found in terms of the generating functions of a certain SPRT. An example is discussed and some applications are given.
On the Elimination of Nuisance Parameters in the Proportional Odds Model
Authors: McCullagh Peter 
Published: December 2018

SUMMARY
The problem of eliminating nuisance parameters in the context of the proportional odds model is considered. The technique used involves a form of sequential conditioning but it is not equivalent to a conditional or partial likelihood. Applications to ordered responses in the context of a matched pairs design are considered but the method can also be used where the responses are continuous.
Two New Tests Associated with Analysis of Variance
Authors: Azzalini A. , Cox D. R. 
Published: December 2018

SUMMARY
Two new tests are described in connexion with analysis of variance. The first is for the presence of qualitative interaction between a treatment and an intrinsic factor, both with qualitatively defined levels. Such an interaction is said to be present if there are two levels of the treatment factor and two levels of the intrinsic factor such that the treatment difference has opposite signs at the two levels of the intrinsic factor. The second test is for the presence of a treatment effect which can be tested via any of a hierarchical sequence U 1, U 1 + U 2, …, U 1 + … + Ur of sums of squares. An allowance for selection is calculated.
Asymptotic Expansions for Distributions and Quantiles with Power Series Cumulants
Authors: Withers C. S. 
Published: December 2018

SUMMARY
Cornish and Fisher (1937) gave the first few terms of the formal expansions for the distribution and percentiles of an asymptotically normal random variable (r.v.) in terms of its cumulants. They also showed that these expansions can sometimes produce extremely accurate approximations. In practice the cumulants are first expanded as a power series in a known parameter n (such as the sample size or degrees of freedom), and these expansions are substituted into the Edgeworth and Cornish-Fisher expansions, which are then rearranged in powers of 1/√n and truncated. This paper gives explicit formulae for the general terms both for the Cornish-Fisher expansions and for the derived expansions.
A New Bayesian Analysis of a Random Effects Model
Authors: Ghosh Malay , Meeden Glen 
Published: December 2018

SUMMARY
For a typical random effects model in the one way analysis of variance a new Bayesian analysis is offered. An exchangeable prior distribution is defined over the parameters. It is suggested that rather than use the usual “F” test one should consider this as an estimation problem. The posterior distribution of a function of the parameters which measures the amount of variation present is found.
Bartlett Adjustments to the Likelihood Ratio Statistic and the Distribution of the Maximum Likelihood Estimator
Authors: Barndorff-Nielsen O. E. , Cox D. R. 
Published: December 2018

SUMMARY
For rather general parametric models, a simple connection is established between the Bartlett adjustment factor of the log-likelihood ratio statistic and the normalizing constant c of the formula c | ĵ |½ L̄ for the conditional distribution of a maximum likelihood estimator as applied to the full model and the model of the hypothesis tested. This leads to a relatively simple demonstration that division of the likelihood ratio statistic by a suitable constant or estimated factor improves the chi-squared approximation to its distribution. Various expressions for these quantities are discussed. In particular, for the case of a one-dimensional parameter an approximation to the constants involved is derived, which does not require integration over the sample space.
A Different Empirical Bayes Interpretation of Ridge and Stein Estimators
Authors: Oman Samuel D. 
Published: December 2018

SUMMARY
Ridge and Stein-like estimators have been motivated as being empirical Bayes estimates with respect to the prior β ~ N(0, τ2 V- 1) for the vector of regression coefficients β in the usual model y = Xβ + ϵ, where the hyperparameter τ2 is estimated from the data. V = I results in a ridge pattern of shrinkage while V = Xt X gives a Stein-like pattern. In this article an alternative formulation is considered, according to which the data represent a sample from a multivariate normal random vector (Y, X 1, …,Xp) and β gives the regression of Y on the Xi in the population. Ridge estimators are shown to arise as empirical Bayes estimators when one believes a priori that the independent variables are uncorrelated, while Stein-like estimators are shown to obtain under an indifference prior for the population dispersion matrix of the Xi.
Locally Best Invariant Tests of the Error Covariance Matrix of the Linear Regression Model
Authors: King Maxwell L. , Hillier Grant H. 
Published: December 2018

SUMMARY
This paper considers a class of hypothesis testing problems concerning the covariance matrix of the disturbances in the classical linear regression model. A test that is locally best invariant against one-sided alternative hypotheses is constructed and shown to be identical to a one-sided version of the Lagrange Multiplier test.
Tailor-Made Tests of Goodness of Fit
Authors: Hall Peter 
Published: December 2018

SUMMARY
We examine a large class of distributional tests equivalent to tests based on degenerate U-statistics. The factors which influence the performance of these tests are isolated. An analysis of the factors leads us to suggest tests with improved power properties against certain types of alternatives. One such test is based on a chi squared statistic which employs overlapping cells, rather than disjoint cells. It is shown that this test has power superior to that of the classical chi squared statistic.
The Use of Uncategorized Data to Improve the Performance of a Nonparametric Estimator of a Mixture Density
Authors: Hall Peter , Titterington D. M. 
Published: December 2018

SUMMARY
We present a very simple, utilitarian technique for combining categorized and uncategorized data to construct a nonparametric estimator of a component mixture density. The method can be used for a wide class of estimators, and provides a reduction in the variance compared with the estimator based solely on categorized data.
Analysis of Field Experiments by Least Squares Smoothing
Authors: Green Peter , Jennison Christopher , Seheult Allan 
Published: December 2018

SUMMARY
Assuming a smooth trend plus independent error model for the environmental effects in the yields of a field plot experiment, least squares smoothing methods are developed to estimate both the treatment effects and the unknown trend. Treatment estimates are closely related to those resulting from a generalized least squares analysis in which the covariance structure for the environmental effects has a particular form. However, the main emphases are on the accuracy of treatment estimates under a fixed smooth trend plus error model and the exploratory power of the basic method to isolate trend effects of unknown form.
Although the detailed development is for the one-dimensional case, generalizations of the smoothness concept and extensions to two dimensions are also discussed.
Application of the basic method is illustrated on three data sets and the results compared with other analyses.
The Effect of Positive Dependence on Chi-Squared Tests for Categorical Data
Authors: Gleser Leon J. , Moore David S. 
Published: December 2018

SUMMARY
We introduce a general definition of positive dependence for finite-state processes, and show that if successive observations are positively dependent, all tests that are asymptotically equivalent to standard Pearson chi-squared tests have asymptotic null distributions stochastically larger than those obtained under the usual independence assumptions. Ignoring positive dependence therefore leads to too frequent rejection of null hypotheses. This qualitative conclusion applies in particular to certain cases of Markov dependence, and of dependence induced by cluster sampling, that have been studied by previous authors.
Bayesian Estimation of Finite Population Parameters in Categorical Data Models Incorporating Order Restrictions
Authors: Sedransk J. , Monahan J. , Chiu H. Y. 
Published: December 2018

SUMMARY
This note describes a Bayesian method for estimation of finite population parameters in general population surveys where acceptable regression-type models are typically unavailable. A categorical data model is adopted as in Ericson (1969, Section 4). However, specifications of smoothness are incorporated into the prior distribution. These smoothness conditions are expressed as unimodal or, possibly, multi-modal order relations among the category probabilities. Emphasis is placed on posterior inference about the finite population mean. Of independent interest is the methodology for evaluating the posterior moments and probabilities using Monte Carlo integration with importance sampling.
Distinguishing Outlier Types in Time Series
Authors: Muirhead C. R. 
Published: December 2018

SUMMARY
Distinguishing an outlier in a time series arising through measurement error from one arising through a perturbation of the underlying system can be of use in data validation. In this paper a method of testing for the presence of an outlier of unknown type is proposed. Then the properties of a rule based on the likelihood ratio which attempts to distinguish the two types of outlier are examined and compared with those of the corresponding Bayes rules. An example involving data from an industrial production process is studied.
Screening in a Normal Model
Authors: Boys R. J. , Dunsmore I. R. 
Published: December 2018

SUMMARY
An individual is deemed ‘successful’ if the value of a certain measurement of interest Y lies in some given specification region. In situations in which Y is difficult to measure, the individuals may be screened using a correlated variable X which is easier to measure. The motivation behind screening is to try to increase the proportion of 'successes' by eliminating those individuals who are unlikely to be successful. Most approaches, except for very large sample situations, have incorporated the involved notions of tolerance regions. We present here a predictive approach to find the critical values of X for which the success probability for a retained future individual reaches a satisfactory level. A simple loss structure is also introduced to provide an alternative criterion for finding the critical values of the screening variable X. The methods are exemplified within the context of a bivariate normal model.
Existence of Maximum Likelihood Estimates in Regression Models for Grouped and Ungrouped Data
Authors: Silvapulle M. J. , Burridge J. 
Published: December 2018

SUMMARY
In general, concavity of the log likelihood alone does not imply that the MLE exists always. For a class of linear regression models for grouped and ungrouped data, a necessary and sufficient condition is obtained for the existence of the maximum likelihood estimator. This condition has an intuitively simple interpretation. Further, it turns out that there are similar necessary and sufficient conditions for the existence of maximum likelihood estimates for a number of other non-linear models such as Cox's Regression Model. For a given set of data, these conditions may be verified by Linear Programming Methods.
Efficient Two-Factor Balanced Designs
Authors: Suen Chung-Yi , Chakravarti I. M. 
Published: December 2018

SUMMARY
Efficient two-factor balanced designs are constructed by using balanced arrays of strength two with indices λ(x, y) = λ0 if x = y or λ1 if x ≠ y. Balanced arrays of this type are constructed from doubly transitive permutation groups and partly resolvable orthogonal arrays. Some two-factor balanced design with all the main effects unconfounded with block effects are constructed.
Maximum Likelihood Estimation for the Near(2) Model
Authors: Smith Richard L. 
Published: December 2018

SUMMARY
The NEAR(2) model is a model for nonlinear time series with exponential marginals. It formed the subject of a recent discussion paper by Lawrance and Lewis. We explore the possibility of maximum likelihood estimation with particular reference to the difficulties created by discontinuities in the likelihood function. Two exploratory techniques are proposed for resolving those difficulties. This note is an expanded version of a contribution presented orrally at the meeting.