bluuebunny/crossref_metadata_2025_split huggingface dataset:
Regression and Ordered Categorical Variables
Authors: Anderson J. A. 
Published: December 2018

SUMMARY
A general approach to regression modelling for ordered categorical response variables, y, is given, which is equally applicable to ordered and unordered y. The regressor variables xT = (x 1 ,…,xp) may be continuous or categorical. The method is based on the logistic family which contains a hierarchy of regression models, ranging from ordered to unordered models. Ordered properties of the former, the stereotype model, are established. The choice between models is made empirically on the basis of model fit. This is particularly important for assessed, ordered categorical response variables, where it is not obvious a priori whether or not the ordering is relevant to the regression relationship. Model simplification is investigated in terms of whether or not the response categories are distinguishable with respect to x. The models are fitted iteratively using the method of maximum likelihood. Examples are given.
Symmetric Sequential Analysis: The Efficiencies of Sports Scoring Systems (With Particular Reference to Those of Tennis)
Authors: Miles Roger E. 
Published: December 2018

SUMMARY
Bernoulli-type models are adopted for the sequence of contested points between two players (or teams). In bipoints, there are two types of point – “A serving” and “B serving” – necessary for a realistic model of top (class) men's tennis. Unipoints is the special case of bipoints in which the probabilities that A (and hence B) wins each type of point are the same–a reasonable model for top men's squash rackets. Sports scoring systems based upon such play (“uniformats” and “biformats”) may be regarded as sequential statistical tests concerning the identity of the “better player”. Provided they satisfy certain fairness criteria, these tests are symmetric, with equal probabilities of type I and II error, and a unique efficiency p; then formally 1 – p represents the average fraction of play being virtually wasted through the use of a suboptimal scoring system.
Largely because in top men's tennis the proportions of service points won by both players are so high, the efficiency of traditional tennis scoring in such play is unduly low. Whilst the introduction of tiebreaker games has further reduced efficiency, it is shown how a simple entropy-increasing modification of the traditional scoring system serves to increase efficiency.
Monte Carlo Methods of Inference for Implicit Statistical Models
Authors: Diggle Peter J. , Gratton Richard J. 
Published: December 2018

SUMMARY
A prescribed statistical model is a parametric specification of the distribution of a random vector, whilst an implicit statistical model is one defined at a more fundamental level in terms of a generating stochastic mechanism. This paper develops methods of inference which can be used for implicit statistical models whose distribution theory is intractable. The kernel method of probability density estimation is advocated for estimating a log-likelihood from simulations of such a model. The development and testing of an algorithm for maximizing this estimated log-likelihood function is described. An illustrative example involving a stochastic model for quantal response assays is given. Possible applications of the maximization algorithm to ad hoc methods of parameter estimation are noted briefly, and illustrated by an example involving a model for the spatial pattern of displaced amacrine cells in the retina of a rabbit.
Recursive Parameter Estimation Using Incomplete Data
Authors: Titterington D. M. 
Published: December 2018

SUMMARY
Stochastic approximation procedures are considered for the estimation of parameters using incomplete data. One procedure is stated and illustrated which often leads to asymptotically efficient estimators. Others are developed which, although possibly not optimal in the above sense, will be very much easier to apply. This will be particularly advantageous when quick recursive estimates are required. Examples are given and a link is made between one of the sub-optimal methods and the EM algorithm.
A Test for Comparing Estimators Under the Generalized Mean Square Error Criterion
Authors: Price J. Michael 
Published: December 2018

SUMMARY
This paper presents an asymptotic test for comparing alternative estimators of regression coefficients in terms of the generalized mean square error criterion. The paper also reports the outcome of a Monte Carlo experiment which was conducted to determine the small sample properties of the test statistic. These results indicate that the asymptotic test may be applied successfully even when the sample size is small.
Outlier Models and Prior Distributions in Bayesian Linear Regression
Authors: West Mike 
Published: December 2018

SUMMARY
Bayesian inference in regression models is considered using heavy-tailed error distributions to accommodate outliers. The particular class of distributions that can be constructed as scale mixtures of normal distributions are examined and use is made of them as both error models and prior distributions in Bayesian linear modelling, including simple regression and more complex hierarchical models with structured priors depending on unknown hyperprior parameters.
On the Analysis of Heterogeneity Among Populations
Authors: Gilula Zvi 
Published: December 2018

SUMMARY
A set of populations, each of which is characterized by a distribution, is said to be “overall homogeneous” if the underlying distributions are identical. Assuming that the underlying distributions are normal or multinomial, this paper provides a theory of establishing test statistics for testing hypotheses of homogeneity within subsets of populations and of homogeneity between subsets of populations. The desired test statistics are derived by a decomposition of the test statistic for the hypothesis of overall homogeneity into a sum of mutually statistically independent components.
Changeover Designs and Factorial Structure
Authors: Fletcher D. J. , John J. A. 
Published: December 2018

SUMMARY
Existing changeover designs given in the literature are not necessarily suitable when the treatments come from a factorial experiment. The concept of factorial structure in changeover designs is defined and shown to be a desirable requirement for these types of designs. Different classes of designs are then examined in the light of this concept. In particular, it is shown that all generalized cyclic (GC/n) changeover designs have factorial structure, thereby providing a flexible class of useful designs.
Efficiency Bounds for Partially Balanced Change-Over Designs Based on M-Associate Class PBIB Designs
Authors: Raghavarao D. , Blaisdell E. A. 
Published: December 2018

SUMMARY
In an earlier paper the authors introduced PBCOD(m) designs, which are 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 class PBIB designs. In this paper we determine the maximum efficiencies for the estimated direct and residual elementary treatment contrasts of PBCOD(m), and the A-optimality (Kiefer, 1959) of these designs is explored.
A General Class of Distributions on the Simplex
Authors: Aitchison J. 
Published: December 2018

SUMMARY
The Dirichlet class of distributions on the simplex is overstructured for practical work because of its many strong independence properties. A persistent problem of distribution theory has thus been to extend this class to include members free of these independence properties. An alternative approach has been the recent introduction of the logistic-normal class which contains members with and without the independence properties. Unfortunately, this class is separate from the Dirichlet class and so cannot sustain discussion of some strong forms of independence. These difficulties are overcome in this paper by the introduction of a more general class which includes as special cases the Dirichlet and logistic-normal classes. The role of this new class in relation to the analysis of compositional data, in particular the investigation of independence hypotheses, is discussed and illustrated.
Modelling and Residual Analysis of Nonlinear Autoregressive Time Series in Exponential Variables
Authors: Lawrance A. J. , Lewis P. A. W. 
Published: December 2018

SUMMARY
An approach to modelling and residual analysis of nonlinear autoregressive time series in exponential variables is presented; the approach is illustrated by an analysis of a long series of wind velocity data which has first been detrended and then transformed into a stationary series with an exponential marginal distribution. The stationary series is modelled with a newly developed type of second order autoregressive process with random coefficients, called the NEAR(2) model; it has a second order autoregressive correlation structure but is nonlinear because its coefficients are random. The exponential distributional assumptions involved in this model highlight a very broad four parameter structure which combines five exponential random variables into a sixth exponential random variable; other applications of this structure are briefly considered. Dependency in the NEAR(2) process not accounted for by standard autocorrelations is explored by developing a residual analysis for time series having autoregressive correlation structure; this involves defining linear uncorrelated residuals which are dependent, and then assessing this higher order dependence by standard time series computations. The application of this residual analysis to the wind velocity data illustrates both the utility and difficulty of nonlinear time series modelling.
On Palm's Model with a Large Number of Machines and Repairmen
Authors: Salia R. N. , Shashiashvili M. A. 
Published: December 2018

SUMMARY
It is established in this note that the stationary probabilities of Palm's model for a large number of machines and repairmen (and the relation between them) can be approximated by a probability density, which is the “pasting together” of two normal densities with different variances.
Further, this result is used to find o(n ½)-optimal number of repairmen in the problem of minimization of the expected costs of production.
In the last part of the paper, in connection with the investigation of the asymptotics of the operative efficiency, some results by Naor (1957) are discussed.
An Exact Chi-Squared Decomposition Theorem for Inverse Gaussian Variates
Authors: Letac G. , Seshadri V. , Whitmore G. A. 
Published: December 2018

SUMMARY
It is proved that the scaled deviance of an inverse Gaussian sample of size n can be expressed as a sum of n – 1 independent chi-square variates. Applications to prediction intervals and squared recursive-residuals are illustrated.
On Auto-Repeat Facilities and Telephone Network Performance
Authors: Kelly F. P. 
Published: December 2018

SUMMARY
New developments in telecommunications technology are likely to lead to substantial increases in repeat-attempt rates and this may well adversely affect the performance of the telephone network. One aspect of this complex problem is considered in this paper, where we explore how the characteristics of a multi-server system are affected by the proportion of demands that are prepared to wait for service.
Assessment of Local Influence
Authors: Cook R. Dennis 
Published: December 2018

SUMMARY
Statistical models usually involve some degree of approximation and therefore are nearly always wrong. Because of this inexactness, an assessment of the influence of minor perturbations of the model is important. We discuss a method for carrying out such an assessment. The method is not restricted to linear regression models, and it seems to provide a relatively simple, unified approach for handling a variety of problems.
The Statistical Behaviour of Some Least Squares Estimators of the Centre and Radius of a Circle
Authors: Berman Mark , Culpin David 
Published: December 2018

SUMMARY
This paper examines the statistical behaviour of some least squares estimators of the centre and radius of a circle. Two error models are used. The asymptotic consistency of the estimators is investigated. Where asymptotic consistency is established, asymptotic covariance matrices are obtained. A small sample simulation study gives results showing the same pattern as those of the asymptotic theory.
A Note on Bayes Factors for Log-Linear Contingency Table Models with Vague Prior Information
Authors: Raftery A. E. 
Published: December 2018

SUMMARY
The approximate Bayes factor, B 01, for log-linear contingency table models proposed by Spiegelhalter and Smith (1982) is indeterminate if any of the cell frequencies is zero. It is noted that use of a standard Jeffreys prior overcomes this difficulty. It is pointed out that – 2 log B 01 is approximately equivalent to Schwarz's (1978) model selection criterion in large samples.
A Note on the Calculation of Bartlett Adjustments
Authors: Barndorff-Nielsen O. E. , Blaesild P. 
Published: December 2018

SUMMARY
We describe, for the numerical calculation of Bartlett adjustments, a method which may be of use when the cumulants of the log likelihood derivatives are easy to determine in one parametrization while the hypotheses to be tested are all linear in some other parametrization. The method relies on the cartesian tensorial nature of those cumulants and should be particularly convenient in connection with statistical packages with structure similar to GLIM.
Parameter Orthogonality and Approximate Conditional Inference
Authors: Cox D. R. , Reid N. 
Published: December 2018

SUMMARY
We consider inference for a scalar parameter Ψ in the presence of one or more nuisance parameters. The nuisance parameters are required to be orthogonal to the parameter of interest, and the construction and interpretation of orthogonalized parameters is discussed in some detail. For purposes of inference we propose a likelihood ratio statistic constructed from the conditional distribution of the observations, given maximum likelihood estimates for the nuisance parameters. We consider to what extent this is preferable to the profile likelihood ratio statistic in which the likelihood function is maximized over the nuisance parameters. There are close connections to the modified profile likelihood of Barndorff-Nielsen (1983). The normal transformation model of Box and Cox (1964) is discussed as an illustration.
Confidence and Conflict in Multivariate Calibration
Authors: Brown Philip J. , Sundberg Rolf 
Published: December 2018

SUMMARY
Multivariate calibration involves using an estimated relationship between a multivariate response Y and an explanatory vector X to predict unknown X in future from further observed responses. In controlled calibration with multivariate normal errors, the profile likelihood function for the unknown X (denoted ξ) displays a term which measures the mutual inconsistency of the given response vector (denoted Z) in predicting ξ. This inconsistency diagnostic fundamentally differentiates the behaviour of likelihood based and Bayes “confidence” regions from those of the unconditional sampling approach. In addition the diagnostic serves to pinpoint an inadequate response vector Z.
On the Bootstrap and Continuity Correction
Authors: Hall Peter 
Published: December 2018

SUMMARY
A continuity correction to the bootstrap is suggested, useful when the bootstrap is employed to approximate distributions of lattice-valued sample means. Versions of the correction are available quite generally, even in multivariate cases. Asymptotic theory reveals that the correction can reduce maximum rounding error by up to 50%. An example, where continuity correction is used in the construction of confidence intervals, illustrates the performance of the procedure with smaller samples.
Asymptotic Posterior Normality for Stochastic Processes Revisited
Authors: Sweeting T. J. , Adekola A. O. 
Published: December 2018

SUMMARY
The problem of demonstrating the limiting normality of a posterior distribution arising from some stochastic process is revisited. It is shown that certain processes of practical interest are not covered by conditions currently available in the literature. In this paper we present a fairly general set of conditions for asymptotic posterior normality which cover a wide class of problems. The theory is applied to a nonhomogeneous Poisson process and birth process.
Estimation and Inference by Compact Coding
Authors: Wallace C. S. , Freeman P. R. 
Published: December 2018

SUMMARY
The systematic variation within a set of data, as represented by a usual statistical model, may be used to encode the data in a more compact form than would be possible if they were considered to be purely random. The encoded form has two parts. The first states the inferred estimates of the unknown parameters in the model, the second states the data using an optimal code based on the data probability distribution implied by those parameter estimates. Choosing the model and the estimates that give the most compact coding leads to an interesting general inference procedure. In its strict form it has great generality and several nice properties but is computationally infeasible. An approximate form is developed and its relation to other methods is explored.
A Note on the Aitkin-Rubin Approach to Hypothesis Testing in Mixture Models
Authors: Quinn B. G. , McLachlan G. J. , Hjort N. L. 
Published: December 2018

SUMMARY
In testing for the number of components in a mixture, it is well known that regularity conditions do not hold for minus twice the log likelihood ratio to have its usual asymptotic null distribution of chi-squared. In an attempt to overcome some of the problems, it has been proposed in the literature that a prior distribution be placed on the mixing proportions. We show here that the usual regularity conditions still do not hold.
Symmetry Models and Hypotheses for Structured Data Layouts
Authors: Dawid A. P. 
Published: December 2018

SUMMARY
The structure of a data layout may be usefully described in terms of its symmetries, i.e. transformations which do not affect attitudes to the data. Interesting null hypotheses may be considered as imposing still further symmetries. These symmetry properties may be used to derive valid tests of such null hypotheses, and to construct appropriate statistical models for the data. The general ideas are developed and illustrated in the context of a replicated two-way classification.
Consistent Order Determination for Processes with Infinite Variance
Authors: Bhansali R. J. 
Published: December 2018

SUMMARY
Order determination for autoregressive processes when the residuals have a distribution belonging to the domain of attraction of an infinite variance stable law is examined. The use of the FPEα criterion of Bhansali and Downham (1977) is shown to provide a consistent estimator of the order with any α &gt; 0. Order determination for moving average processes is considered; a procedure based on autoregressive estimates of the inverse correlations, see Bhansali (1983), is shown to provide a consistent estimator of the order. Consistency of the estimated autoregressive and moving average parameters of an arbitrary order is established. The question of consistent discrimination between autoregressive and moving average models is also considered. The finite sample behaviour of the discrimination procedure is investigated by means of a simulation study.
A Note on Extended Quasi-Likelihood
Authors: Davidian M. , Carroll R. J. 
Published: December 2018

SUMMARY
We study the method of extended quasi-likelihood estimation and inference of a variance function recently proposed by Nelder and Pregibon. The estimates are inconsistent in general, and the test levels can be biased, but in many cases such as the exponential family the inconsistency and bias will not be a major concern. Extended quasi-likelihood is compared with Carroll and Ruppert's pseudo-likelihood method, which gives consistent estimates and, when slightly modified, asymptotically unbiased tests. We quantify the notion of a problem in which the amount of statistical information is large in each unit, showing in this instance that the two estimates are closely related and may be asymptotically equivalent in many important cases. However, in some cases outside the exponential family, an asymptotic bias can persist.
Balanced Polycross Designs
Authors: Morgan J. P. 
Published: December 2018

SUMMARY
Methods for constructing non-directional balanced polycross designs are presented, the balance being over row, column, and diagonal neighbours. The idea of a terrace is generalised and used to construct a set of arrays which enjoys complete balance. The arrays are then combined to obtain smaller, balanced designs; explicit constructions are given for ν &lt; 20 varieties. Many of the designs are in v or v + 1 replicates and have treatments balanced in rows and columns. As an additional benefit, some of the EBIBD designs of Kiefer and Wynn (1981) are produced.
Consistent Estimates for the Near(2) and Nlar(2) Time Series Models
Authors: Karlsen Hans , TjøStheim Dag 
Published: December 2018

SUMMARY
Consistent and asymptotically normal estimates for all four parameters of the NEAR(2) and NLAR(2) time series models are obtained. The estimates are given in explicit form and are easy to compute, but with simulation experiments the standard errors are large, especially for small values of the parameters, so very substantial sample sizes may be needed. Hence, from this point of view, the NEAR(2) and NLAR(2) models are of limited practical value. The simulations indicate, however, that reasonably good estimates can be obtained for sample sizes in the range used by Lawrance and Lewis in their investigation of a series of wind data.
Quantifying Expert Opinion in Linear Regression Problems
Authors: Garthwaite Paul H. , Dickey James M. 
Published: December 2018

SUMMARY
This paper describes a method for choosing a natural conjugate prior distribution for a normal linear sampling model. A person using the method to quantify his/her opinions performs specified elicitation tasks. The hyperparameters of the conjugate distribution are estimated from the elicited values. The method is designed to require elicitation tasks that people can perform competently and introduces a type of task not previously reported. A property of the method is that the assessed variance matrices are certain to be positive definite. The method is sufficiently simple to implement with an interactive computer program on a microcomputer.
Leave-K-Out Diagnostics for Time Series
Authors: Bruce Andrew G. , Martin R. Douglas 
Published: December 2018

SUMMARY
We propose diagnostics for autoregressive integrated moving average (ARIMA) model fitting for time series formed by deleting observations from the data and measuring the change in the estimates of the parameters. The use of leave-one-out diagnostics is a well-established tool in regression analysis. We demonstrate the efficacy of observation-deletion-based diagnostics for ARIMA models, addressing issues special to the time series setting. It is shown that the dependency aspect of time series data gives rise to a ‘smearing’ effect, which confounds the diagnostics for the coefficients. It is also shown that the diagnostics based on the innovations variance are much clearer and more sensitive than those for the coefficients. A ‘leave-k-out’ diagnostics approach is proposed to deal with patches of outliers, and problems caused by ‘masking’ are handled by use of iterative deletion. An overall strategy for ARIMA model fitting is given and applied to two data sets.