bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sequential Tests for Composite Hypotheses with Two Binomial Populations
Authors: Hoel D. G. , Weiss G. H. , Simon R. 
Published: December 2018

Summary
Sequential procedures for testing composite hypotheses about the parameters of two binomial distributions are developed. The testing of a null hypothesis against a onesided alternative is studied. It is shown that SPRT tests of simple hypotheses can be used for the composite situation with the desired error probabilities being preserved. Computer simulations indicate that when discrimination is based upon the difference in success probabilities, a procedure based on a likelihood ratio with nuisance parameters replaced by sequential maximum-likelihood estimates is shown to result in the smallest expected sample size.
Properties of the Exponential Scores Test for the K-Sample Problem Under the Lehmann Model
Authors: Burr P. , Young D. H. 
Published: December 2018

Summary
Properties of an exponential scores statistic which provides a non-parametric test of the homogeneity of k continuous populations are considered. Tables of exact critical values are given for the case k = 3 and some small sample sizes and approximations to the critical values are developed and evaluated. Small sample power comparisons are made with the Kruskal–Wallis rank test under the Lehmann model and some asymptotic efficiency results are developed under a limiting form of this model. Finally, a modified form of the test to allow for censoring is presented.
Identification of Parameters by the Distribution of a Maximum Random Variable
Authors: Anderson T. W. , Ghurye S. G. 
Published: December 2018

Summary
Under what conditions does the distribution of the maximum of a set of independent random variables uniquely determine the distributions of the component random variables? In the univariate case, a sufficient condition is that for every pair of distinct densities in the family of possible densities, one density converges to zero infinitely faster than the other, as x → ∞. Hence, the distribution of max {X i, i = 1, ..., n} when X i has the distribution N(μi, σ2 i), uniquely determines μi, σ2 i, i = 1, ..., n (except for indexing). The identifiability property is proved for bivariate normal distributions for every n when all correlations are positive; each component of the vector of maxima consists of the maximum of that component of the n constituent vectors.
A Cramér–Von Mises Type Goodness of Fit Statistic Related to √B
1 and B
2
Authors: Pettitt A. N. 
Published: December 2018

Summary
We investigate a statistic introduced by de Wet and Venter to test for normality and a Cramér–von Mises statistic asymptotically equivalent to this statistic.
It is shown that the Cramér–von Mises statistic can be written as a sum of components which are polynomial functions of the original standardized observations, the most important of which are Pearson's moment ratio statistics, √b 1 and b 2. Approximate small sample percentage points are given for the de Wet and Venter statistic. The asymptotic power of this statistic is investigated and the results of a small sample power study reported, confirming these results.
Bayes Factors from Mixed Probabilities
Authors: Dickey J. M. , Gunel E. 
Published: December 2018

Summary
A new expression for a Bayes factor as a weighted average of conditional Bayes factors is given, resembling the familiar relation for a marginal probability as a weighted average of conditional probabilities. Interpretations are given for the three contexts of (i) the use of a non-sufficient statistic, (ii) inference for a simple hypothesis versus a vague alternative in terms of inference for a dichotomy and (iii) opinion pooling before sampling.
Bayesian Analysis of a Two-Sample Problem Based on the Rank Order Statistic
Authors: Brooks R. J. 
Published: December 2018

Summary
A Bayesian analysis of the two-sample, location-shift problem based on the rank order statistic is described. It is assumed that the data available can only at best be considered as being on the ordinal scale. Exact and simplified approximate procedures are suggested. These are not distribution-free since the underlying population distributions need to be specified.
Density Estimation, Stochastic Processes and Prior Information
Authors: Leonard Tom 
Published: December 2018

Summary
A method is proposed for the non-parametric estimation of a probability density, based upon a finite number of observations and prior information about the smoothness of the density. A logistic density transform and a reproducing inner product from the first-order autoregressive stochastic process are employed to represent prior information that the derivative of the transform is unlikely to change radically within small intervals. The posterior estimate of the density possesses a continuous second derivative; it typically satisfies the frequentist property of asymptotic consistency. A direct analogy is demonstrated with a smoothing method for the time-dependent Poisson process; this is similar in spirit to the normal theory Kalman filter. A procedure for grouped observations in a histogram provides an alternative to the histospline method of Boneva, Kendall and Stefanov. Five practical examples are presented, including two investigations of normality, an analysis of pedestrian arrivals at a Pelican crossing and a histogram smoothing method for mine explosions data.
Two-Sample Cramér-Von Mises Type Rank Statistics
Authors: Pettitt A. N. 
Published: December 2018

Summary
In this paper weighted Cramér-von Mises type two-sample rank statistics are considered. It is shown that the statistics can be written as sums of squares of linear rank statistics. The asymptotic equivalence of the statistics to an easily computed version is shown and the asymptotic distribution of the statistics investigated. Finally, some specific examples are considered relating the Cramér-von Mises statistics to the Wilcoxon, Mood, Normal Scores and Savage rank statistics.
Reference Posterior Distributions for Bayesian Inference
Authors: Bernardo Jose M. 
Published: December 2018

Summary
A procedure is proposed to derive reference posterior distributions which approximately describe the inferential content of the data without incorporating any other information. More explicitly, operational priors, derived from information-theoretical considerations, are used to obtain reference posteriors which may be expected to approximate the posteriors which would have been obtained with the use of proper priors describing vague initial states of knowledge. The results obtained unify and generalize some previous work and seem to overcome criticisms to which this has been subject.
On Asymptotic Posterior Normality for Stochastic Processes
Authors: Heyde C. C. , Johnstone I. M. 
Published: December 2018

Summary
Asymptotic normality of the posterior distribution of a parameter in a stochastic process is shown to hold under conditions which do little more than ensure consistency of a maximum likelihood estimator. Much more stringent conditions are required to ensure asymptotic normality of the MLE. This contrast, which has implications of considerable significance, does not emerge in the classical context of independent and identically distributed observations.
The Symmetric M-Dimensional Simple Epidemic Process
Authors: Billard L. , Lacayo H. , Langberg Naftali A. 
Published: December 2018

Summary
In the classical simple epidemic process (Bailey, 1975), only one disease is involved. In this work, the model is generalized to the case in which susceptibles are simultaneously exposed to m, m ≥ 1, diseases. An individual once infected by a particular disease remains infected and is immune to further infection from another disease. The process is called an m-dimensional simple epidemic process. The limiting finite-dimensional joint state probabilities and the marginal probabilities associated with each disease are obtained.
On Outlier Rejection Phenomena in Bayes Inference
Authors: O'Hagan A. 
Published: December 2018

Summary
Inference is considered for a location parameter given a random sample. Outliers are not explicitly modelled, but rejection of extreme observations occurs naturally in any Bayesian analysis of data from distributions with suitably thick tails. For other distributions outlier rejection behaviour can never occur. These phenomena motivate new definitions of outlier-proneness and outlier-resistance. The definitions and methodology are Bayesian but the conclusions also have meaning for non-Bayesians because they are proved for arbitrary prior distributions. Thus, for example, the t distribution is said to be outlier-prone because it is shown that any admissible inference procedure applied to a t sample will effectively ignore extreme outlying observations regardless of prior information. On the other hand, the normal distribution, for example, is said to be outlier-resistant because it never allows outlier rejection, regardless of prior information.
The Exponential Autoregressive-Moving Average Earma(P,Q) Process
Authors: Lawrance A. J. , Lewis P. A. W. 
Published: December 2018

Summary
A new model for pth-order autoregressive processes with exponential marginal distributions, ear(p), is developed and an earlier model for first-order moving average exponential processes is extended to qth-order, giving an ema(q) process. The correlation structures of both processes are obtained separately. A mixed process, earma(p,q), incorporating aspects of both ear(p) and ema(q) correlation structures is then developed. The earma(p, q) process is an analog of the standard arma(p, q) time series models for Gaussian processes and is generated from a single sequence of independent and identically distributed exponential varables.
Lagrange-Multiplier Tests of Time-Series Models
Authors: Hosking J. R. M. 
Published: December 2018

Summary
The Lagrange-multiplier test procedure is applied to hypotheses concerning autoregressive moving-average time-series models. The portmanteau and Quenouille goodness-of fit tests are derived in this manner against specific alternative hypotheses and two other tests are obtained which have the nature of pure significance tests.
On the Relative Efficiency of Two Paired-Data Experiments
Authors: Brooks R. J. 
Published: December 2018

Summary
The expected gain in information through obtaining data is used to help evaluate, for the two-sample, paired-data problem, the consequences of using only the signs of the differences of the pairs of observations for inference. Relative efficiencies are calculated under the assumption of normal population distributions. The Bayesian approach to inference is used.
Invertibility and Generalized Invertibility of Time Series Models
Authors: Hallin Marc 
Published: December 2018

Summary
A new invertibility concept was recently introduced by Granger and Andersen (1978), generalizing the classical one for time-independent models; we show that, in the case of time-dependent models, it cannot be considered any more as a generalization. We also give a modified definition, which provides a strict generalization and still preserves Granger and Andersen's results.
Best Linear Unbiased Estimators for Repeated Surveys
Authors: Jones Roger G. 
Published: December 2018

Summary
Recent work on the problem of obtaining best linear unbiased estimators from a sample survey which is repeated on several occasions has centred on the effects of extending the model assumptions to allow for stochastic variation in the parameters being estimated.
In this paper a unified approach to the problem is given using least squares theory. The results of Blight and Scott (1973) are extended, and the relationships between their results, those of Scott and Smith (1974) and the classical approach of Patterson (1950), or its generalization by Gurney and Daly (1965), are clarified. The problems involved in putting the time series estimators into practice are examined, with particular reference to the role of stationarity.
Threshold Autoregression, Limit Cycles and Cyclical Data
Authors: Tong H. , Lim K. S. 
Published: December 2018

Summary
The notion of a limit cycle, which can only exist in a non-linear system, plays the key role in the modelling of cyclical data. We have shown that the class of threshold autoregressive models is general enough to capture this notion, a definition of which in discrete time is proposed. The threshold value has an interesting interpretation. Simulation results are presented which demonstrate that this new class of models exhibits some well-known features of non-linear vibrations. Detailed analyses of several real data sets are discussed.
Smooth Estimates for the Hazard Function
Authors: Anderson J. A. , Senthilselvan A. 
Published: December 2018

Summary
Consideration is given to the piecewise smooth estimation of hazard functions given by Cox's (1972) proportional hazards model. The smoothest estimate available hitherto is a step-function which is not continuous. The method used is penalized maximum likelihood estimation introduced by Good and Gaskins (1971) and developed by Silverman (1978). This gives a quadratic spline with discontinuities in the slope at the times of death. The knot points are at times of death and censoring. The hazard function and corresponding survival curves are estimated for a group of 51 women with carcinoma of the vulva. The estimated survival curve is compared with those given by Breslow's (1972) and Kalbfleisch and Prentice's (1973) methods.
Exchange and Interchange Procedures to Search for Optimal Row-And-Column Designs
Authors: Eccleston John A. , Jones Byron 
Published: December 2018

Summary
An algorithm to search for optimal row-and-column designs is described. It is similar to the one for block designs described by Jones and Eccleston (1980). The algorithm determines the treatment replications and searches for an optimal set of treatment concurrences. The optimality criterion is the sum of the weighted variances of a set of treatment contrasts of interest. Examples which illustrate the use of the algorithm are given and discussed.
Population Algebras
Authors: Holgate P. 
Published: December 2018

Summary
The use of non-associative algebras to describe the dependence of the composition of a population on two (possibly identical) populations in a previous generation, which has been exploited in the genetics context, is extended to deal with stochastic population processes.
Aspects of the theory brought into focus by the new applications are investigated. A number of specific examples are studied with a view to exploring the kind of problems that can be handled by the algebraic approach. Lastly, the interaction between population and classical genetic algebras is studied.
Maximum Likelihood Estimation for a Class of Multinomial Distributions Arising in Reliability
Authors: Samaniego F. J. , Jones L. E. 
Published: December 2018

Summary
Let Xi, i = 1, …,k be independent Bernoulli variables, with Xi ~ B(1, pi). Let Y = σ Xi, and consider a multinomial experiment based on n independent, identically distributed observations Y 1,…, Yn. This model is identifiable in the ordered parameter vector (p (1),…, p (k)), where p(i -1) ≤ p(i), and arises in reliability experiments in which k components in parallel have potentially different probabilities of failure. The family of multinomial distributions with the structure described above is properly contained in the family of general multinomial distributions with (k + 1) classes. Maximum likelihood estimation for this family is considered, and it is shown that for sufficiently large n, the maximum likelihood estimate of (p (1),…, p (k)) may be identified with high probability from the roots of a kth degree polynomial whose coefficients are consistent estimates of the elementary symmetric functions of the ratios θ(i) = p (i)/(1 - p (i)). A probability computation for the case k = 2 sheds light on the sample size required for our asymptotic results to take hold.
Empirical Bayes Analysis of Survival Time Data
Authors: Urridge J. B 
Published: December 2018

Summary
An empirical Bayes analysis of life-time data is described. The paper examines and extends the model discussed by Kalbfleisch (1978) in which the cumulative hazard function is treated as a gamma process. It is shown that the results obtained from the model by using grouped data (the usual case in practice) are more satisfactory than those obtained using continuous data. The paper also shows that a parameter representing the variability of the underlying gamma process can be estimated from such data. A further feature is that the analysis considers more general processes than the gamma process using the latter for illustrative purposes only.
On the Bayesian Steady Forecasting Model
Authors: Key P. B. , Godolphin E. J. 
Published: December 2018

Summary
A recent criterion of Smith (1979) seeks to generalize the steady forecasting model of Harrison and Stevens (1976) to take account of non-normality in the observation and system variables. In this note, predictive distributions and forecasts corresponding to quadratic and step loss functions are examined when the observation variable has a distribution belonging to the exponential family. It is shown that Smith's generalized model does not always yield forecasts which satisfy constraints obtained from the more familiar forms of steady forecasting. Two particular cases of the exponential family are used to illustrate the point.
The Circular Structural Model
Authors: Anderson Dorothy A. 
Published: December 2018

SUMMARY
This paper deals with consistent estimation of the structural parameters of the circular structural model. Identifiability is proved and the difficulties of dealing with the structural model in practice mentioned. A uniform distribution is then assumed for the angular variables and maximum likelihood estimation of the parameters examined. Also discussed are use of an approximation to the ratio of modified Bessel functions, asymptotic behaviour of the estimators as the ratio of the circle radius to the underlying structural variance parameter increases, and efficiency of other estimators of the parameters. The circular functional model, and the difficulties associated with it, is briefly mentioned.
A Method for Constructing Valid Restricted Randomization Schemes Using the Theory of D-Optimal Design of Experiments
Authors: White L. V. , Welch W. J. 
Published: December 2018

SUMMARY
Complete randomization of a one-way layout design may lead to problems if the resulting choice of layout is considered unsuitable by the experimenter. Valid restricted randomization is an attempt to retain the mathematical properties of complete randomization while ensuring that the probability of selection of an unsuitable layout is zero. The problem of constructing valid restricted randomization schemes, when these exist, is shown to be equivalent to a particular problem in the theory of optimal design of experiments.
A Goodness-Of-Fit Test Based on the Empirical Characteristic Function When Parameters Must be Estimated
Authors: Koutrouvelis I. A. , Kellermeier John 
Published: December 2018

SUMMARY
The goodness-of-fit test using the empirical characteristic function proposed by Koutrouvelis (1980) is extended to the case where parameters must be estimated. The proposed test statistic is shown to have an asymptotic chi-squared distribution with degrees of freedom reduced by the number of parameters estimated.
A Family of Distributions for Modelling Non-Elliptically Symmetric Multivariate Data
Authors: Cook R. Dennis , Johnson Mark E. 
Published: December 2018

SUMMARY
The paucity of distributions that can be used as an aid to modelling the structure of multivariate data is a recognized limitation. We present a relatively simple family of distributions for use in describing data which are not elliptically symmetric. Properties of the family are discussed and it is shown that multivariate Pareto, Burr and Logistic distributions are special cases. In addition, simulation methods for applications in Monte Carlo robustness studies are outlined and, finally, the fit to a data set on uranium exploration is compared to that obtained using the usual bivariate normal distribution.
Randomized Allocation of Treatments in Sequential Experiments
Authors: Bather J. A. 
Published: December 2018

Summary
Since the idea of sequential allocation was first studied, in a version of what is now called the multi-armed bandit problem, the results of many investigations have shown that, even when an optimal allocation rule can be found, its form depends on the parameters of the model and no single rule has emerged that is widely acceptable in practice. On the other hand, it can be shown that conventional methods, using equal allocations of the treatments under comparison, involve many unnecessary applications of inferior treatments even if the final choice of the “best” treatment relies on a sequential test. The evidence for this claim is based on an investigation of randomized allocation rules, where the choice of the next treatment is randomized in such a way that some preference is given to the “favourite” at any stage, but other treatments are not permanently excluded.
Further Results on Quasi-Complete Latin Squares
Authors: Freeman G. H. 
Published: December 2018

Summary
Further results are given for quasi-complete latin squares. These are latin squares which have the property that every unordered pair of elements occurs next to each other exactly twice in rows and twice in columns. All possible squares with first row in natural order are considered up to size 6, while possible squares for sizes 7 to 9 are increasingly numerous so are considered progressively less exhaustively. Pairs of squares which together have equality of concurrences of ordered pairs of elements are also examined. The uses of quasi-complete squares for the practical design of field experiments requiring equality of occurrences of unordered pairs of neighbours are discussed.
A Two-Variety Square Lattice Design Balanced Under Besag's Coding Scheme
Authors: Veevers A. , Zafar-Yab M. 
Published: December 2018

Summary
Experimental designs for planting a mixture of two varieties on a square lattice of sites are available which possess a certain balance property. The additional property of being balanced within each set of sites obtained using Besag's first-order coding scheme is evident in just one of these designs.
Parametrizations of Non-Linear Models
Authors: Hougaard Philip 
Published: December 2018

summary
in the literature there have been many suggestions on how to parametrize models. some properties you can seek are (1) stability of variance of the mle; (2) normal likelihood; (3) zero asymptotic skewness of the mle; (4) asymptotic unbiasedness of the mle.
the parametrizations corresponding to these demands are found in the one-dimensional curved exponential family. they all belong to a general class of transformations, but they are in general not identical. the transformations in this class are characterized by a differential equation. the transformations are identical in the nonlinear normal regression model.
Some E-Optimal Designs for the One-Way and Two-Way Elimination of Heterogeneity
Authors: Jacroux Mike 
Published: December 2018

Summary
In exploratory experiments aimed at providing as much information as possible on the effects of the treatments being studied and where heterogeneity needs to be eliminated in one or two directions, use of an equally replicated block or row-column design may mean wasting some of the available experimental units. In this paper we prove the E-optimality of several different types of block and row-column designs that have unequally replicated treatments; such unequally replicated designs can maximize the information on treatment effects without wasting units.