bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal Estimating Equations with Applications to Insect Development Times
Authors: Morton R. 
Published: February 2008

SummaryThis paper proposes a general method of estimation when a function of data and parameters is to be modelled, which is most efficient in a certain sense. It is applied to the following situation. If X(t) is the temperature at time t, the time Y taken for an insect to complete a stage of growth is assumed to satisfy the model.
Anomalies in the Gambling Market
Authors: Tuckwell R. H. 
Published: February 2008

SummaryApparent irregularities between the win and the place betting markets in Australian horseracing are examined. Win odds are used to predict win probabilities from which place probabilities are estimated and compared with the place odds on offer. It is concluded that anomalies do in fact exist and are capable, in theory at least, of profitable exploitation.
On Non‐Normal Invariance Principles for Multi‐Response Permutation Procedures
Authors: Brockwell Peter J. , Mielke Paul W. , Robinson John 
Published: February 2008

SummaryA non‐normal invariance principle is established for a restricted class of univariate multi‐response permutation procedures whose distance measure is the square of Euclidean distance. For observations from a distribution with finite second moment, the test statistic is found asymptotically to have a centered chi‐squared distribution. Spectral expansions are used to determine the asymptotic distribution for more general distance measures d, and it is shown that if d(x, y) = |x — y|u, u† 2, the asymptotic distribution is not invariant (i.e. it is dependent on the distribution of the observations).
Bayes Risk Consistency of Nonparametric Bayes Density Estimates2
Authors: Ghorai J. K. , Rubin H. , Rubin H. 
Published: February 2008

SummaryEven though the literature on nonparametric density estimation is large, the literature on Bayesian estimation of the density function is relatively small. The reason is the lack of a suitable prior over the space of probability density functions. There have been attempts to define priors over the space of probability measures, but they have not yielded any workable prior for the purpose of density estimation. Dubins & Freedman (1963) have denned random distribution functions which are singular with probability one. Kraft (1964) has denned a class of distribution functions which have derivatives but not continuous derivatives and hence are not suitable for density estimation. The only really convenient prior is the Dirichlet process prior due to Ferguson (1973), but unfortunately this prior concentrates all its mass over the discrete distribution with a dense set of jumps. Recently Lo (1978) has overcome this difficulty by taking convolution of the Dirichlet process with a fixed continuous kernel.In Section 2, the existence of a version of the posterior distribution and the conditional expectation for arbitrary prior over the space of continuous density functions are discussed. The Bayes risk consistency of the Bayes estimator is discussed in Section 3. The Bayes estimator and its properties with respect to two specific prior distributions are discussed in Section 4. In Section 5 some negative results are presented. Finally a numerical example is given in Section 6.
The Use of Multiplicity in a Household Survey of Disabilities
Authors: Jones Roger G. 
Published: February 2008

SummaryThe multiplicity technique has been proposed (Sirken, 1970) as a means of improving estimates of the number of rare “events” in a population. This paper describes one use of this technique, namely estimation of the number of persons with certain types of disability living in private dwellings in Canberra. The results are taken from three household surveys conducted in Canberra during 1978–79 in which a multiplicity rule linking parents, siblings and children of residents of sample households who were also living in private dwellings in Canberra was used. While no direct evidence on the level of non‐sampling error is available here, it appears that net response errors at least are not substantial using this multiplicity rule and that a reasonable gain in sampling efficiency results from the use of multiplicity.
On the Convergence of the Horvitz‐Thompson Estimator
Authors: Robinson P. M. 
Published: February 2008

SummaryWe investigate the degree to which the Horvitz‐Thompson estimator approximates the population mean in survey sampling. Specifically, conditions for (mean square) consistency are given, along with rates of convergence. The conditions involve only first and second‐order inclusion probabilities, and for various sampling designs they are easy to check.
Estimated Generalized Least Squares for a Heteroscedastic Regression Model
Authors: Anh V. V. 
Published: February 2008

SummaryThis paper considers the general linear regression model yc = X1β+ut under the heteroscedastic structure E(ut) = 0, E(u2) =σ2‐ (Xtβ)2, E(ut us) = 0, tæs, t, s= 1, T. It is shown that any estimated GLS estimator for β is asymptotically equivalent to the GLS estimator under some regularity conditions. A three‐step GLS estimator, which calls upon the assumption E(ut2) =s̀2(X,β)2 for the estimation of the disturbance covariance matrix, is considered.
An Investigation of two Sequence‐Comparison Statistics
Authors: Gordon A. D. 
Published: February 2008

SummaryThis paper describes an investigation of two statistics which compare two ordered sequences of objects by providing a measure of the discordance when the sequences are slotted together. Configurations having zero discordance are described. Plausible null models for data are postulated, and an account is given of a simulation study which investigated the distribution of the statistics when these models were used in the analysis of physical logging data from two wells.
ANALYSIS OF DATA FROM A SINGLE EPIDEMIC1
Authors: Becker Niels G. 
Published: February 2008

SummaryA model with nonrandom latent and infectious periods is suggested for epidemics in a large community. This permits a relatively complete statistical analysis of data from the spread of a single epidemic. An attractive feature of such models is the possibility of exploring how the rate of spread of the disease depends on the number of susceptibles and infectives. An application to smallpox data is included.
ON THE CONVERGENCE RESULT FOR THE SUPERCRITICAL BELLMAN‐HARRIS PROCESS1
Authors: Cohn Harry 
Published: February 2008

SummaryIt was proved in Cohn (1982) that for any finite offspring mean, supercritical Bellman‐Harris process {Z(t)} there exist some norming constants {C(t)} such that {Z(t)/C(t)} converges almost surely to a non‐degenerate random variable W. {C(t)} were defined to be the μ‐quantiles of {Z(t)}. Schuh (1982) has given an alternative proof of this result, identifying C(t) as “the Seneta constants” 1/(‐log Ft(‐1)(γ)), where F1(γ) = E(γZ(t)). Both proofs are long and complicated. It will be shown here that a much simpler proof can be devised from Cohn (1982), if use is made of an elementarily proved property given in Schuh (1982).
WEIGHTED AREA TECHNIQUES FOR THE ESTIMATION OF THE PARAMETERS OF A GROWTH CURVE1
Authors: Leedow M. I. , Tweedie R. L. 
Published: February 2008

SummaryA weighted area estimation technique which allows explicit estimation of the parameters of the growth curvelt= L∞[1‐exp(‐K(t‐t0))]is introduced.Simulated data are used to compare an optimal weighted area method with that of maximum likelihood. Length at age data from fisheries field studies are used to compare the weighted area and maximum likelihood techniques with the “graphical” method of Ford‐Walford.The weighted area methods achieve 80–90% efficiency on simulated data, and are shown to provide robust and sensible estimates on field data.
GENERALIZED INVERSES USED IN RECURSIVE ESTIMATION OF THE GENERAL LINEAR MODEL1
Authors: McGilchrist C. A. , Sandland R. L. , Hennessy J. L. 
Published: February 2008

SummaryRecursive methods in regression have proved useful in providing diagnostic tools for checking the model as well as checking the stability of the model over time. Such methods are now extended to deal with the problems of singularity that arise when one variable is completely confounded with previously fitted variables up to a particular time point. The problem is solved by setting it in the framework of the general linear model with dependent errors.
A NEW EXPANSION FOR THE MULTIVARIATE NORMAL DISTRIBUTION1
Authors: Moran P. A. P. 
Published: February 2008

SummaryA new expansion is given for the integral of the multivariate normal distribution over a region of the form (V1, m) ×.× (uk, ∞). The regions of convergence and divergence are partially identified and shown to be different from those of the tetrachoric expansion.
APPROXIMATIONS TO SOME TEST STATISTICS FOR PERMUTATION TESTS IN A COMPLETELY RANDOMIZED DESIGN1
Authors: Robinson J. 
Published: February 2008

SummaryUnder a randomization model for a completely randomized design permutation tests are considered based on the usual F statistic and on a multi‐response permutation procedure statistic. For the first statistic the first two moments are obtained so a comparision with the distribution under the normal theory model can be made. The second statistic is shown to converge in distribution to an infinite weighted sum of chi‐squared variates, the weights being the limits of the eigenvalues of a matrix depending on the distance measure used and the order statistics of the observations.
SOME STATISTICAL DISTRIBUTIONS OF USE IN ANALYSING BIRD COUNT DATA1
Authors: Watterson G. A. 
Published: February 2008

SummaryThe following report arose from an enquiry by a zoologist, J. M. Cullen, concerning various possible ways of analysing bird count data. He was particularly interested in two questions: (i) What is the best way to estimate abundances of various species?, and (ii) What evidence is there that birds occur randomly (in particular, as a Poisson process) as opposed to in flocks or according to some mutual repulsion process?
TESTING FOR MOVING AVERAGE REGRESSION DISTURBANCES
Authors: King Maxwell L. 
Published: February 2008

SummaryThis paper considers a locally optimal procedure for testing for first order moving average disturbances in the linear regression model. For this hypothesis testing problem, the Durbin‐Watson test is shown to be approximately locally best invariant while the new test is most powerful invariant in a given neighbourhood of the alternative hypothesis. Two versions of the test procedure are recommended for general use; one for problems involving positively correlated disturbances and one for negatively correlated disturbances. An empirical comparison of powers shows the clear superiority of the recommended tests over the Durbin‐Watson test. Selected bounds for the tests' significance points are tabulated.
TESTING THE CONCORDANCE OF EVENTS WITH THEIR PROBABILITIES
Authors: Moran P. A. P. 
Published: February 2008

SummaryA test due to D. R. Cox (1958a, 1958b) for the agreement between an observed set of independent events and independently prescribed probabilities is considered. Although the exact distribution can be found for small numbers of events this is often not possible. A method of modifying the scoring system slightly is shown to result in much simpler calculation with very little loss in power. The exact distribution is desirable because of slow convergence to normality. The logic of using a simulation test is considered.
MINIMAX ESTIMATION OF A MULTIVARIATE NORMAL MEAN UNDER A CONVEX LOSS FUNCTION
Authors: Lin Pi‐Erh , Mousa Amany 
Published: February 2008

SummaryLet X = (X1, ‐ Xp)prime; ˜ Np (μ, Σ) where μ= (μ1, ‐, μp)′ and Σ= diag (Σ21, ‐, Σ2p) are both unknown and p3. Let (ni ‐ 2) wi/Σ2i! X2ni, independent. of wi (I ≠ j = 1, ‐, p). Assume that (w1, ‐, wp) and X are independent. Define W = diag (w1, ‐, wp) and ¶ X ¶2w= X′W‐1Q‐1W‐1X where Q = diag (q1, ‐,n qp), qi > 0, i = 1, ‐, p. In this paper, the minimax estimator of Berger & Bock (1976), given by δ (X, W) = [Ip ‐ r(X, W) ¶ X ¶‐2w Q‐1W‐1] X, is shown to be minimax relative to the convex loss (δ ‐ μ)′[αQ + (1 ‐ α) Σ‐1] δ ‐ μ)/C, where C =α tr (Σ) + (1 ‐ α)p and 0 α 1, under certain conditions on r(X, W). This generalizes the above mentioned result of Berger & Bock.
ADMISSIBILITY OF LIKELIHOOD RATIO RULES IN COVARIATE CLASSIFICATION USING DEPENDENT SAMPLES
Authors: Bandyopadhyay Shibdas 
Published: February 2008

SummaryThe two populations considered for this study are two distinct time points. Samples consist of observations made at both the time points on every sampling unit. The unit to be classified is observed at one of the two time points. The observation vectors contain covariates, having same expectation at both the time points. In this set‐up admissibility of some likelihood ratio rules is established.
ON A STOCHASTIC MODEL FOR AN IRREVERSIBLE SYSTEM OF COMPARTMENTS
Authors: Becker Niels , Finch Caroline 
Published: February 2008

summaryA continuous time stochastic model for an irreversible system of kcompartments with nonhomogeneous Poisson inputs is considered. The model allows an arbitrary residence time for each of the compartments. Individuals may enter the system through any compartment and may depart from the system via any compartment.
STRATEGY, NONTRANSITIVE DOMINANCE AND THE EXPONENTIAL DISTRIBUTION
Authors: Kaminsky K. S. , Luks E. M. , Nelson P. I. 
Published: February 2008

SummaryAn easily programmed recursive formula for the evaluation of the distribution function of ratios of linear combinations of independent exponential random variables is developed. This formula is shown to yield the probability that one team beats another in a contest we call the special gladiator game. This game generates tournaments which exhibit nontransitive dominance and have some surprising consequences. Similar results are obtained for a recursive formula based on the geometric distribution.
A K‐SAMPLE EXTENSION TO FLIGNER'S CLASS OF TESTS FOR SCALE
Authors: Koffler Stephen L. , Penfield Douglas A. 
Published: February 2008

SummaryThe parameteric tests for equality of variance are well known. The classical F‐test is typically used to test the hypothesis of equality of two variances, while tests such as those developed by Bartlett (1937) are commonly used for the k‐sample hypothesis. These tests assume an underlying normal distribution and are quite sensitive to departures from normality (Box, 1953). Thus, when considering data that are from non‐normal distributions, alternative nonparametric tests must be employed.Fligner (1979) has proposed a class of two‐sample distribution‐free tests which possess very desirable properties and are attractive alternatives to other nonparametric tests for scale. The present paper extends the Fligner class of tests to the more general k‐sample case.
A GROUP OF SPLIT‐PLOT DESIGNS WITH A HETEROSCEDASTIC MODEL
Authors: Bhuyan K. C. 
Published: February 2008

SummaryFor a group of split‐plot designs it is assumed that the error variance is constant for a particular experiment but it varies from experiment to experiment. Assuming error variances to be known, estimators of the treatment parameters are obtained by weighted (generalised) least squares method and the corresponding analysis is given. For unknown error variances, an adjustment of the statistics using estimated weights is proposed for removing much of the resulting bias. The adjustment stems from a theorem due to Meier (1953).
GENERATING MECHANISMS OF, AND PARAMETER ESTIMATORS FOR, THE EXTREME VALUE DISTRIBUTION
Authors: Revfeim K. J. A. 
Published: February 2008

SummaryIdentified types of extreme value distributions are obtained from a basic form by variable transformation or truncation. The truncated distribution form is naturally generated as the extreme of a Poisson process with a randomly distributed size of each point event. If the size has a Negative Exponential distribution then, for moderate rates of the Poisson process, the extreme value distribution is of the type often called the Gumbel distribution. A Pareto distribution of size gives a second type which has natural association with a recurrence process of threshold exceedance.This specification of the extreme value distribution generates the identified distribution types in terms of physically meaningful parameters. The natural two parameter format provides the basis for a three parameter model which allows a more realistic description of the distribution of event sizes.Extreme values are often associated with data censoring due to some observations being out of range. Equations are given which demonstrate the simple association between estimators of the physically meaningful parameters in censored and uncensored situations.
COMBINING INFORMATION FROM LINEAR MODELS ON POSSIBLY DIFFERENT SCALES
Authors: Berman Mark 
Published: February 2008

SummaryThis paper examines the joint statistical analysis of M independent data sets, the jth of which satisfies the model λjYj=XjB +εj, where the λjare unknown and the εiare normally distributed with a known correlation structure. The maximum likelihood equations, their asymptotic covariance matrix, and the likelihood ratio test of the hypothesis that the λjs are all equal are derived. These results are applied to two examples.
CHANGE‐OVER DESIGNS WITH ERRORS FOLLOWING A FIRST ORDER AUTOREGRESSIVE PROCESS
Authors: Bora A. C. 
Published: February 2008

SummaryThis paper deals with the problem of analysing the change over design in the context of a first order autoregressive process for the error terms. The method of maximum likelihood has been adopted for estimating treatment effects. The conditions derived for obtaining a balanced change over design show that a change over design balanced in the absence of autocorrelation is not necessarily balanced in the presence of autocorrelation. Also, it is observed that the autocorrelation co‐efficient and the treatment effect when p≠0 can be tested as usual with the likelihood ratio test criterion.
SURVEY DESIGN
Authors: Foreman E. K. 
Published: February 2008

summarySurvey design involves planning and documenting questionnaires, sample selection and survey operations. It constitutes a simultaneous solution to numerous interrelated conceptual and practical design issues, more than may usually be settled effectively, or even recognised, in any reasonable time by contemplation alone. The alternative is an iterative approach, making a trial plan, testing and then improving it, and, if necessary, repeating it until a satisfactory plan is evolved. Pilot testing can then become the focus of design decisions by providing relevant data and operating experience, and by using experimental methods, as a basis for adroit revision of trial survey plans. This paper concerns the planning of efficient survey design processes along these lines.
DELIBERATELY BIASING SURVEY QUESTIONS
Authors: Fischer A. J. , Lewis R. D. 
Published: February 2008

summaryIn “before” and “after” surveys of Attitudes Towards Random Breath Testing in South Australia, three basic versions of the questionnaire were used. In the first, a set of “lead‐up” questions, which were designed to deliberately bias the results towards acceptance of the tests, was included before the main questions; in the second, there were no lead‐up questions; in the third, a different set of lead‐up questions was used, and was aimed at deliberately biasing the results against the tests. The results in two out of the four attempts to influence the answers (compared with no lead‐up questions) were significant in the expected direction, and in the other two cases were in the correct direction but not significant. The difference between the positive‐and negative‐biasing versions was highly significant in both cases. It is important to be aware that changes in context rather than in question wording per se can give rise to effects which dwarf the sampling error.
A NEW PROCEDURE FOR ASSESSING LARGE SETS OF CORRELATIONS
Authors: Cameron M. A. , Eagleson G. K. 
Published: February 2008

SummaryIn this paper, a new test of the hypothesis that all the correlations between a set of variables are zero is proposed. It is based on the asymptotic behaviour of the largest of the observed correlation coefficients. Here “asymptotic” refers to the size of the correlation matrix considered. Simulations show that the critical levels, calculated using the asymptotic theory, are conservative but quite accurate, even for small correlation matrices.
ON THE INVARIANCE OF ISOMETRY UNDER UNEQUAL SCALE CHANGES
Authors: James Ian R. 
Published: February 2008

SummaryIsometry is the independence of a size variable G(X) and shape. This note characterizes G(X) =γΦXb1i as the only continuous size variable for which isometry with respect to G can continue to hold for all unequal changes of scale of the variables Xi, assuming some conditions on the ranges of the Xi.
GROUPING PROBLEMS IN DISTRIBUTION‐FREE REGRESSION1
Authors: Brown B. M. 
Published: February 2008

SummaryIn regression models having symmetric errors, exact distribution‐free inference about individual parameters may be carried out by grouping observations, eliminating unwanted parameters within groups, and applying distribution free techniques for the symmetric location parameter problem. Models whose errors have identical but not symmetric distributions may obtain symmetry by taking differences between pairs of observations. Both grouping and differencing involve potential efficiency loss. The choice of an optimal scheme to minimize efficiency loss is expressible as a multi–assignment type of problem, whose solutions, exact and approximate, are discussed.
THE EFFECTS OF CORRELATION AMONG OBSERVATIONS ON THE ACCURACY OF APPROXIMATING THE DISTRIBUTION OF SAMPLE MEAN BY ITS ASYMPTOTIC DISTRIBUTION1
Authors: Sharma Subhash C. 
Published: February 2008

SummaryIn this paper, we investigate the effects of correlation among observations on the accuracy of approximating the distribution of sample mean by its asymptotic distribution. The accuracy is investigated by the Berry‐Esseen bound (BEB), which gives an upper bound on the error of approximation of the distribution function of the sample mean from its asymptotic distribution for independent observations. For a given sample size (n0) the BEB is obtained when the observations are independent. Let this be BEB. We then find the sample size (n*) required to have BEB below BEB0, when the observations are dependent. Comparison of n* with n0 reveals the effects of correlation among observations on the accuracy of the asymptotic distribution as an approximation. It is shown that the effects of correlation among observations are not appreciable if the correlation is moderate to small but it can be severe for extreme correlations.
THE MULTIPLE CORRELATION COEFFICIENT AND FISHER'S A STATISTIC1
Authors: Venable W.N. 
Published: February 2008

SummaryFisher's A statistic, often called the adjusted R2 statistic, is shown to be a close approximation to the maximum likelihood estimate of the multiple correlation coefficient, p2, based on the marginal distribution of R2. Expansions for the estimate are obtained. The same methods lead to maximum marginal likelihood estimators for the noncentrality parameters for noncentral X2 and F.