bluuebunny/crossref_metadata_2025 huggingface dataset:
SOCIOECONOMIC INFLUENCES ON EDUCATIONAL Attainment: EVIDENCE And IMPLICATIONS FOR THE TERTIARY EDUCATION FINANCE DEBATE
Authors: Miller Paul , Volker Paul
Published: February 2008

Examination of educational continuation decisions from Year 10 to tertiary entrance indicates that the major decision point appears to be at the end of Year 10. This is true even for persons entering tertiary education. Policies aimed at altering the social composition of high school graduates or tertiary sector participants should therefore be aimed at the Year 10 threshold or at even earlier stages in the educational process. However, as differences in participation in education amongst various socioeconomic groups appear to reflect differences in attitudes rather than purely financial factors, it may be difficult for authorities to expand school retention in the short term.
WHAT DRIVES AUSTRALIAN TEENAGE LABOUR FORCE PARTICIPATION?
Authors: Larum John , Beggs John J.
Published: February 2008

This paper examines the disaggregated components of Australian teenage labour force participation rates for males and females, for part‐time and full‐time participants and for those still at school. The paper also examines school participation rates for the same group of teenagers. Data are analysed for the period 1978 quarter 1 to 1987 quarter 2. In line with previous research, labour market demand factors are found to be major explanators of participation rates, but supply side effects due to the government's secondary school student allowance and also unemployment benefits are found to be important determinants of work and schooling decisions.
MODELLING OF TRANSITIONS BETWEEN EMPLOYMENT STATES FOR YOUNG AUSTRALIANS
Authors: Dunsmuir William , Tweedie Richard , Flack Lloyd , Mengersen Kerrie
Published: February 2008

This paper reports results from modelling the employment state calendars merged from the Australian Longitudinal Survey's 1985 and 1986 Area Samples. A semi‐Markov model is proposed for the transitions between the three employment states: employed, unemployed and not‐in‐the‐labour‐force.

Sojourn times in each of the states are fitted by the non‐parametric maximum likelihood methods proposed by Dinse & Larsen (1986), as are probabilities of transition between states. These models contrast with existing work where, typically, only the unemployment state is modelled and where unemployment times are fitted by parametric means.

The richness of the ALS data enables us to take in sex, age and educational attainment factors at two levels each by use of cohorts rather than by parametric models.

We show that age and education both play a significant role in duration of employment and unemployment. We also show that the initial state sampled (which will often be the first event post‐schooling) may be different from the other events to a much greater degree than length‐biasing would indicate.

We find that there is a significant “hard‐core” of unemployed and employed, and that a Weibull model fits the data well after this effect is taken out.
ON TESTING AGAINST RESTRICTED ALTERNATIVES FOR THE VARIANCES OF GAUSSIAN MODELS
Authors: Adegboye O. Sola , Gupta A.K.
Published: February 2008

Let π1,…,πp be p independent normal populations with means μ1…, μp and variances σ21,…, σ2p respectively. Let X(ni) be a simple random sample of size ni from πi, i = 1,…,p. Given the simple random samples X(n1),…, X(np) from π1,…,πp respectively, a test has been proposed for testing the homogeneity of variances H0: σ21=…σ2p, against the restricted alternative, H1: σ21≥…≥σ2p, with at least one strict inequality. Some properties of the test are discussed and critical values are tabulated.
OPTIMAL DESIGNS FOR FITTING A PROPORTIONAL HAZARDS REGRESSION MODEL TO DATA SUBJECT TO CENSORING
Authors: Becker Niels , McDonald Barry , Khoo Celestine
Published: February 2008

Suppose the probability model for failure time data, subject to censoring, is specified by the hazard function λ(t)exp(βT x), where x is a vector of covariates. Analytical difficulties involved in finding the optimal design are avoided by assuming that λ is completely specified and by using D‐optimality based on the information matrix for β Optimal designs are found to depend on β, but some results of practical consequence are obtained. It is found that censoring does not affect the choice of design appreciably when βT x ≥ 0 for all points of the feasible region, but may have an appreciable effect when βixi 0, for all i and all points in the feasible experimental region. The nature of the effect is discussed in detail for the cases of one and two parameters. It is argued that in practical biomedical situations the optimal design is almost always the same as for uncensored data.
LOWER BOUNDS FOR THE NORMAL APPROXIMATION OF A SUM OF INDEPENDENT RANDOM VARIABLES
Authors: Maesono Yoshihiko
Published: February 2008

The rates of convergence to the normal distribution are investigated for a sum of independent random variables. Using Stein's method, we derive a lower bound of the uniform distance between two distributions of independent sum and normal.
ON THE UNIMODALITY OF TRANSITION PROBABILITIES IN MARKOV CHAINS
Authors: Kijima Masaaki
Published: February 2008

Consider a discrete time Markov chain X(n) denned on {0,1,…} and let P be the transition probability matrix governing X(n). This paper shows that, if a transformed matrix of P is totally positive of order 2, then poj(n) and pio(n) are unimodal with respect to n, where pij(n) = Pr[X(n) = j |X(0) = i]. Furthermore, the modes of poj(n) and pio(n) are non‐increasing in j and I, respectively, when additionally P itself is totally positive of order 2. These results are transferred to a class of semi‐Markov processes via a uniformization.
AN ALGORITHMIC APPROACH TO BAYESIAN LINEAR MODEL CALCULATIONS
Authors: Carlin J.B.
Published: February 2008

A framework is described for organizing and understanding the computations necessary to obtain the posterior mean of a vector of linear effects in a normal linear model, conditional on the parameters that determine covariance structure. The approach has two major uses; firstly, as a pedagogical tool in the derivation of formulae, and secondly, as a practical tool for developing computational strategies without needing complicated matrix formulae that are often unwieldy in complex hierarchical models. The proposed technique is based upon symbolic application of the sweep operator SWP to an appropriate tableau of means and covariances. The method is illustrated with standard linear model specifications, including the so‐called mixed model, with both fixed and random effects.
TEACHING STATISTICS: A TALK ABOUT TIME SERIES
Authors: Anderson Oliver D.
Published: February 2008

When presenting research results to a mixed audience, it is common for seminar speakers either to address the specialists or the uninitiated (thus alienating the other, uncatered‐for group of listeners), or to risk falling between these two stools (and so satisfy no one). We suggest a way that the lecturer's dilemma might often be more optimally resolved; and, in so doing, present material which could provide useful ideas for wider application in enlivening formal class‐room instruction in time‐series analysis. Finally, we pre‐empt some of the predicted response that the content of this paper may trigger from certain experts; and discuss the general lack of emphasis on assumptions in statistical teaching.
A MARKOV CHAIN MODEL FOR MAGAZINE EXPOSURE
Authors: Danaher Peter J.
Published: February 2008

Klotz's (1973) Markov chain model for dependent Bernoulli trials is applied to magazine exposure distributions. Simple parameter estimates are derived and are shown to compare well with the maximum likelihood estimates. The Markov model is fitted to forty magazines from a large print media survey and compares favourably with the most popular non‐proprietary magazine model, the beta‐binomial model. In addition, the Markov model is used to simulate magazine exposure distributions.
COVARIANCES FOR FIXED INTERVAL SMOOTHED KALMAN FILTER PARAMETER ESTIMATES
Authors: Haslett Stephen
Published: February 2008

Given time series data for fixed intervalt= 1,2,…,Mwith non‐autocorrelated innovations, the regression formulae for the best linear unbiased parameter estimates at each timetare given by the Kalman filter fixed interval smoothing equations. Formulae for the variance of such parameter estimates are well documented.

However, formulae for covariance between these fixed interval best linear parameter estimates have previously been derived only for lag one. In this paper more general formulae for covariance between fixed interval best linear unbiased estimates at timestandt ‐ lare derived fort= 1,2,…,Mandl= 0,1,…,t‐ 1. Under Gaussian assumptions, these formulae are also those for the corresponding conditional covariances between the fixed interval best linear unbiased parameter estimates given the data to timeM.They have application, for example, in determination via the expectation‐maximisation (EM) algorithm of exact maximum likelihood parameter estimates for ARMA processes expressed in statespace form when multiple observations are available at each time point.
A NEW UPPER BOUND FOR THE EFFICIENCY FACTOR OF A BLOCK DESIGN
Authors: Tjur Tue
Published: February 2008

An upper bound for the efficiency factor of a block design, which in many cases is tighter than those reported by other authors, is derived. The bound is based on a lower bound for E(1/X) in terms of E(X) and var(X) for a random variableXon the unit interval. For the special case of resolvable designs, an improved bound is given which also competes with known bounds for resolvable designs in some cases.
LOSS OF INFORMATION ASSOCIATED WITH THE ORDER STATISTICS AND RELATED ESTIMATORS IN THE DOUBLE EXPONENTIAL DISTRIBUTION CASE
Authors: Akahira Masafumi , Takeuchi Kei
Published: February 2008

Fisher (1934) derived the loss of information of the maximum likelihood estimator (MLE) of the location parameter in the case of the double exponential distribution. Takeuchi & Akahira (1976) showed that the MLE is not second order asymptotically efficient. This paper extends these results by obtaining the (asymptotic) losses of information of order statistics and related estimators, and by comparing them via their asymptotic distributions up to the second order.
AN ITERATIVE METHOD FOR ESTIMATION OF PARAMETERS IN A CLUSTERED SAMPLING MODEL
Authors: Scott David J. , Qian Wang Dong
Published: February 2008

This paper considers an iterative method for obtaining maximum likelihood estimates for a contingency table derived from a clustered sampling model. Comparisons are made with other methods proposed in the literature.
A ROLE FOR VARIOGRAMS
Authors: Robinson G.K.
Published: February 2008

In time series texts and journals, variograms are mentioned seldom, if at all. The autocovariance function is preferred. However there are situations where the variogram can be estimated with moderate precision but the autocovariance function cannot, because the variance of the process is not well known. If the problem to be solved does not require the process variance for its solution then it is generally more straightforward to use the variogram rather than the autocovariance function in solving this problem.
A CLASS OF NONPARAMETRIC TESTS FOR THE ONE‐SAMPLE LOCATION PROBLEM
Authors: Mehra K.L. , Prasad N.G.N. , Rao K.S. Madhava
Published: February 2008

A class of “optimal”U‐statistics type nonparametric test statistics is proposed for the one‐sample location problem by considering a kernel depending on a constant a and all possible (distinct) subsamples of size two from a sample of n independent and identically distributed observations. The “optimal” choice of a is determined by the underlying distribution. The proposed class includes the Sign and the modified Wilcoxon signed‐rank statistics as special cases. It is shown that any “optimal” member of the class performs better in terms of Pitman efficiency relative to the Sign and Wilcoxon‐signed rank statistics. The effect of deviation of chosen a from the “optimal” a on Pitman efficiency is also examined. A Hodges‐Lehmann type point estimator of the location parameter corresponding to the proposed “optimal” test‐statistics is also defined and studied in this paper.
ESTIMATION OF THE PARAMETER OF A POISSON DISTRIBUTION USING A LINEX LOSS FUNCTION
Authors: Sadooghi‐Alvandi S.M.
Published: February 2008

This paper considers estimation of the parameter of a Poisson distribution using Varian's (1975) asymmetric LINEX loss function L (δ) = b{exp(aδ) ‐ aδ ‐ 1}, where δ is the estimation error and b > 0, a 0. It is shown that for a < 0, the sample mean X¯ is admissible whereas for a > 0, X¯ is dominated by c*X¯, where c*= (n/a)log(1+a/n). Practical implications of this result are indicated. More general results, concerning the admissibility of estimators of the form cX¯+ d are also presented.
ESTIMATING BIOMASS IN A VEGETATION MOSAIC USING DOUBLE SAMPLING WITH REGRESSION
Authors: Catchpole W.R. , Catchpole E.A.
Published: February 2008

In some vegetation types, total fuel loading (phytomass) can be predicted by easily‐measured variables such as vegetation type and height. A double sampling scheme is proposed in which fuel loading is estimated on a particular site by using quadrat sampling within patches of similar vegetation to develop a general prediction equation, and then line intercept sampling is used to estimate the mean of the easily‐measured variables on the site. This method is applied to estimate the total fine fuel loading on a heathland site.
BAHADUR EFFICIENCY OF LIKELIHOOD RATIO AND RELATED TESTS IN NONREGULAR MODELS
Authors: Kourouklis Stavros
Published: February 2008

Consider the model of k populations whose densities are nonreg‐ular in the sense that they involve one or two unknown truncation parameters. In this paper a unified treatment of the problem of Bahadur efficiency of the likelihood ratio test for such a model is presented. The Bahadur efficiency of a certain test based on the union‐intersection principle is also studied. Some of these results are then extended to a larger class of nonregular densities.
SELECTION PROCEDURES FOR SCALE PARAMETERS USING TWO‐SAMPLE U‐STATISTICS
Authors: Gill A.N. , Mehta G.P.
Published: February 2008

Let be k independent populations having the same known quantile of order p (0 p 1) and let F(x)=F(x/i) be the absolutely continuous cumulative distribution function of the ith population indexed by the scale parameter 1, i = 1,…, k. We propose subset selection procedures based on two‐sample U‐statistics for selecting a subset of k populations containing the one associated with the smallest scale parameter. These procedures are compared with the subset selection procedures based on two‐sample linear rank statistics given by Gill & Mehta (1989) in the sense of Pitman asymptotic relative efficiency, with interesting results.
A NEW APPROACH TO MAXIMUM LIKELIHOOD ESTIMATION OF THE THREE‐PARAMETER GAMMA AND WEIBULL DISTRIBUTIONS
Authors: Bai Jun , Jakeman Anthony J. , McAleer Michael
Published: February 2008

A new approach, is proposed for maximum likelihood (ML) estimation in continuous univariate distributions. The procedure is used primarily to complement the ML method which can fail in situations such as the gamma and Weibull distributions when the shape parameter is, at most, unity. The new approach provides consistent and efficient estimates for all possible values of the shape parameter. Its performance is examined via simulations. Two other, improved, general methods of ML are reported for comparative purposes. The methods are used to estimate the gamma and Weibull distributions using air pollution data from Melbourne. The new ML method is accurate when the shape parameter is less than unity and is also superior to the maximum product of spacings estimation method for the Weibull distribution.
NEAREST NEIGHBOUR ANALYSIS OF UNEQUALLY REPLICATED TRIALS
Authors: Pithuncharurnlap M. , Basford K.E. , Cullis B.R.
Published: February 2008

There has been much work on the use of neighbouring plots to control environmental variation in the analysis of agricultural field experiments. In particular, the Residual Maximum Likelihood Neighbour (REMLN) analysis of Gleeson&Cullis (1987) appears very promising. The application of the REMLN analysis to an unequally replicated field trial augmented with an additional variety planted every six plots in a grid system is here compared with a covariance (COV) analysis using the neighbouring grid or check plot values as the covariate. The results indicate that the REMLN analysis gives more accurate estimates of treatment contrasts than the COV analyses, but that the estimate of treatment means can be biased. The bias depends on the mean of the check plot. This bias can be removed by adjusting the estimates of the treatment means such that their average equals the average of the raw means rather than that of the raw data.
ESTIMATING THE COMMON MEAN OF A BIVARIATE NORMAL POPULATION
Authors: Kumar Somesh , Sharma Divakar
Published: February 2008

For estimating the common mean of a bivariate normal distribution, Krishnamoorthy & Rohatgi (1989) proposed some estimators which dominate the maximum likelihood estimator in a large region of the parameter space. We consider some modifications of these estimators and study their risk performance.
COMPARISONS AND SELECTION OF TWO‐PARAMETER EXPONENTIAL POPULATIONS
Authors: Bofinger Eve
Published: February 2008

For two‐parameter exponential populations with the same scale parameter (known or unknown) comparisons are made between the location parameters. This is done by constructing confidence intervals, which can then be used for selection procedures. Comparisons are made with a control, and with the (unknown) “best” or “worst” population. Emphasis is laid on finding approximations to the confidence so that calculations are simple and tables are not necessary. (Since we consider unequal sample sizes, tables for exact values would need to be extensive.)
RENEWAL COUNTING PROCESS INDUCED BY A DISCRETE MARKOV CHAIN
Authors: Adke S.R. , Balakrishna N.
Published: February 2008

Some renewal theoretic properties of a renewal counting process induced by a Markov chain on the set of non‐negative integers are established, namely, analogues of the classical elementary, Blackwell, and Breiman theorems and the key renewal theorem. These results generalize those of Vere‐Jones (1975) who considered a Markov chain on the set of positive integers.
ALLOCATION OF REMOTELY SENSED DATA USING MARKOV MODELS FOR IMAGE DATA AND PIXEL LABELS
Authors: Kiiveri H.T. , Campbell N.A.
Published: February 2008

For remotely sensed data, this paper reviews the Bayesian approach to the allocation of picture elements (pixels) to groups. Group labels are assumed a priori to be spatially correlated and, conditional on the labels, the image data are also assumed to be spatially correlated. The models considered have the property that the posterior distribution of the pixel labels given the image data inherits conditional independence constraints. Two allocation algorithms which exploit this fad are discussed. These algorithms are based on maximising the posterior distribution, and involve the use of neighbouring image and label data to update the label of any given pixel. The effect of spatial correlation in the image data on allocation performance is examined.
QUALITY CONTROL WITH MULTIVARIATE DATA
Authors: Sparks Ross S.
Published: February 2008

The paper examines statistical process control of bivariate and multivariate data, using in particular the multivariate equivalents of the univariate Shewhart chart, CUSUM chart and the Exponentially Weighted Moving Average chart. This illustrates the usefulness of Principal Component methods in statistical process control with multivariate data.
A NEW APPROACH TO DISCRIMINATION AND CLASSIFICATION USING A HAUSDORFF TYPE DISTANCE
Authors: Chatterjee Sangit , Narayanan A.
Published: February 2008

A new method of discrimination and classification based on a Hausdorff type distance is proposed. In two groups, the Hausdorff distance is defined as the sum of the furthest distance of the nearest elements of one set to another. This distance has some useful properties and is exploited in developing a discriminant criterion between individual objects belonging to two groups based on a finite number of classification variables. The discrimination criterion is generalized to more than two groups in a couple of ways. Several data sets are analysed and their classification accuracy is compared to that obtained from linear discriminant function and the results are encouraging. The method in simple, lends itself to parallel computation and imposes less stringent conditions on the data.
SEMIPARAMETRIC COMPARISON OF REGRESSION CURVES VIA NORMAL LIKELIHOODS
Authors: Carroll R.J. , Hall Peter
Published: February 2008

Härdle & Marron (1990) treated the problem of semiparametric comparison of nonparametric regression curves by proposing a kernel‐based estimator derived by minimizing a version of weighted integrated squared error. The resulting estimators of unknown transformation parameters are n‐consistent, which prompts a consideration of issues. of optimality. We show that when the unknown mean function is periodic, an optimal nonparametric estimator may be motivated by an elegantly simple argument based on maximum likelihood estimation in a parametric model with normal errors. Strikingly, the asymptotic variance of an optimal estimator of θ does not depend at all on the manner of estimating error variances, provided they are estimated n‐consistently. The optimal kernel‐based estimator derived via these considerations is asymptotically equivalent to a periodic version of that suggested by Härdle & Marron, and so the latter technique is in fact optimal in this sense. We discuss the implications of these conclusions for the aperiodic case.
THE BOOTSTRAP RISK OF LINEAR EMPIRICAL BAYES ESTIMATES
Authors: Mousa M. A. M. Ali
Published: February 2008

This paper discusses the bootstrap risk of the linear empirical Bayes estimate of the form θ=Ǎ+B̌x, where x is the current observation, and Ǎ and B̌ are generally functions of the estimates of the prior parameters. The standard error of this risk is developed and ‘computations’ of both the bootstrap risk and its standard error are made.
A MODIFIED GROWTH CURVE MODEL AND ITS APPLICATION TO CLINICAL STUDIES
Authors: Weissfeld L.A. , Kshirsagar Anant M.
Published: February 2008

This paper considers a technique for applying growth curve analysis to data which are correlated across different groups. The method is illustrated using a study comparing three methods of suctioning an endotracheal tube.
ROBUST METHODS FOR DEPENDENT CELL PEDIGREE DATA
Authors: Marschner Lan C.
Published: February 2008

This paper studies a robust approach to the analysis of cell pedigree data, building on the work of Huggins & Marschner (1991) which discussed M‐estimation for the so‐called bifurcating autoregressive process. The study allows for incomplete observation of the pedigree, and incorporates the possibility of additive effects outliers, as discussed in the time series literature. Some properties of the proposed estimation procedure are studied, including a Monte Carlo investigation of robustness in the presence of contamination.
ON QUASI‐LIKELIHOOD METHODS AND ESTIMATION FOR BUNCHING PROCESSES AND HETEROSCEDASTIC REGRESSION MODELS
Authors: Heyde C.C. , Lin Y.‐X.
Published: February 2008

This paper is concerned with quasi‐likelihood estimation methods which provide benchmarks in efficiency against which other methods may be as sessed. We discuss general principles and illustrate them by the estimation of parameters for branching processes with immigration and heteroscedastic regression models.
RECURSIVE RESIDUALS FOR MULTIVARIATE REGRESSION MODELS
Authors: Tobing H. , McGilchrist C.A.
Published: February 2008

Recursive residuals and their relationship to the recursive estimation of regression parameters have been developed for unvaried regression mod els. Such residuals and estimates have been used to test the constancy of regression over time. The current paper extends this work to multivariate regression modal.
FITTING FINITE MIXTURE MODELS IN A REGRESSION CONTEXT
Authors: Jones P.N. , McLachlan G. J.
Published: February 2008

Suppose data are collected in a three‐mode fashion (individuals x items X attributes), and it is sought to cluster the individuals into groups on the basis of lineat relations between scores on the attributes for each item and auxiliary measurements made on the same items. A mixture model is pro posed and the EM algorithm is used to fit it to the data by simultaneously estimating the group parameters and allocating individuals to groups. The method is illustrated by a simulation study and a real example in which consumers are clustered on the basis of product scores that are related to a sensory laboratory measurement.
EFFECTIVE CONDITIONING
Authors: Lloyd Christopher
Published: February 2008

The ultimate aim of conditioning is to obtain a more relevant estimate of estimation precision by separating experiments of higher precision from those of lower precision. The cost is the information, if any, contained in the conditioning variable. This paper considers the problem of quantifying the costs and benefits of conditioning. Several examples are studied and some of the asymptotic consequences explored.
GOODNESS OF FIT TESTS FOR HETEROGENEITY IN A CAPTURE‐RECAPTURE EXPERIMENT
Authors: Yip Paul
Published: February 2008

A class of goodness of fit tests is proposed to test for heterogeneity in a population in a capture‐recapture experiment when individuals' capture intensities may depend on time in an unspecified manner.
MULTIVARLYTE ANALYSIS OF CLUSTERED SURVEY DATA: TESTING EQUALITY OF COVARLANCE MATRICES
Authors: Pezvaiz Muhammad Khalid , Skinner C.J.
Published: February 2008

The problem of testing the hypothesis of equality of covariance matrices in the presence of two‐stage sampling is considered. Asymptotic test procedures based on linearization, grouping and jackknifing with or without transformation are proposed. The finite sample properties of these procedures are investigated in sampling experiments both from simulated known distributions and from a natural population.
LENGTH‐BIASING, CHARACTERIZATIONS OF LAWS AND THE MOMENT PROBLEM
Authors: Pakes Anthony G. , Khattree Ravindra
Published: February 2008

A positive random variableXwith a finite mean has an induced length‐biased law represented byY, andYis stochastically larger thanX. An independent uniform random contraction ofY, UY, has the same law asXif and only if the latter is exponential. This property is extended to non‐uniform contractions and a more general notion of length‐biasing. The distributional equality ofXandWleads to a functional equation for the moment function ofX, which has either Infinitely many solutions or none. WhenUis constant,Xcan have a log‐normal law, but it can also have laws with the same moment sequence as this log‐nod law. The case whereUhas a certain beta, or generalized beta, law give t3 characterizations of generalized gamma laws, or to products of independent copies of them. This occurs even when these laws are not determined by their moment sequences.
FACTORIAL ANALYSIS OF RECIDIVIST DATA
Authors: Maller R.A.
Published: February 2008

This paper presents a method of fitting factorial models to recidivism data consisting of the (possibly censored) time to ‘fail’ of individuals, in order to test for differences between groups. Here ‘failure’ means rearrest, reconviction or reincarceration, etc. A proportion P of the sample is assumed to be ‘susceptible’ to failure, i.e. to fail eventually, while the remaining 1‐P are ‘immune’, and never fail. Thus failure may be described in two ways: by the probability P that an individual ever fails again (‘probability of recidivism’), and by the rate of failure Λ for the susceptibles. Related analyses have been proposed previously: this paper argues that a factorial approach, as opposed to regression approaches advocated previously, offers simplified analysis and interpretation of these kinds of data. The methods proposed, which are also applicable in medical statistics and reliability analyses, are demonstrated on data sets in which the factors are Parole Type (released to freedom or on parole), Age group (≤ 20 years, 20–40 years, > 40 years), and Marital Status. The outcome (failure) is a return to prison following first or second release.
A COMPARISON OF DESIGN‐BASED AND MODEL‐BASED ESTIMATORS OF THE FINITE POPULATION DISTRIBUTION FUNCTION
Authors: Dorfman Alan H.
Published: February 2008

Rival estimators of the distribution function of a finite population, derived from the model‐based and design‐based approaches to sampling inference, are compared. A design‐based estimator due to Rao, Kovar & Mantel (1990) has the desirable property from a model‐based viewpoint, of being model‐unbiased under misspecification of model, when the sample meets certain conditions. A modified version of this estimator is suggested; it relies less on design‐based ingredients, and in particular avoids second‐order inclusion probabilities. It is the preferred estimator when, as is often the case in sampling practice, the model is adopted without thorough consideration of goodness of fit. However, if standard regression procedures of model construction and criticism are employed, then a strictly model‐based estimator does better.
DO NOT WEIGHT FOR HETEROSCEDASTICITY IN NONPARAMETRIC REGRESSION
Authors: Jones M.C.
Published: February 2008

The potential role of weighting in kernel regression is examined. The concept that weighting has something to do with heteroscedastic errors is shown to be false. However, weighting does affect bias, and ways in which this might be exploited are indicated.
SADDLEPOINT METHODS FOR BOOTSTRAP CONFIDENCE BANDS IN NONPARAMETRIC REGRESSION
Authors: Wang Suojin
Published: February 2008

Bootstrap techniques have been used to construct confidence bands in nonparametric regression problems (Härdle & Bowman, 1988). Yet the required simulation is generally computationally intensive and therefore makes it difficult to conduct further investigations. In this paper, two saddlepoint methods are considered as alternatives to the naive simulation procedure. Some improvements to Härdle & Bowman's bootstrap method are suggested. The improvements are numerically verified using these efficient and accurate analytic methods.
PREDICTION OF THE FINITE POPULATION DISTRIBUTION FUNCTION UNDER GAUSSIAN SUPERPOPULATION MODELS
Authors: Bolfarine Heleno , Sandoval Mönica C.
Published: February 2008

This article considers optimal prediction of the finite population distribution function under Gaussian superpopulation models, which allows auxiliary prior information to be incorporated into the estimation process. Large sample approximations for the variance of the optimal predictors are derived in some special important cases. A small scale Monte Carlo study illustrates comparisons between the optimal predictor and some others which are proposed in the literature. The conclusion is that the optimal predictor can be considerably more efficient in situations where the normal superpopulation model is adequate.
SOME PROPERTIES OF PROFILE BOOTSTRAP CONFIDENCE INTERVALS
Authors: Kabaila Paul
Published: February 2008

We consider the problem of finding an equi‐tailed confidence interval, with coverage probability (1‐α), for a scalar parameter θ0 in the presence of a (possibly infinite dimensional) nuisance parameter ψ0. It is supposed that the value taken by θ0 does not restrict the value that ψ0 may take and vice‐versa. Given a sensible estimate ψn of ψ0, profile bootstrap confidence interval for θ0 is defined to be the exact equi‐tailed confidence interval with coverage probability (1‐α) assuming that ψ0=ψn. We compare the properties of the profile bootstrap confidence interval and the ordinary bootstrap confidence interval when they are based on studentised and unstudentised quantities. Under mild regularity conditions the profile bootstrap confidence interval is always a subset of the set of allowable values of θ0 and is transformation‐respecting when based on either an unstudentised quantity or a studentised quantity satisfying certain restrictions. As a confidence interval for the autoregressive parameter of an AR(1) process, the profile bootstrap confidence interval has important advantages over the ordinary bootstrap confidence interval based on a studentised quantity.
ON STUDENTIZING AND BLOCKING METHODS FOR IMPLEMENTING THE BOOTSTRAP WITH DEPENDENT DATA
Authors: Davison A.C. , Hall Peter
Published: February 2008

Gōtze & Kūnsch (1990) announced that a certain version of the bootstrap percentile‐t method, and the blocking method, can be used to improve on the normal approximation to the distribution of a Studentized statistic computed from dependent data. This paper shows that this result depends fundamentally on the method of Studentization. Indeed, if the percentile‐t method is implemented naively, for dependent data, then it does not improve by an order of magnitude on the much simpler normal approximation despite all the computational effort that is required to implement it. On the other hand, if the variance estimator used for the percentile‐t bootstrap is adjusted appropriately, then percentile‐t can improve substantially on the normal approximation.
A CHARACTERIZATION OF GEOMETRIC DISTRIBUTIONS THROUGH CONDITIONAL INDEPENDENCE
Authors: Liang TaChen , Balakrishnan N.
Published: February 2008

LetX1,…,Xnbe mutually independent non‐negative integer‐valued random variables with probability mass functionsfi(x) > 0 forz= 0,1,…. LetEdenote the event that {X1≥X2≥…≥Xn}. This note shows that, conditional on the eventE, Xi‐Xi+ 1 andXi+ 1 are independent for all t = 1,…,kif and only ifXi(i= 1,…,k) are geometric random variables, where 1 ≤k≤n‐1. Thekgeometric distributions can have different parameters θi,i= 1,…,k.
EXPECTATIONS OF RATIOS OF QUADRATIC FORMS IN NORMAL VARIABLES: EVALUATING SOME TOP‐ORDER INVARIANT POLYNOMIALS
Authors: Smith Murray D.
Published: February 2008

Chikuse's (1987) algorithm constructs top‐order invariant polynomials with multiple matrix arguments. Underlying it is a set of simultaneous equations for which all integer solutions must be found. Each solution represents a component of the sum of terms which comprise the polynomial. The system of equations has a specialised structure which may be exploited to obtain a polynomial with r matrix arguments in terms of a polynomial with r‐1 matrix arguments. This is demonstrated for two particular polynomials that have two matrix arguments. These results are applied to problems involving expectations of ratios of quadratic forme in normal variables; analytic as well as computable formulae are derived.
THE REDUCED‐RANK GROWTH CURVE MODEL FOR DISCRIMINANT ANALYSIS OF LONGITUDINAL DATA
Authors: Albert Jeffrey M. , Kshirsagar Anant M.
Published: February 2008

This paper presents a method of discriminant analysis especially suited to longitudinal data. The approach is in the spirit of canonical variate analysis (CVA) and is similarly intended to reduce the dimensionality of multivariate data while retaining information about group differences. A drawback of CVA is that it does not take advantage of special structures that may be anticipated in certain types of data. For longitudinal data, it is often appropriate to specify a growth curve structure (as given, for example, in the model of Potthoff & Roy, 1964). The present paper focuses on this growth curve structure, utilizing it in a model‐based approach to discriminant analysis. For this purpose the paper presents an extension of the reduced‐rank regression model, referred to as the reduced‐rank growth curve (RRGC) model. It estimates discriminant functions via maximum likelihood and gives a procedure for determining dimensionality. This methodology is exploratory only, and is illustrated by a well‐known dataset from Grizzle & Allen (1969).
TESTING THE SIGNIFICANCE OF GENOTYPE‐ENVIRONMENT INTERACTION
Authors: Williams E.R. , Wood J.T.
Published: February 2008

The AMMI (additive main effects‐multiplicative interaction) model is often used to investigate interactions in two‐way tables, in particular for genotype‐environment interactions. Both Gollob (1968) and Mandel (1969, 1971) proposed methods for testing the significance of such interactions. These methods are compared using simulated data. Our results support Mandel's conclusions, but his method is conservative and we recommend a test proposed by Johnson & Graybill (1972).
AN ALGORITHM FOR CONSTRUCTING OPTIMAL RESOLVABLE ROW‐COLUMN DESIGNS
Authors: Nguyen Nam‐Ky , Williams E.R.
Published: February 2008

This paper describes an effective algorithm for constructing optimal or near‐optimal resolvable row‐column designs (RCDs) with up to 100 treatments. The performance of this algorithm is assessed against 20 2‐replicate resolvable RCDs of Patterson & Robinson (1989) and 17 resolvable RCDs based on generalized cyclic designs (GCDs) of Ipinyomi & John (1985). The use of the algorithm to construct RCDs with contiguous replicates is discussed.
ESTIMATING A LOGIT MODEL WITH RANDOMIZED DATA: THE CASE OF COCAINE USE
Authors: KERKVLIET JOE
Published: February 2008

In his influential book, Maddala (1983) suggests combining randomized response survey data with other personal information to estimate logit models predicting immoral, unpopular, or unlawful behaviour. This study is one of the first to implement this technique using real data. Models of college students' recent cocaine use are estimated with academic performance and socio‐economic characteristics as determinants. Parameter estimates obtained from randomized response surveys are compared to those obtained using conventional, direct question surveys. The results indicate that randomized response estimates provide useful information on the degree to which inferences regarding the determinants of cocaine use are sensitive to survey type.
ASYMPTOTIC PROPERTIES OF RESTRICTED MAXIMUM LIKELIHOOD (REML) ESTIMATES FOR HIERARCHICAL MIXED LINEAR MODELS
Authors: RICHARDSON A.M. , WELSH A.H.
Published: February 2008

This paper explores the asymptotic distribution of the restricted maximum likelihood estimator of the variance components in a general mixed model. Restricting attention to hierarchical models, central limit theorems are obtained using elementary arguments with only mild conditions on the covariates in the fixed part of the model and without having to assume that the data are either normally or spherically symmetrically distributed. Further, the REML and maximum likelihood estimators are shown to be asymptotically equivalent in this general framework, and the asymptotic distribution of the weighted least squares estimator (based on the REML estimator) of the fixed effect parameters is derived.
SHOULD WE ESTIMATE THE PROBABILITY OF CORRECT SELECTION?
Authors: BOFINGER EVE
Published: February 2008

Various authors, given k location parameters, have considered lower confidence bounds on (standardized) dserences between the largest and each of the other k ‐ 1 parameters. They have then used these bounds to put lower confidence bounds on the probability of correct selection (PCS) in the same experiment (as was used for finding the lower bounds on differences). It is pointed out that this is an inappropriate inference procedure. Moreover, if the PCS refers to some later experiment it is shown that if a non‐trivial confidence bound is possible then it is already possible to conclude, with greater confidence, that correct selection has occurred in the first experiment. The short answer to the question in the title is therefore ‘No’, but this should be qualified in the case of a Bayesian analysis.
ASYMPTOTIC EXPANSIONS APPLIED TO F‐TEST CRITERIA
Authors: GOVINDARAJULU Z. , HOWARD H.C.
Published: February 2008

Using asymptotic expansions of the Kummer hypergeometric function, the sequential. F‐test criterion is evaluated asymptotically in terms of the sample size. The continuation region inequalities for the test are inverted and expressed in terms of known test criteria. A rapidly converging algorithm for carrying out the sequential procedure is provided. This makes the F‐test easier for the practitioner to use. Almost sure finite termination of the sequential. F‐test is asserted by appealing to the continuation inequalities and a heuristic asymptotic expansion of the test criterion. Average stopping times of the sequential procedure for a variety of population means and population number configurations are tabulated. The computer symbolic manipulation program MAPLE was used to derive some formulae.
A‐OPTIMAL RUN ORDERS WITH A LINEAR TREND
Authors: MUKERJEE RAHUL , SENGUPTA S.
Published: February 2008

A‐optimal ran orders are considered in the presence of a linear trend with emphasis on non‐orthogonal situations where no trend‐free run order can be. A‐optimal. Some possibilities for further extension are also briefly indicated.
A STOCHASTIC MODEL FOR TEAM GOLF COMPETITIONS WITH APPLICATIONS TO HANDICAPPING
Authors: Tallis G.M.
Published: February 2008

This paper examines the current handicapping systems for team golf competitions. Ambrose, foursomes, Canadian foursomes and Pinehurst events are analysed. Statistical models are built to capture the stochastic features of these events, and to allow new handicapping systems to be developed. Some substantial discrepancies between the new and the old systems are noted.
THE RELATIONSHIP BETWEEN QUASI‐SCORE AND LOCALLY E‐SUFFICIENT ESTIMATING FUNCTIONS
Authors: Lin Yan‐Xia
Published: February 2008

The relationship between quasi‐score estimating functions and locally E‐sufficient estimating functions is discussed. Sufficient conditions for a quasi‐score estimating function to be locally E‐sufficient are given.
ON CONFIDENCE REGIONS OF EMBEDDED MODELS IN REGULAR PARAMETRIC FAMILIES (A GEOMETRIC APPROACH)
Authors: Wei Bo‐Cheng
Published: February 2008

Regular parametric families are commonly encountered in statistical problems (e.g. Cox & Hinkley, 1974). In this paper, we propose a differential geometric framework for the embedded models in these families. Our framework may be regarded as an extension of that presented by Bates & Watts (1980) for nonlinear regression models. As an application, we use this geometric framework to derive three kinds of improved approximate confidence regions for the parameter and parameter subsets in terms of curvatures. The results obtained by Hamiltonet al.(1982) and Hamilton (1986) are extended to embedded models in regular parametric families.
PROBABILITY APPROXIMATIONS FOR DIVISIBLE DISCRETE DISTRIBUTIONS
Authors: Quine M.P.
Published: February 2008

If an integer‐valued random variable can be represented as a sum of independent random variables, then powerful tools exist to derive approximations to its distribution. We apply this idea to examples in some of which it is not clear how to give a physical interpretation to the independent sum‐mands. We consider bounds on the accuracy of single term approximations, Edgeworth expansions and saddlepoint approximations for both individual probabilities and cumulative probabilities.
LOCATION OF THE OPTIMAL SAMPLING POINT FOR THE QUALITY ASSESSMENT OF CONTINUOUS STREAMS
Authors: Barnett Neil S. , Gomm Ian S. , Armour Len
Published: February 2008

The paper makes an appraisal of the most appropriate sampling point for situations where a single sample must be used to estimate the mean flow of a continuous stream during a set time interval. Taking ‘optimal’ to mean the point at which the estimation error variance is minimised, optimal sampling locations are obtained for constant, linear and exponential flow rates when the process variogram is assumed linear or exponential. Numerical results illustrate the significance of failing to sample at the optimal point.
SMOOTH TESTS FOR THE BIVARIATE POISSON DISTRIBUTION
Authors: Rayner J.C.W. , Best D.J.
Published: February 2008

A theorem of Rayner & Best (1989) is generalised to permit the construction of smooth tests of goodness of fit without requiring a set of orthonormal functions on the hypothesised distribution. This result is used to construct smooth tests for the bivariate Poisson distribution. The test due to Crockett (1979) is similar to a smooth test that assesses the variance structure under the bivariate Poisson model; the test due to Loukas & Kemp (1986) is related to a smooth test that seeks to detect a particular linear relationship between the variances and covariance under the bivariate Poisson model. Using focused smooth tests may be more informative than using previously suggested tests. The distribution of the Loukas & Kemp (1986) statistic is not well approximated by the x2distribution for larger correlations, and a revised statistic is suggested.
ROBUST VARIABLE SELECTION IN REGRESSION IN THE PRESENCE OF OUTLIERS AND LEVERAGE POINTS
Authors: Sommer Suzanne , Staudte Robert G.
Published: February 2008

In linear regression, outliers and leverage points often have large influence in the model selection process. Such cases are downweighted with Mallows‐type weights here, during estimation of submodel parameters by generalised M‐estimation. A robust version of Mallows's Cp (Ronchetti &. Staudte, 1994) is then used to select a variety of submodels which are as informative as the full model. The methodology is illustrated on a new dataset concerning the agglomeration of alumina in Bayer precipitation.
SOME PROPERTIES OF A FOLDOVER DESIGN
Authors: Diamond Neil T.
Published: February 2008

The foldover design of the 12‐run Plackett‐Burman design is shown to give a 24and 24‐1design in every set of four factors, and to be resolutionVin every set of five factors. In addition, the design allows the search and estimation of up to two non‐zero interactions.
THE OPTIMAL CRITICAL VALUE OF A PRE‐TEST FOR AN INEQUALITY RESTRICTION IN A MIS‐SPECIFIED REGRESSION MODEL
Authors: Wan Alan T.K.
Published: February 2008

Optimal critical values are derived for a pre‐test of an inequality restriction in a model where relevant regressors are unwittingly omitted. The criterion adopted is either that of the minimum average relative risk, or that of the mini‐max regret. The latter approach yields an optimal critical value which is sensitive to the degree of model mis‐specification, while the former criterion always leads to the choice of the unrestricted estimator.
QUALITY ISSUES IN TELEPHONE SURVEYS: COVERAGE, NON‐RESPONSE and QUOTA SAMPLING
Authors: Steel David , Vella Joe , Harrington Peter
Published: February 2008

The quality of a telephone survey is affected by several factors: telephone coverage, non‐response, the methods used to select households and persons, and the quality of responses obtained from respondents. Data are provided which show that a large proportion of Australian households have telephone connections. However, telephone coverage is not uniform and some subgroups of the population have much lower connection rates. This paper reviews evidence of the effect of non‐response and the effectiveness of repeated call backs, and reports the results of a new study. The use of quota sampling to select respondents from randomly selected households is also examined. The results suggest that telephone surveys under‐represent older persons and the unemployed, and over‐represent middle‐aged persons. It is shown that while call backs can increase the response rate, the effect on the composition of the sample and resulting estimates is minimal. The main effects are due to refusals and variation in coverage rates.
CHANCE OF MISCLASSIFICATION IN THE CASE OF SEVERAL NORMAL POPULATIONS
Authors: Paek Pil S. , Kshirsagar AnantM.
Published: February 2008

When a new observation is to be classified into one of several multivariate normal populations with different means and the same covariance matrix, by Rao's method of scoring, the chance of misclassification is expressed as a multiple integral. This paper gives a practical method of obtaining reasonable approximations to this integral by using tables prepared by Gibbons, Olkin & Sobel (1977) for a different task.