bluuebunny/crossref_metadata_2025_split huggingface dataset:
ON TESTS FOR INTERACTION IN A NONREPLICATED TWO‐WAY LAYOUT
Authors: Piepho Hans‐Peter 
Published: February 2008

SummaryThe paper proposes tests for interaction in a two‐way table with one observation per cell. The power of these tests is independent of the additive main effects in the linear model. This is an advantage compared to a test suggested earlier, which has low power if main effects are very variable.
ESTIMATING TEMPORAL FARM INCOME DISTRIBUTIONS USING SPATIAL SMOOTHING TECHNIQUES
Authors: Kokic Philip N. , Moon Lynelle , Gooday Jane , Chambers Ray L. 
Published: February 2008

SummaryThe Australian Bureau of Agricultural & Resource Economics (ABARE) has conducted the annual Australian Agricultural and Grazing Industries Survey, covering the broadacre sector, in a consistent format since 1978/79. In this period the incomes of farmers have fluctuated considerably. Interest has focused on temporal income variation because of its relationship to the inherent riskiness of farming. Part of these income fluctuations can be explained by changing commodity prices and by highly variable climatic conditions. This paper outlines a method of measuring the magnitude of this income variation and how it relates to other variables. Due to rotation within the sample of farms surveyed, data are limited for determining the income variability of any particular farm. This paper, using spatial locations of surveyed farms, shows how kernel smoothing techniques can be adapted to estimate the distribution of a farm's income. This analysis uses data collected from 1978/79 to 1991/92. Summary information from these distributions is cross‐tabulated against several other variables. This shows that income variability is strongly related to the production mixture of farming businesses.
ON NON‐NEGATIVE UNBIASED ESTIMATION OF QUADRATIC FORMS IN FINITE POPULATION SAMPLING
Authors: Vijayan K. , Mukhopadhyay P. , Bhattacharyya S. 
Published: February 2008

SummaryThe paper investigates non‐negative quadratic unbiased (NnQU) estimators of positive semi‐definite quadratic forms, for use during the survey sampling of finite population values. It examines several different NnQU estimators of the variance of estimators of population total, under various sampling designs. It identifies an optimal quadratic unbiased estimator of the variance of the Horvitz‐Thompson estimator of population total.
ESTIMATING THE ANGLE BETWEEN THE MEAN DIRECTIONS OF TWO SPHERICAL DISTRIBUTIONS
Authors: Lewis Toby , Fisher Nicholas I. 
Published: February 2008

SummaryThis paper considers the problem of calculating a confidence interval for the angular difference between the mean directions of two spherical random variables with rotationally symmetric unimodal distributions. For large sample sizes, it is shown that the asymptotic distribution of 1 – cos α, where α is the sample angular difference, is approximately exponential if the true difference is zero, and approximately normal for a ‘large’ true difference; a scaled beta approximation is determined for the general case. For small sample sizes, a bootstrap approach is recommended. The results are applied to two sets of palaeomagnetic data.
TWO‐MOMENT SCALING FORMULAE FOR AGGREGATING EXAMINATION MARKS
Authors: Daley D.J. 
Published: February 2008

SummaryThree different methods are presented for deriving linear transformations of raw examination marks in order to construct ostensibly fair’ average or aggregate marks for students taking possibly different subsets of subjects from the curriculum. All methods are based on the same linear model, which in the case of a balanced dataset (that is, if all students have marks in all subjects) is closely related to a simple principal component representation.
A ONE‐SIDED TRIMMED MEAN ESTIMATOR FOR THE MEAN OF POSITIVELY SKEWED DISTRIBUTIONS
Authors: Chen Ling 
Published: February 2008

SummaryThis article proposes estimating the mean of positively skewed distributions by a one‐sided trimmed mean estimator, called the upper‐mean, and uses it to assess soil contact exposures at sites with toxic contamination. An optimal upper‐mean is found by maximising the probability of the estimator falling in a target range. Monte Carlo studies are conducted for several positively skewed distributions and for a distribution obtained from real data. The simulation results show that the upper‐mean is a better estimator than the upper 95% confidence limit estimator currently used, because it is more probable that the estimator is covered by the target range.
SOME RESAMPLING PROCEDURES UNDER SYMMETRY
Authors: Jing BinG‐Yi 
Published: February 2008

SummaryThis paper investigates the properties of bootstrap and related methods assuming that the underlying distribution is symmetric but otherwise unknown. In particular it studies the percentile‐t, nonparametric tilting and empirical likelihood and finds that the performance of percentile‐t and non‐parametric tilting methods can be improved by incorporating the symmetry into the resampling procedure. However, for symmetric empirical likelihood, the Bartlett correctability no longer holds, although use of bootstrap calibration restores the good coverage properties typically associated with Bartlett correction. This surprising result shows that Bartlett correctability is a very delicate property.
AN ALGORITHM FOR THE DESIGN OF 2? FACTORIAL EXPERIMENTS ON CONTINUOUS PROCESSES
Authors: Saunders I.W. , Eccleston J.A. , Martin R.J. 
Published: February 2008

SummarySaunders & Eccleston (1992) presented an approach to the design of 2‐level factorial experiments for continuous processes. It determined sets of contrasts between the observations that could be well estimated, and then selected a design so that those contrasts estimated the parameters of interest. This paper shows that a well‐estimated contrast must have a large number of changes of sign or level, and also be ‘paired’ in a particular sense. It develops an algorithm which constructs designs that must have a large number of changes of sign, evenly spread among the contrasts and optimal or near optimal. When such designs exist they are often preferable to those produced by the reverse foldover algorithm of Cheng & Steinberg (1991).
A STATISTICAL MODEL FOR DUPLICATE TOURNAMENTS IN GAMES OF LUCK AND SKILL
Authors: Yu Philip L.H. , Cowan Richard 
Published: February 2008

SummaryMany games played between two contestants start with a random deal, which introduces luck from the deal. The concept of ‘duplication’, commonly employed in Bridge tournaments, can be used to nullify that luck in a tournament. This paper poses a model for the contest between any two players and analyses the model for a tournament which employs the duplication principle. Two example tournaments on a new game are analysed.
THE IMPACT OF METHOD CHOICE ON META‐ANALYSIS
Authors: Mengersen K.L. , Tweedie R.L. , Biggerstaff B. 
Published: February 2008

SummaryThis paper focusses mainly on three crucial choices identified in recent meta‐analyses, namely (a) the effect of using approximate statistical techniques rather than exact methods, (fa) the effect of using fixed or random effect models, and (c) the effect of publication bias on the meta‐analysis result. The paper considers their impact on a set of over thirty studies of passive smoking and lung cancer in non‐smokers, and addresses other issues such as the role of study comparability, the choice of raw or adjusted data when using published summary statistics, and the effect of biases such as misclassification of subjects and study quality. The paper concludes that, at least in this example, different conclusions might be drawn from metaanalyses based on fixed or random effect models; that exact methods might increase estimated confidence interval widths by 5–20% over standard approximate (logit and Mantel‐Haenszel) methods, and that these methods themselves differ by this order of magnitude; that taking study quality into account changes some results, and also improves homogeneity; that the use of unadjusted or author‐adjusted data makes limited difference; that there appears to be obvious publication bias favouring observed raised relative risks; and that the choice of studies for inclusion is the single most critical choice made by the modeller.
MISSING VALUES, IMPUTATION AND ERROR RATE ESTIMATORS IN DISCRIMINANT ANALYSIS
Authors: Bello A.L. 
Published: February 2008

SummaryError rate is a popular criterion for assessing the performance of an allocation rule in discriminant analysis. Training samples which involve missing values cause problems for those error rate estimators that require all variables to be observed at all data points. This paper explores imputation algorithms, their effects on, and problems of implementing them with, eight commonly used error rate estimators (three parametric and five non‐parametric) in linear discriminant analysis. The results indicate that imputation should not be based on the way error rate estimators are calculated, and that imputed values may underestimate error rates.
A NOTE ON ARMA PARAMETERISATION*
Authors: Hannan E.J. , Kavalieris L. 
Published: February 2008

SummaryThis paper considers the relationship between ARMA parameterisations of models fory(t)andAy(t), whereAis invertible andy(t)is a vector time series (t = 0,±1,…). An ARMA model for the transformed seriesAy(t)may have fewer parameters than a model fory(t).This paper shows that such a saving is illusory because the apparently saved parameters are exactly balanced by the number of new parameters appearing inA.
TESTS FOR SEASONAL DIFFERENCING WITH AN UNKNOWN BREAK‐POINT
Authors: Ng T.M. , Li W.K. 
Published: February 2008

SummarySome Lagrange multiplier tests for seasonal differencing are proposed; their main objective is to avoid over‐differencing due to structural change. The null hypothesis is either the presence of both regular and seasonal unit roots or the presence of a seasonal unit root. Alternative hypotheses allow for stationarity around a possible structural change where the break‐point is unknown. The location of the structural change is estimated using the proposed procedures, the asymptotic distribution of the test statistics under the null hypothesis is derived and some useful percentiles are tabulated. An illustrative example based on the Canadian Consumer Price Index is presented.
DISTRIBUTION‐FREE CONFIDENCE PROCEDURE FOR UMBRELLA ORDERINGS
Authors: Pan Guohua 
Published: February 2008

SummaryResponses in a one‐factor experiment with A; ordered treatments follow an umbrella, ordering if they consist of two piecewise monotone segments, i.e. increasing and then decreasing, or the converse. This paper proposes a non‐parametric distribution‐free confidence procedure for umbrella orderings, the aim being to identify the treatments that correspond to the optimal effects. It uses a method that joins the seemingly unrelated theories of U‐statistics and isotonic regression. A random confidence subset of the ordered treatments is constructed, such that it contains all the unknown peaks (optimal treatments) of an umbrella ordering with any prespecified confidence level. The paper demonstrates that the proposed confidence procedure is nonparametric distribution‐free and, further, that the proposed procedure naturally implies a test for umbrella alternatives. Since the proposed confidence procedure is always more informative than tests for umbrella alternatives, it should be used in their place in practice. An example illustrates the proposed procedure.
INDEPENDENCE DISTRIBUTION‐PRESERVING NONNEGATIVE‐DEFINITE COVARIANCE STRUCTURES FOR THE SAMPLE VARIANCE
Authors: Young Dean M. , Lehman Leah M. , Meaux Laurie M. 
Published: February 2008

SummaryThis paper explicitly characterizes the general nonnegative‐definite covariance structure for a multivariate normal random vector such that the univariate sample variance is distributed exactly as if the sample observations are normal independent and identically distributed (i.i.d.). This work extends the results of Baldessari (1965) and Stadje (1984) who have characterized the general positive‐definite covariance matrix such that the univariate sample variance is distributed exactly as if the sample observations are normal i.i.d.
REGRESSION ANALYSIS USING SCRAMBLED RESPONSES
Authors: Singh Sarjinder , Joarder A.H. , King Maxwell L. 
Published: February 2008

SummaryThis paper investigates the general linear regression modelY = Xβ+eassuming the dependent variable is observed as a scrambled response using Eichhorn & Hayre's (1983) approach to collecting sensitive personal information. The estimates of the parameters in the model remain unbiased, but the variances of the estimates increase due to scrambling. The Wald test of the null hypothesis H0:β=β0, against the alternative hypothesis Ha:β#β0, is also investigated. Parameter estimates obtained from scrambled responses are compared to those from conventional or direct‐question surveys, using simulation. The coverage by nominal 95% confidence intervals is also reported.
MULTI‐FACTOR BALANCED BLOCK DESIGNS WITH COMPLETE ADJUSTED ORTHOGONALITY FOR ALL PAIRS OF TREATMENT FACTORS
Authors: Preece D.A. 
Published: February 2008

SummaryA new series of multi‐factor balanced block designs is introduced. Each of these designs has the following properties: (i) each of its k– 1 treatment factors is disposed in a cyclic or multi‐cyclic balanced incomplete block design with parameters (v,b,r,k,Λ) = (a(k‐l) + 1,a2(k‐1) +a, ak, k, k) (a > 1); (ii) the incidence of any one of the treatment factors on any other is balanced; and (iii) after adjustment for blocks only, the relationship between any two of the treatment factors is that of adjusted orthogonality. The treatment factors are thus orthogonal to one another in the within‐blocks stratum of the analysis of variance. The designs provide a benchmark with which other designs may be compared.
DISPERSION CONTROL FOR MULTIVARIATE PROCESSES – SOME COMPARISONS
Authors: Tang Pak F. , Barnett Neil S. 
Published: February 2008

SummaryTang & Barnett (1996) present some techniques for monitoring and controlling the dispersion of multivariate normal processes based on subgroup data. The current paper compares the proposed techniques and various competing procedures. Simulation results indicate that the proposed techniques are superior to existing procedures.
ESTIMATION OF QUANTILES FOR A FRÉCHET‐TYPE DISTRIBUTION
Authors: Hasofer A.M. , Wang J.Z. 
Published: February 2008

SummaryEstimation of high quantiles of a distribution in the domain of attraction of the Fréchet distribution is based on the extremal distribution of theklargest order statistics. The problem is treated by a local maximum likelihood method on a three parameter model. The estimators are shown to be asymptotically consistent for the whole range of the tail index parameter.
IMPROVED INFERENCE FROM RECAPTURE EXPERIMENTS WITH BEHAVIOURAL RESPONSE THROUGH MODELLING and AUXILIARY EXPERIMENTATION
Authors: Chaiyapong Y. , Lloyd C.J. 
Published: February 2008

SummaryAllowing for behavioural response in a recapture experiment involves a large reduction in the precision of estimating the unknown population size. Unless the number of individuals captured is very large, the model is of little use in practice. This paper studies the extent to which this efficiency loss can be reduced by modelling the behavioural response. The most dramatic improvements in efficiency are obtained by estimating the rate of the behavioural response from an intensive study of a small subset of the population. In many practical situations this may be a cost‐ and time‐effective alternative to intensively sampling the entire population.
MODEL‐BASED VARIANCE ESTIMATION IN SURVEYS WITH STRATIFIED CLUSTERED DESIGN
Authors: Longford N.T. 
Published: February 2008

SummaryA model‐based method for estimating the sampling variances of estimators of (sub‐)population means, proportions, quantiles, and regression parameters in surveys with stratified clustered design is described and applied to a survey of US secondary education. The method is compared with the jackknife by a simulation study. The model‐based estimators of the sampling variances have much smaller mean squared errors than their jackknife counterparts. In addition, they can be improved by incorporating information about the unknown parameters (variances) from external sources. A regression‐based smoothing method for estimating the sampling variances of the estimators for a large number of subpopulation means is proposed. Such smoothing may be invaluable when subpopulations are represented in the sample by only few subjects.
ESTIMATION FOR THE GENERAL SAMPLE SELECTION MODELS
Authors: Wang You‐Gan , Yin Ming 
Published: February 2008

summaryConsider a general regression model with an arbitrary and unknown link function and a stochastic selection variable that determines whether the outcome variable is observable or missing. The paper proposes U‐statistics that are based on kernel functions as estimators for the directions of the parameter vectors in the link function and the selection equation, and shows that these estimators are consistent and asymptotically normal.
TWO‐SAMPLE NONPARAMETRIC TILTING METHOD
Authors: Jing Bing‐Yi , Robinson John 
Published: February 2008

summaryEfron introduced the one‐sample nonparametric tilting method for constructing confidence intervals. The method enjoys some good features such as being range‐respecting and having internal studentizing and good coverage accuracy. This paper presents a two‐sample version of the one‐sample non‐parametric tilting method for dealing with two independent sample problems, whereby each of the two individual samples is ‘tilted’ separately by different amounts. The paper shows that careful choice of the two tilting parameters produces second‐order accurate confidence limits. Further, saddlepoint approximations can be used to avoid extensive Monte Carlo simulations, and it turns out that the approximations are exceptionally easy to apply with the tilting parameters. Numerical examples illustrate the two‐sample nonparametric tilting method.
FOURIER SERIES ESTIMATION FOR LENGTH BIASED DATA
Authors: Jones M.C. , Karunamuni R.J. 
Published: February 2008

summaryThis paper proposes and investigates Fourier series estimators for length biased data. Specifically, two Fourier series estimators are constructed and studied based on ideas of Jones (1991) and Bhattacharyya et al. (1988) in the case of kernel density estimation. Approximate expressions for mean squared errors and integrated mean squared errors are obtained and compared, and some simulated examples are investigated. The Fourier series estimator based on the proposal of Jones seems to have the more desirable properties of the two. The paper concludes with some comments that put this work in a wider context.
ON CHARACTERIZING DISTRIBUTIONS VIA LINEARITY OF REGRESSION FOR ORDER STATISTICS
Authors: Wesolowski Jacek , Ahsanullah Mohammad 
Published: February 2008

summaryLet X1Xn be a random sample from an absolutely continuous distribution with the corresponding order statistics X1:n≤X2:n≤Xn:n. A complete solution of the problem, posed in 1967 by T. Ferguson, of determining the distribution by linearity of regression of Xk+2:n with respect to Xk:n is given. The only possible distributions are of the exponential, power and Pareto type. A linear regression relation for exponents of order statistics is also considered.
ON A MULTIPLE THREE‐DECISION PROCEDURE FOR COMPARING SEVERAL TREATMENTS WITH A CONTROL
Authors: Liu W. 
Published: February 2008

summaryFor the two‐sided comparisons of several treatments with a control, a common statistical problem is to decide which treatments are better than the control and which are worse than the control. This paper studies a multiple three‐decision procedure for this purpose, proposed by Bohrer (1979) and Bohrer et al. (1981), and provides tables of critical points to facilitate the application of the procedure. The paper defines a power function of the procedure, and tabulates sample sizes necessary to guarantee a given power level. It addresses the problem of optimal sampling allocation in order to maximize the power for a given total sample size, and considers generalization to the situation where the treatments might have unequal numbers of observations.
ESTIMATING EQUATIONS AND NON‐LINEAR FUNCTIONAL RELATIONSHIPS
Authors: Morton Richard , Patefield Mike , Bowtell Phil 
Published: February 2008

summaryA nonlinear functional relationship is defined as anR‐dimensional manifold inP‐dimensional space. The formulation of the model may be explicitly in terms ofR‐dimensional vectors of incidental parameters or implicitly by a (P‐R)‐dimensional vector function of constraints. The objective is to estimate and make inference about aK‐vector of parameters θ which defines the manifold. Each observedP‐vector has its expectation lying on the manifold, and the error vector has a variance matrix defined in terms of a further vector of parametersThe theory of estimating equations in the presence of incidental parameters is extended and applied to the explicit formulation, to give equations suitable for estimating θ given knowledge of only the first two moments. The method has a geometrical interpretation. Estimating equations for are chosen to be those which would be optimal if the normality assumption were true. First order corrections to the biases of these estimates are included.An example where the manifold is a circle centred on the origin is used to illustrate the theory. Further examples incorporate more general features, including the estimation of two variance parameters and estimation in higher dimensions.
LOWER BOUNDS FOR THE DIVERGENCE OF DIRECTIONAL AND AXIAL ESTIMATORS
Authors: Kakarala Ramakrishna , Watson Geoffrey S. 
Published: February 2008

SummaryConsider estimation of a unit vector parameter a in two classes of distributions. In the first, α is a direction. In the second, α is an axis, so that –α and α are equivalent: the aim is to obtain the projector ααt. In each case the paper uses first principles to define measures of the divergence of such estimators and derives lower bounds for them. These bounds are computed explicitly for the Fisher‐Von Mises and Scheidegger‐Watson densities on the g‐dimensional sphere, ωq. In the latter case, the tightness of the bound is established by simulations.
A NOTE ON MULTIVARIATE LOGISTIC MODELS FOR CONTINGENCY TABLES
Authors: Kauermann Goran 
Published: February 2008

SummaryThe log‐linear model is a tool widely accepted for modelling discrete data given in a contingency table. Although its parameters reflect the interaction structure in the joint distribution of all variables, it does not give information about structures appearing in the margins of the table. This is in contrast to multivariate logistic parameters, recently introduced by Glonek & McCullagh (1995), which have as parameters the highest order log odds ratios derived from the joint table and from each marginal table. Glonek & McCullagh give the link between the cell probabilities and the multivariate logistic parameters, in an algebraic fashion. The present paper focuses on this link, showing that it is derived by general parameter transformations in exponential families. In particular, the connection between the natural, the expectation and the mixed parameterization in exponential families (Barndorff‐Nielsen, 1978) is used; this also yields the derivatives of the likelihood equation and shows properties of the Fisher matrix. The paper emphasises the analysis of independence hypotheses in margins of a contingency table.