bluuebunny/crossref_metadata_2025_split huggingface dataset:
Linear Rank Statistics for the Simple Tree Alternatives
Authors: Desu M. M. , Park S. , Chakraborti S. 
Published: May 2007

AbstractThe problem of comparing several treatments with a control is considered. It is formulated as a test of homogeneity with one‐sided alternatives that at least one treatment is better than the control, while no treatment is worse than the control. A class of linear rank tests for the simple tree alternatives is proposed, assuming a location model. The most efficient test of the proposed class for detecting a specified pattern is derived. Two optimal tests for use in the case of vaguely specified tree alternatives are derived. In a simulation study the power of these two tests are compared with the power of Kruskal‐Wallis test. Recommendations about the use of the proposed tests are made.
On the Power Behaviour of Läuter's Exact Multivariate One‐sided Tests
Authors: Frick H. 
Published: May 2007

AbstractRecently Läuter [1996] developed a new class of parametric tests for the one‐sided multivariate hypothesis with unknown covariances. Unlike most procedures for this problem, Läuter's tests are exact, i.e. they strictly maintain the nominal level. In this paper two scale invariant Läuter tests are considered, the standardized sum and the principal component test. Here conditions are investigated under which the powers of these tests are insufficient for arbitrarily large alternatives and, for the second test, also for unbounded sample sizes. It turns out that power insufficiencies can happen only at the boundary of the set of alternatives so that for alternatives usually occurring in biometrical practice both tests prove to be efficient.
A Multivariate Procedure for Comparing Mean Vectors for Populations with Unequal Regression Coefficient and Residual Covariance Matrices
Authors: Misra R. K. 
Published: May 2007

AbstractIn fisheries research there is a need to compare vectors of means of continuous random response variables adjusted for concomitant variations of covariables for populations that have unequal regression coefficient and residual covariance matrices. A multivariate procedure that provides an extended comparison of vectors of adjusted means is presented. An example is presented using a real data set. The procedure is quite general and applicable to many other fields of research.
Estimation of a Generalized Linear Mixed‐Effects Model with a Finite‐Support Random‐Effects Distribution via Gibbs Sampling
Authors: Leung Moon K. , Elashoff Robert M. 
Published: May 2007

AbstractWe discuss a Bayesian hierarchical generalized linear mixed‐effects model with a finite‐support random‐effects distribution and show how Gibbs sampling can be used for estimating the posterior distribution of the parameters and for clustering on the basis of longitudinal data. When directly sampling from the conditional distributions is laborious, the adaptive rejection sampling (ARS, Gilks and Wild, 1992; Gilks, 1992) algorithm or adaptive rejection Metropolis sampling (ARMS, Gilks et al., 1995) algorithm is used. Log‐concavity, a prerequisite of ARS, of the conditional distributions is examined. We also discuss a Bayesian solution to the uncertainty of the support size of the random‐effects distribution in statistical inference. The methodology is illustrated with an analysis of data from a study of regulation of serum parathyroid hormone secretion.
Dynamic Trends in Distributions
Authors: Voit Eberhard O. 
Published: May 2007

AbstractTransformations of distributions belong to the standard repertoire of theoretical statistics, if they are given as explicit, algebraic functions. In practical applications, transformations are often not given explicitly, however, but as processes that are best described as solutions of ordinary differential equations. The paper describes how trends in distributions can be computed under such conditions. It begins with a description of the method, illustrates it with didactic examples, and applies the method to the accumulation of mercury in king mackerel (Scomberomorus cavalla). A brief discussion alludes to limitations and to other potential application areas.
Detection of Cyclic Trends in Incidence Data Using the Efficiency Score Method
Authors: Nam Jun‐Mo 
Published: May 2007

AbstractIn studies involving a cyclic regularity, researchers usually have a good working knowledge regarding the peak time in the cycle. Capitalizing on this information, we derive the asymptotically uniformly most powerful unbiased test for detecting a cyclic trend using the likelihood score, and present the asymptotic power function of the test and the approximate formula for sample size. Numerical studies demonstrate great advantages of the proposed test over the standard test in terms of power and sample size. Asymptotic power of the score test is satisfactorily close to actual power. We also generalize this method so that it is applicable for incidence data from unequally spaced intervals or risk populations of unequal size.
Predictors for Seasonal and Nonseasonal Fractionally Integrated ARIMA Models
Authors: Peiris M. Shelton , Singh N. 
Published: May 2007

AbstractThis paper derives easy‐to‐calculate preditors for seasonal and nonseasonal fractionally integrated autoregressive‐moving average (ARIMA (p, d, q) × (P, D, Q)s) models with both differencing parametersdandDassuming values on the real line. It is shown that these predictors are optimum. Special attention is given to the one‐step‐ahead predictors as they are constantly in demand in almost every practical situation.
Estimating the Weighting Components of a Health Quality Index
Authors: Jeong Jinook , Mauldin Patrick D. 
Published: May 2007

AbstractThis paper introduces a maximum likelihood estimation model that uses patients' pretherapy characteristics to determine the weighting components of multipe outcomes of quality health. The resulting health quality index' can then be included in cost‐effectiveness analyses to compare alternative treatment plans. A Monte Carlo simulation is performed to test the model's robustness and power.
Testimation in Regression Parameter Estimation
Authors: Rahman Mezbahur , Gokhale D. V. 
Published: May 2007

AbstractTestimation is considered in the problem of estimation of regression parameters. The first stage sample is used to test a (null) hypothesis that specifies initial (preassumed) values for some of the regression parameters. Linear combination of the preassumed values and the ordinary least square (OLS) estimates is considered as the estimate if the data agree with the hypothesis. Otherwise, a second sample is taken and parameters are estimated only by using OLS, based on the combined sample. The procedure protects against type II error and against taking larger samples when inference can be made from a smaller sample.
Predictive Performance of the Methods of Restricted and Mixed Regression Estimators
Authors: Toutenburg H. , Shalabh  
Published: May 2007

AbstractThis article considers the problem of simultaneous prediction of actual and average values of the study variable in a linear regression model when a set of linear restrictions binding the regression coefficients is available, and analyzes the performance properties of predictors arising from the methods of restricted regression and mixed regression besides least squares.
The Residual Likelihood Ratio Test for the Variance Ratio in a Linear Model with Two Variance Components
Authors: Li Yulan , Birkes David , Thomas David R. 
Published: May 2007

AbstractResidual maximum likelihood has proved to be a successful approach to the estimation of variance components. In this paper, its counterpart in testing, the residual likelihood ratio test, is applied to testing the ratio of two variance components. The test is compared with the Wald test and the locally most powerful invariant test.
Biased Estimation of Adjusted Odds Ratios from Incomplete Covariate Data Due to Violation of the Missing at Random Assumption
Authors: Vach Werner , Illi Sabina 
Published: May 2007

AbstractWe investigate the possible bias due to an erroneous missing at random assumption if adjusted odds ratios are estimated from incomplete covariate data using the maximum likelihood principle. A relation between complete case estimates and maximum likelihood estimates allows us to identify situations where the bias vanishes. Numerical computations demonstrate that the bias is most serious if the degree of the violation of the missing at random assumption depends on the value of the outcome variable or of the observed covariate. Implications for the analysis of prospective and retrospective studies are given.
Bayesian Testing of Relative Mortality, with Application to Occupational Cohort Studies
Authors: Giudici Paolo , Crosignani Paolo 
Published: May 2007

AbstractAn important issue in epidemiology, often exploratory, is to compare the mortality of a certain cohort, exposed to some dangerous conditions, to that of a reference population. The main statistical tool to study relative mortality has long been the subject‐years method.We propose a Bayesian version of the subject‐years method, particularly suited to analyse the mortality of small cohorts. In particular, we employ the Bayes factor in order to test whether the mortality of a cohort exceeds that of a reference population. The methodology is applied to an occupational epidemiology study. The results are compared to those obtained from the classical one‐sided tests on the same data.
A General Framework for Longitudinal Data through Marked Point Processes
Authors: Scheike Thomas H. 
Published: May 2007

AbstractThis paper reviews a general framework for the modelling of longitudinal data with random measurement times based on marked point processes and presents a worked example. We construct a quite general regression models for longitudinal data, which may in particular include censoring that only depend on the past and outside random variation, and dependencies between measurement times and measurements. The modelling also generalises statistical counting process models. We review a non‐parametric Nadarya‐Watson kernel estimator of the regression function, and a parametric analysis that is based on a conditional least squares (CLS) criterion. The parametric analysis presented, is a conditional version of the generalised estimation equations of LIANG and ZEGER (1986). We conclude that the usual nonparametric and parametric regression modelling can be applied to this general set‐up, with some modifications. The presented framework provides an easily implemented and powerful tool for model building for repeated measurements.
Measurement of Diversity and Dissimilarity for Stochastic Populations
Authors: Adke S. R. , Ratnaparkhi M. V. 
Published: May 2007

AbstractThe existing measures of diversity within a population and of dissimilarity between two populations postulate a static population with unchanging probabilistic structure. We assume that the measurements associated with an individual change with time according to a stochastic process. General measures of diversity and of dissimilarity when the underlying process is a finite Markov chain are developed. An aportionment of these measures based on a partition of the state‐space is provided. Their asymptotic behaviour is investigated. The spectral analysis of measures of diversity is carried out. Some specific measures of diversity and dissimilarity are obtained when the underlying stochastic process is a general vector‐valued process. An indication of directions for future work is provided.
On the Efficiency of Nearest Neighbour Balanced Block Designs with Correlated Error Structure
Authors: Santharam C. , Ponnuswamy K. N. 
Published: May 2007

AbstractNearest Neighbour Balanced Block Designs (NNBD) are widely used in biological and industrial experiments. In this article we examine the optimality and efficiencies of NNBD under various correlated error structure models. A numerical comparison of NNBD with regular block design (RBD) is also presented.
A Note on the Bias of O'Brien's OLS Test
Authors: Frick Hans 
Published: May 2007

AbstractO'Brien's Ordinary Least Squares (OLS) test is a well known procedure for testing the multivariate one‐sided hypothesis when the covariance matrix is unknown. Simulation results have been reported where the actual test level exceeds the nominal one. Here it is shown analytically that the actual level is always larger than the nominal one if the covariance matrix is nonnegative.
Conditional Maximum Likelihood Estimate and Exact Test of the Common Relative Difference in Combination of 2 × 2 Tables under Inverse Sampling
Authors: Lui Kung‐Jong 
Published: May 2007

AbstractOn the basis of the conditional distribution, given the marginal totals of non‐cases fixed for each of independent 2 × 2 tables under inverse sampling, this paper develops the conditional maximum likelihood (CMLE) estimator of the underlying common relative difference (RD) and its asymptotic conditional variance. This paper further provides for the RD an exact interval calculation procedure, of which the coverage probability is always larger than or equal to the desired confidence level and for investigating whether the underlying common RD equals any specified value an exact test procedure, of which Type I error is always less than or equal to the nominal α‐level. These exact interval estimation and exact hypothesis testing procedures are especially useful for the situation in which the number of index subjects in a study is small and the asymptotically approximate methods may not be appropriate for use. This paper also notes the condition under which the CMLE of RD uniquely exists and includes a simple example to illustrate use of these techniques.
A Clinically Orientated Approach for Combining Discordant and Concordant Sib Pairs
Authors: Ziegler Andreas , Hebebrand Johannes , Schäfer Helmut 
Published: May 2007

AbstractThe use of extreme discordant sib pairs (EDSP) or extreme concordant sib pairs (ECSP) has recently been proposed to increase power for mapping quantitative traits in humans (RISCH and ZHANG, 1995, 1996). In this paper we propose a test statistic to jointly analyze EDSP and ECSP based on a clinical sampling procedure. This test statistic does not fulfill any optimality criteria. However, this approach is useful for quantitative traits of clinical significance for which EDSP are rare and/or expensive to ascertain. We show how sample size calculations can be adjusted for recombination using single markers, multipoint analysis, incompletely polymorphic markers and varying proportions of ECSP. If the true genetic model is unknown, the combined approach appears to be more robust than sampling based on only EDSP or only ECSP. We discuss how to find the optimal proportion of EDSP and ECSP to be included in an analysis under power considerations.
Estimation of Variance Components Based on a Diallel Model Involving Maternal and Maternal Interaction Effects
Authors: Venkateswarlu K. 
Published: May 2007

AbstractFor a diallel model (TOPHAM, 1966) estimators of variance components are obtained by adopting the general approach outlined by SEELY (1969, 1970, 1971), SEELY and ZYSKIND (1971) and YUAN (1977) in the case of unbalanced data.
Significance Levels of Randomization Trend Tests in the Event of Rare Occurrences
Authors: Chen James J. , Kodell Ralph L. , Pearce Bruce A. 
Published: May 2007

AbstractIn the statistical evaluation of data from a dose‐response experiment, it is frequently of interest to test for dose‐related trend: an increasing trend in response with increasing dose. The randomization trend test, a generalization of Fisher's exact test, has been recommended for animal tumorigenicity testing when the numbers of tumor occurrences are small. This paper examines the type I error of the randomization trend test, and the Cochran‐Armitage and Mantel‐Haenszel tests. Simulation results show that when the tumor incidence rates are less than 10%, the randomization test is conservative; the test becomes very conservative when the incidence rate is less than 5%. The Cochran‐Armitage and Mantel‐Haenszel tests are slightly anti‐conservative (liberal) when the incidence rates are larger than 3%. Further, we propose a less conservatived method of calculating the p‐value of the randomization trend test by excluding some permutations whose probabilities of occurrence are greater than the probability of the the observed outcome.
Extended Poisson Process Modelling and Analysis of Count Data
Authors: Faddy M. J. 
Published: May 2007

AbstractIt is shown that any discrete distribution with non‐negative support has a representation in terms of an extended Poisson process (or pure birth process). A particular extension of the simple Poisson process is proposed: one that admits a variety of distributions; the equations for such processes may be readily solved numerically. An analytical approximation for the solution is given, leading to approximate mean‐variance relationships. The resulting distributions are then applied to analyses of some biological data‐sets.
Sample Size Determination for Designing a Strata‐Matched Case‐Control Study to Detect Multiple Risk Factors
Authors: Nam Jun‐Mo 
Published: May 2007

AbstractInvestigations of sample size for planning case‐control studies have usually been limited to detecting a single factor. In this paper, we investigate sample size for multiple risk factors in strata‐matched case‐control studies. We construct an omnibus statistic for testing M different risk factors based on the jointly sufficient statistics of parameters associated with the risk factors. The statistic is non‐iterative, and it reduces to the Cochran statistic when M = 1. The asymptotic power function of the test is a non‐central chi‐square with M degrees of freedom and the sample size required for a specific power can be obtained by the inverse relationship. We find that the equal sample allocation is optimum. A Monte Carlo experiment demonstrates that an approximate formula for calculating sample size is satisfactory in typical epidemiologic studies. An approximate sample size obtained using Bonferroni's method for multiple comparisons is much larger than that obtained using the omnibus test. Approximate sample size formulas investigated in this paper using the omnibus test, as well as the individual tests, can be useful in designing case‐control studies for detecting multiple risk factors.
Estimation of Residual Valve Gradient from Incomplete Data with Outliers
Authors: Chen Chao L. , Fernandez Javier , McGrath Lynn B. 
Published: May 2007

AbstractAn important indicator for the long‐term recovery after valve replacement surgery is postoperative valve gradient. This information is available only for patients received catheterization or echocardiogram postoperatively. It is plausible that sicker patients are more inclined to undergo these postoperative procedures and their valve gradients tend to be higher. Under this situation, ignoring the missing values and using sample mean based on the available information as an estimate of the whole study population leads to overestimation. Regression estimator is a reasonable choice to eliminate this bias if independent (explanatory) variables closely associated with both residual valve gradient and non‐response mechanism can be identified. Using a series of patients receiving St. Jude Medical prosthetic valves, we found that valve area index can be used as an independent variable in the regression estimator. Two digressions from the standard assumptions used in linear regression, heteroscedastic trend of the error term and outliers were found in the data set. Iteratively reweighted least square (IRLS) was adopted to handle heteroscedasticity. Influence function approach was used to evaluate the sensitivity of outliers in regression estimator. Under an equal response rate mechanism, IRLS not only solves the problem of heteroscedasticity, but is also less sensitive to outliers.
Simple Correspondence Analysis of Ordinal Cross‐Classifications Using Orthogonal Polynomials
Authors: Beh Eric J. 
Published: May 2007

AbstractA method is developed that caters for the application of correspondence analysis to two‐way contingency tables with one and two ordered sets of categories. The method involves calculating orthogonal polynomials of the type described by EMERSON (1968), and partitioning the chi‐square statistic using the method described in LANCASTER (1953). The method has all the features of simple correspondence analysis, although allows for additional information about the structure and association of the data to be made by isolating location, dispersion and higher order components of the rows and columns.
Stereological Implications of the Geometry of Cones
Authors: Gual‐Arnau X. 
Published: May 2007

AbstractGiven a cone around a curve β in IR3it is shown that there is no relation between the curvature of β and the volume of the cone but, in contrast to the volume, the surface area of the cone depends, albeit rather weakly, on the curvature of β. The geometric meaning of the stereological formulae when applied to cones is considered and it is noted that stereological methods to estimate the surface area of a cone need to be extremely precise to provide information concerning curvature or tortuosity.
A Coefficient of Agreement Adjusted for Bias in Paired Ordered Categorical Data
Authors: Svensson Elisabeth , Svensson Elisabeth 
Published: May 2007

AbstractThe quality of ordered categorical recordings is determined from repeated measurements on the same subject in order to assess the level of agreement between raters, scales or occasions. The presented rating‐invariant method for ordered categorical data provides means of analysing the quality of single‐item rating scales, irrespective of the number of possible response values and the marginal distributions. Marginal heterogeneity implies systematic disagreement, so‐called bias. An augmented ranking approach is the basis for the separation of inter‐rater disagreement into systematic and random components. Correlation between pairs of augmented rank values provides a measure of agreement to the best common ordering of paired classifications, given inter‐rater bias. The essential differences in interpretation and applicability of the proposed coefficient of agreement and the Spearman rank‐order correlation for ordered categorical data are discussed.
Adjustment of Prediction Intervals in Nonlinear Regression
Authors: Goh K. L. , Pooi A. H. 
Published: May 2007

AbstractBy treating the nonlinear model as if it were linear in the parameterization θ in the neighbourhood of the least squares estimate θC, two‐sided nominally‐q‐prediction intervals can be constructed by applying the usual linear model theory. The quadratic approximation of the expected coverage of the prediction intervals is derived for ap‐parameter nonlinear model. An adjustment of the nominally‐q‐prediction intervals is proposed. It is shown that, to the extent that quadratic approximation is adequate, the actual expected coverage of the adjusted prediction intervals isq.
A Minimum Distance Estimation Approach to Estimate the Recombination Fraction from a Marker Locus in Robust Linkage Analysis for Quantitative Traits
Authors: Ziegler Andreas , Kastner Christian 
Published: May 2007

AbstractOLSON and WIJSMAN (1993) have proposed a robust linkage analysis between quantitative traits and a marker locus using all relative pairs. We extend their work to estimate the recombination fraction using a two‐step procedure. In the first step Generalised Estimating Equations are solved. After robust linkage analysis minimum distance estimation is applied in the second step. Our approach requires a single codominant marker locus only. The relevant parameters of the genetic model can also be estimated by this method in the presence of linkage. We illustrate our approach by simulations.