bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimation, Testing and Adjustment of Transient Healthy Entrant Effects
Authors: Hoover D. R. , Muñoz A. , He Y. , Jacobson L. P. 
Published: May 2007

AbstractAt recruitment, cohorts often selectively exclude non‐healthy individuals with symtoms prodromal to the response. After a certain time from study entry, the health status of participants regresses towards the underlying population. Methods developed here estimate, test and adjust out this transient healthy entrant effect from survival function estimation. In the simplest case, each participant's time is divided into: Ω1‐ immediately after study entry while these transient healthy entrant effects are strong, and Ω2‐ later, after they have vanished or greatly diminished. Truncated Kaplan‐Meier models fit separately to risk sets in Ω1and Ω2quantify and remove the bias from transient healthy entrant effects and test for statistical significance. If sufficient data are available, these methods can be expanded to dividing the subjects' time into multiple subperiods or fitting specific models of attenuation for the transient healthy entrant effect. Examination of nickel refinery workers and an HIV‐1 infected cohort suggest that, without the adjustments proposed here, transient healthy entrant effects cause moderate overestimations of survival probability.
Estimating Pair Correlation Functions of Planar Cluster Processes
Authors: Stoyan Dietrich , Stoyan Helga 
Published: May 2007

AbstractThe pair correlation functiong(r)is an important tool in exploratory data analysis and model choice in point process statistics. In the case of cluster processes, the behaviour ofg(r)for smallris particularly interesting. But just these values ofg(r)can be estimated with difficulties only. This paper tries to show that kernel estimators yield reliable results. It is useful to work with variable band widths. An example where the points are positions of pines in a forest illustrates the application of the method.
A Note on Dilution Assays with Errors in Dilutions
Authors: Trajstman A. C. 
Published: May 2007

AbstractDilution assays involve a number of steps in the preparation of the dilutions and the delivery of the diluent. These steps may give rise to dilution errors. If assays are treated as if the nominal dilutions have been used then under estimates of micro‐organism density may occur. In this paper models for dilution errors are introduced.
Modelling Cultivar Mixtures Using SEIR Compartmental Models
Authors: Goleniewski George 
Published: May 2007

AbstractA SEIR (susceptible, exposed, infectious, removed) compartmental model is constructed to represent disease progress in a two cultivar mixture. The concept of the spore pool is the means by which inoculum exchange between the constituent cultivars is represented. Infection frequencies for each cultivar are permitted to vary with that cultivar's susceptible fraction according to a power‐law relationship. For each cultivar, new additions to the susceptible class balance deaths from the removed class so that the total leaf area of all four SEIR classes remains constant. It is shown that an equilibrium with non‐zero diseased classes exists in a certain parameter regime. A numerical stability analysis is performed using the model equations linearised about this equilibrium. The effects of changing induced resistance parameters within the model are demonstrated graphically. It is also demonstrated that the proportion of susceptibles as a function of mixture composition has an optimum in the regime where nontrivial equilibria exist, a feature of practical interest provided that equilibrium is reached within a growing season.
A Comparison of Generalized Poisson Distribution for Modeling Chromosome Aberrations
Authors: Lee Carl M.‐S. , Famoye Felix 
Published: May 2007

AbstractVarious models have been developed for modeling the distribution of chromosome aberrations in the literature (e. g. Consul, 1989). Generalized Poisson distribution is among the popular ones. The parameters of this distribution provide meaningful interpretation of the induction mechanism of the chromosome aberrations. In this article, we apply several estimation methods to estimate the generalized Poisson parameters for fitting the number of chromosome aberrations under different dosages of radiations. The methods compared are moment, maximum likelihood, minimum chi‐square, weighted discrepancy and empirically weighted rates of change. Our study suggests that the empirically weighted rates of change method results in smallest Mean Square Error and Mean Absolute Error for most dosages of radiations. The data used for this comparison are from Janardan and Schaeffer (1977).
An Exact Confidence Interval for the Ratio of Two Related Proportions
Authors: Parker R. A. 
Published: May 2007

AbstractI propose an exact confidence interval for the ratio of two proportions when the proportions are not independent. One application is to estimate the population prevalence using a screening test with perfect specificity but imperfect sensitivity. The population prevalence is the ratio of the observed prevalence divided by the test's sensitivity. I describe a method to calculate exact confidence intervals for this problem and compare these results with approximate confidence intervals given previously.
Selecting the Best Population, Provided It Is Better than a Control: The Unequal Variance Case
Authors: Hsu Lifang , Chen Pinyuen 
Published: May 2007

AbstractBechhofer and Turnbull (1978) proposed two procedures to compare k normal means with a standard and the procedures guarantee that (1) with probability at least P0* (specified), no category is selected when the best experimental category is sufficiently worst than the standard, and (2) with probability at least P1* (specified), the best experimental category is selected when it is sufficiently better than the second best and the standard. For the case of common known variance, they studied a single‐stage procedure. For the case of common unknown variance, they studied a two‐stage procedure. Under the same formulation of Bechhofer and Turnbull (1978) and for the same selection goals (1) and (2) described above, Wilcox (1984a) proposed a procedure to the case of unknown and unequal variances, and supplied a table of the necessary constants to implement the procedure. This paper considers the case of unknown and unequal variances for the same formulation of Bechhofer and Turnbull, and Wilcox, but assumes that μ0 is an unknown control. A two‐stage procedure is proposed to solve the problem. A lower bound of the probability of a correct selection is derived and it takes the same form as the double integral appeared in Rinott (1978) which was used for the lower bound of the probability of a correct selection for a different selection goal.
Estimation of Finite and Closed Population Size: An Inverse Sampling Approach with Unequal Recapture Probability
Authors: Hossain Md. Forhad 
Published: May 2007

AbstractBased on capture‐mark‐recapture sampling methods the problem of estimating unknown population size was considered. The sampling started with the assumption that at the beginning of the experiment all the individuals were unmarked, and the unmarked individuals caught in each sample will be marked and returned to the original population before the next sample is drawn. It is also assumed that the population is closed by birth, death, emigration and immigration. Using a general inverse sampling approach, the unknown population size N is estimated by a maximum likelihood estimator (MLE), and a simple form for approximate MLE is obtained. The probability function for S (the minimum number of samples required to be drawn to have L (L ≥ 1) samples, each of which contains at least one marked individual) and the form for E[S] are also obtained. In addition, corrections and improvements of some previous works in this field are given.
On the Estimation of Intra‐Sire Regression Heritability Subject to Linear Constraint
Authors: Prabhakaran V. T. , Jaggi Seema 
Published: May 2007

AbstractThe derivation of the restricted intra‐sire regression heritability estimator is provided. Procedures for obtaining a stable estimate of residual error variance σ2are outlined. A small illustration based on live data is given.
Estimation of Parameters for One‐Way Layout with Rank Set Sampling
Authors: Muttlak Hassen A. 
Published: May 2007

AbstractRanked set sampling (RSS) as suggested by McIntyre (1952) and independently by Takahasi and Wakimoto (1968) may be used to estimate the parameters of the one‐way layout. The objective is to use the RSS method to increase the efficiency of the estimators relative to the simple random (SRS) method. Estimators of the populations (treatments) effect are considered. Computer simulated results are given, and an example using real data presented to illustrate the computations.
Estimating the Population Mean Using Extreme Ranked Set Sampling
Authors: Samawi Hani M. , Ahmed Mohammad S. , Abu‐Dayyeh Walid 
Published: May 2007

AbstractPrecision of the estimate of the population mean using ranked set sample (RSS) relative to using simple random sample (SRS), with the same number of quantified units, depends upon the population and success in ranking. In practice, even ranking a sample of moderate size and observing the ith ranked unit (other than the extremes) is a difficult task. Therefore, in this paper we introduce a variety of extreme ranked set sample (ERSSs) to estimate the population mean. ERSSs is more practical than the ordinary ranked set sampling, since in case of even sample size we need to identify successfully only the first and/or the last ordered unit or in case of odd sample size the median unit. We show that ERSSs gives an unbiased estimate of the population mean in case of symmetric populations and it is more efficient than SRS, using the same number of quantified units. Example using real data is given. Also, parametric examples are given.
Multivariate Homogeneity Testing Using an Extended Concept of Nearest Neighbors
Authors: Barakat Ali S. , Quade Dana , Salama Ibrahim A. 
Published: May 2007

AbstractGiven independent multivariate random samples {Xij: j = 1, …, ni} from Fi, for i = 1,2, a test is desired for H0: F1 = F2 against general alternatives. Consider the k · (n1 + n2) possible ways of choosing one observation from the combined samples and then one of its k nearest neighbors, and let Sk be the proportion of these choices in which the point and neighbor are in the same sample. Schilling (1986) proposed Sk as a test statistic, but did not indicate how to determine k. We suggest as test statistic W = N Σ kSk, which we show is equivalent to a sum of N Wilcoxon rank sums, and also to a sum of two two‐sample U‐statistics of degrees (1, 2) and (2, 1). Simulation with multivariate normal data suggests that our test is generally more powerful than Schilling's test using k = 1, 2, or 3. We illustrate its use with Fisher's iris data.
Estimation of the Power of the Kruskal‐Wallis Test
Authors: Mahoney Michelle , Magel Rhonda 
Published: May 2007

AbstractPower calculations of a statistical test require that the underlying population distribution(s) be completely specified. Statisticians, in practice, may not have complete knowledge of the entire nature of the underlying distribution(s) and are at a loss for calculating the exact power of the test. In such cases, an estimate of the power would provide a suitable substitute. In this paper, we are interested in estimating the power of the Kruskal‐Wallis one‐way analysis of variance by ranks test for a location shift.We investigated an extension of a data‐based power estimation method presented by Collings and Hamilton (1988), which requires no prior knowledge of the underlying population distributions other than necessary to perform the Kruskal‐Wallis test for a location shift. This method utilizes bootstrapping techniques to produce a power estimate based on the empirical cumulative distribution functions of the sample data. We performed a simulation study of the extended power estimator under the conditions ofk= 3 andk= 5 samples of equal sizesm= 10 andm= 20, with four underlying continuous distributions that possessed various location configurations. Our simulation study demonstates that the Extended Average × & Y power estimation method is a reliable estimator of the power of the Kruskal‐Wallis test fork= 3 samples, and a more conservative to a mild overestimator of the true power fork= 5 samples.
Point Estimation on Relative Risk under Inverse Sampling
Authors: Lui Kung‐Jong 
Published: May 2007

AbstractWhen the underlying disease is rare, to control the coefficient of variation for the sample proportion of cases, we may wish to apply inverse sampling. In this paper, we derive the uniformly minimum variance unbiased estimator (UMVUE) of relative risk and its variance in closed form under inverse sampling. On the basis of a Monte Carlo simulation, we demonstrate that using the UMVUE of relative risk can substantially reduce the mean‐squared‐error of using the maximum likelihood estimator, especially when the number of index cases in both comparison samples is small. For a given fixed total cost, we include a program that can be used to find the optimal allocation for the number of index cases to minimize the variance of the UMVUE as well.
Notes in Case‐Control Studies with Matched Pairs under Inverse Sampling
Authors: Lui Kung‐Jong 
Published: May 2007

AbstractIn case‐control studies with matched pairs, the traditional point estimator of odds ratio (OR) is well‐known to be biased with no exact finite variance under binomial sampling. In this paper, we consider use of inverse sampling in which we continue to sample subjects to form matched pairs until we obtain a pre‐determined number (>0) of index pairs with the case unexposed but the control exposed. In contrast to use of binomial sampling, we show that the uniformly minimum variance unbiased estimator (UMVUE) of OR does exist under inverse sampling. We further derive an exact confidence interval of OR in closed form. Finally, we develop an exact test and an asymptotic test for testing the null hypothesis H0: OR = 1, as well as discuss sample size determination on the minimum required number of index pairs for a desired power at α‐level.
A New Approach to Equivalence Assessment in Standard Comparative Bioavailability Trials by Means of the Mann‐Whitney Statistic
Authors: Wellek S. 
Published: May 2007

AbstractBy a suitable transformation of the pairs of observations obtained in the successive periods of the trial, bioequivalence assessment in a standard comparative bioavailability study reduces to testing for equivalence of two continuous distributions from which unrelated samples are available. Let the two distribution functions be given by F(x) = P[X ≤ x], G(y) = P[Y ≤ y] with (X, Y) denoting an independent pair of real‐valued random variables. An intuitively appealing way of putting the notion of equivalence of F and G into nonparametric terms can be based on the distance of the functional P[X > Y] from the value it takes if F and G coincide. This leads to the problem of testing the null hypothesis Ho P[X > Y] ≤ 1/2 ‐ ε1 or P[X > Y] ≥ 1/2 + ε2 versus H1 : 1/2 − ε1 < P[X > Y] < 1/2 + ∈2, with sufficiently small ε1, ε2 ∈ (0, 1/2). The testing procedure we derive for (0, H1) and propose to term Mann‐Whitney test for equivalence, consists of carrying out in terms of the U‐statistics estimator of P[X > Y] the uniformly most powerful level a test for an interval hypothesis about the mean of a Gaussian distribution with fixed variance. The test is shown to be asymptotically distribution‐free with respect to the significance level. In addition, results of an extensive simulation study are presented which suggest that the new test controls the level even with sample sizes as small as 10. For normally distributed data, the loss in power as against the optimal parametric procedure is found to be almost as small as in comparisons between the Mann‐Whitney and the t‐statistic in the conventional one or two‐sided setting, provided the power of the parametric test does not fall short of 80%.
Normal Approximation of a Discrete Distribution on Exact Test of Uniform Association in a 2 × 3 Table
Authors: Tomizawa Sadao , Seo Takashi , Kikuchi Seiji 
Published: May 2007

AbstractFor the analysis of 2 × 3 tables, TOMIZAWA (1993) considered an exact test of uniform association, which is an extension of independence, and then derived a discrete distribution. This paper gives a normal approximation of the discrete distribution and describes that the normalized statistic can test a one‐sided hypothesis on the uniform association. Also it points out that the square of the normalized test statistic is equal to the Pearson's chi‐squared statistic for testing the uniform association.
A Two‐Stage Test for Distinguishing Random, Pseudo‐Random and Nonrandom Mating Populations
Authors: Tai J. J. , Liu Joumiao 
Published: May 2007

AbstractThere is no such an implication that a population in Hardy‐Weinberg equilibrium must have undergone random mating. Therefore, it is unequivocal that the usual tests for “Hardy‐Weinberg equilibrium” are indeed tests for “random union of gametes” but not for “random mating”. In this paper, utilizing population characteristics expressed in equilibrium state (equilibrium or disequilibrium) and mating behavior (random or nonrandom), a two‐stage testing procedure for distinguishing random, pseudo‐random and nonrandom mating populations is proposed. At the first stage, a population is tested for Hardy‐Weinberg equilibrium. If insignificant result (i.e., in equilibrium) is obtained, then to a second stage the population is further tested for mating behavior. Random mating‐pairs data are needed here for analysis instead of random individuals for usual Hardy‐Weinberg equilibrium tests. Since distinguishing the three types of mating populations depends on the combined results of two stages, the probability of correct determination of the two‐stage tests is discussed by simulation studies.
Universal Optimality of Nearest Neighbour Balanced Block Designs using Second Order Correlated Models
Authors: Santharam C. , Ponnusamy K. N. , Chandrasekar B. 
Published: May 2007

AbstractBlock designs for observations correlated in one dimension are investigated. Santharam and Ponnusamy (1995) investigated the universal optimality of Nearest Neighbour balanced block designs (NNBD) using first order correlated models (AR(1), MA(1) and ARMA(1,1)). In this article we have investigated the universal optimality of NNBD using second order correlated models (ar(2), and MA (2)).
Estimation of Ratio Using Rank Set Sampling
Authors: Samawi Hani M. , Muttlak Hassen A. 
Published: May 2007

AbstractRanked set sampling (RSS) as suggested by McIntyre (1952) and developed by Takahasi and Wakimoto (1968) is used to estimate the ratio. It is proved that by using RSS method the efficiency of the estimator relative to the simple random sampling (SRS) method has increased. Computer simulated results are given. An example using real data is presented to illustrate the computations.
Testing Equality of Regressions When Error Variances are Heterogeneous to Study the Production Differentials of Rice in Bangladesh
Authors: Bhuyan K. C. , Majumder A. K. 
Published: May 2007

AbstractMethod to test the equality of p sets of regression coefficients in presence of heterogeneous error variances is suggested. The sets of regression coefficients are obtained from a sample where the sample observations are classified into p classes. The test statistic to test the significance of the regression is also suggested.As an application of the method, the equality of production functions of boro rice produced in different districts of Bangladesh in two different periods is tested.
Assessing Local Influence in Measurement Error Models
Authors: Lee Andy H. , Zhao Yuejen 
Published: May 2007

AbstractWe consider the assessment of local influence for generalized linear models when the covariates are measured with errors. We show how to evaluate the effect that perturbations to the data, case weights, and model assumptions may have on the parameter estimates. Based on the likelihood displacement functions, some useful influence diagnostics are derived. Two examples illustrate application of the proposed diagnostics and assessment of the measurement error assumptions.
Modeling Exposure Intensity and Susceptibility‐Latency Effect from Retrospective Cohort Data ‐ Application to Lung Cancer Mortality in Blackfoot Disease Endemic Area in Taiwan
Authors: Lee Wen‐Chung , Lin Ruey‐Shiung 
Published: May 2007

AbstractIt is challenge in epidemiology to characterize the temperol aspect of exposure‐disease association. The authors propose a stochastic model to deal with exposures that are time‐dependent and exhibit susceptibility and latency effects. The model is applied to a retrospective cohort data on lung cancer mortality in the blackfoot disease endemic area in Taiwan. The authors compare the proposed model with the multistage model, the back‐calculation model, the catalytic model, and the age‐period‐cohort models.
Correcting for Confounding in Analyzing Receiver Operating Characteristic Curves
Authors: Smith P. J. , Thompson T. J. 
Published: May 2007

AbstractA method is described for modeling a receiver operating curve as a function of confounding covariates when the outcome of the screening test is a continuous variate. A parametric survival model is proposed for modeling the distribution of the screening test outcome as a function of true disease status and other confounding covariates. The sensitivity and specificity of the screening test at any “cut‐point” along the range of the screening test outcome may be estimated easily from the estimated survival distribution. Confidence intervals and an estimate of the area under the curve are derived.
Construction of Prediction Intervals in Location‐Scale Families
Authors: Kushary Debashis 
Published: May 2007

AbstractLet x1≤x2≤x3… ≤xrbe thersmallest observations out ofnobservations from a location‐scale family with density\documentclass{article}\pagestyle{empty}\begin{document}$ \frac{1}{\sigma}f\left({\frac{{x - \mu}}{\sigma}} \right) $\end{document}where μ and σ are the location and the scale parameters respectively. The goal is to construct a prediction interval of the form\documentclass{article}\pagestyle{empty}\begin{document}$ \left({\hat \mu + k_1 \hat \sigma,\,\hat \mu + k_2 \hat \sigma} \right) $\end{document}for a location‐scale invariant function,T(Y) = T(Y1, …, Ym), ofmfuture observations from the same distribution. Given any invariant estimators\documentclass{article}\pagestyle{empty}\begin{document}$ \hat \mu $\end{document}and\documentclass{article}\pagestyle{empty}\begin{document}$ \hat \sigma $\end{document}, we have developed a general procedure for how to compute the values ofk1andk2. The two attractive features of the procedure are that it does not require any distributional knowledge of the joint distribution of the estimators beyond their first two raw moments and\documentclass{article}\pagestyle{empty}\begin{document}$ \hat \mu $\end{document}and\documentclass{article}\pagestyle{empty}\begin{document}$ \hat \sigma $\end{document}can be any invariant estimators of μ and σ. Examples with real data have been given and extensive simulation study showing the performance of the procedure is also offered.
On Variograms in Point Process Statistics
Authors: Wälder Olga , Stoyan Dietrich 
Published: May 2007

AbstractVariograms, which are frequently used in geostatistics, are of value also in the statistics of marked point processes. When the marks come from a random field which is independent of the point process, ideas of geostatistics suffice for the interpretation of point process variograms. When this model is not appropriate, interactions between the points lead to point process variograms having forms which are unusual in geostcistics. This is shown by three theoretical examples and one from forestry.