bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Logic Programming and Expert Statistical Systems Approach for Tissue Characterization in Magnetic Resonance Imaging
Authors: Levy G. C. , Dudewicz E. J. , Harner T. J. , Dudewicz Edward J. , Wehrli F. W. , Breger R. 
Published: May 2007

AbstractThe main research goal has been to evaluate significant factors affecting the in vivo magnetic resonance imaging (MRI) parameters T, T, T2, and 1H density. This approach differs significantly from other such projects in that the experimental data analysis is being performed while concurrently developing automated, computer‐aided analysis software for such MRI tissue parameters.In the experimental portion of the project, statistical analyses, and a heuristic minimum/maximum discriminant analysis algorithm have been explored. Both methods have been used to classify tissue types from 1.5 Tesla transaxial MR images of the human brain.The developing program, written in the logic programming language Prolog, is similar in a number of ways to many existing expert systems now in use for other medical applications; inclusion of the underlying statistical data base and advanced statistical analyses is the main differentiating feature of the current approach.First results indicate promising classification accuracy of various brain tissues such as gray and white matter, as well as differentiation of different types of gray matter and white matter (e.g.: caudate‐nucleus vs. thalamus, both representatives of gray matter; and, cortical white matter vs. internal capsule as representative of white matter). Taking all four tissue types together, the percentage of correct classifications ranges from 73 to 87%.
Statistical Inference for a Gibbs Point Process of Mutually Non‐Intersecting Discs
Authors: Stoyan D. , Stoyan D. 
Published: May 2007

AbstractFor a Gibbs point process of mutually non‐intersecting discs a parameter estimation method is suggested. It is applied to a pattern of positions of beadlet anemones, for which, until now, no appropriate Gibbs process model has been found.
Nonparametric Two‐Sample Tests for General Clinical Data
Authors: Hilgers R. A. 
Published: May 2007

AbstractStarting from the discussion of a practical example a unifying concept for the derivation of meaningfully interpretable nonparametric tests for the two‐sample case is developed which may well be adapted for other designs, too.This methodology covers other well‐known procedures, e.g. Fisher's exact test, the Wilcoxon‐Mann‐Whitney and Gehan's tests, and may furthermore be extended to all situations sharing the same fundamental structural property of the sample space, namely its strict order induced by the substantial problem under study.The resulting test procedure is discussed for a randomization argument, exact and approximate, as well as for the general specific test problem. A numerical example is provided.
VariousX2‐Tests of Homogeneity: Some Comments
Authors: Elandt‐Johnson Regina C. 
Published: May 2007

AbstractMethodological issues in the analysis of incidence rates or prevalence proportions for count data, presented in a form of a sequence of 2×2 tables, corresponding to levels (strata) of a specified variable (risk factor)X, are discussed. Suppose λ1iand λ2iare the incidence rates of an eventDin the ith stratum for populations 1 and 2, respectively. Thehomogeneity(null) hypothesis is formulated in the form:H0:λ1i=λ2ifor alli(i= 1, 2, …,I). ThreeX2‐tests forH0and their theoretical bases are discussed:XTotal2which is sensitive to alternativesHA:λ1i± λ2ifor at least somei;XComb2which is sensitive to alternativesHA: λ1iλ2i2or< λ2ibutnot bothfor alli; andXDiff2which is sensitive to alternativesHA:λ1i>λ2i3for someiand λ1i′< λ2i′for somei′ (i≠i′). These statistics satisfy the relationXTotal2=XComb2+XDiff2. Also,X′2‐statistic for pooled data is calculated, which in conjunction withXComb2can serve for detecting confounding. Although most of these techniques are known, they are rather scattered in the literature, and not always considered jointly, as it is emphasized in the present paper. It is hoped that these comments will be helpful to biostatisticians as well as to epidemiologists and medical researchers in the analysis of mortality and morbidity data. For illustration, two examples with large sets of epidemiological data are given.
The Wald Statistic in Proportional Hazards Hypothesis Testing
Authors: Bangdiwala Shrikant I. 
Published: May 2007

AbstractIn survivorship modelling using the proportional hazards model of Cox (1972, Journal of the Royal Statistical Society, Series B, 34, 187–220), it is often desired to test a subset of the vector of unknown regression parameters β in the expression for the hazard rate at timet. The likelihood ratio test statistic is well behaved in most situations but may be expensive to calculate. The Wald (1943, Transactions of the American Mathematical Society 54, 426–482) test statistic is easier to calculate, but has some drawbacks. In testing a single parameter in a binomial logit model, Hauck and Donner (1977, Journal of the American Statistical Association 72, 851–853) show that the Wald statistic decreases to zero the further the parameter estimate is from the null and that the asymptotic power of the test decreases to the significance level. The Wald statistic is extensively used in statistical software packages for survivorship modelling and it is therefore important to understand its behavior. The present work examines empirically the behavior of the Wald statistic under various departures from the null hypothesis and under the presence of Type I censoring and covariates in the model. It is shown via examples that the Wald statistic's behavior is not as aberrant as found for the logistic model. For the single parameter case, the asymptotic non‐null distribution of the Wald statistic is examined.
On Testing Equality of Means of Correlated Variates with Missing Data on One Response
Authors: Bhoj Dinesh S. 
Published: May 2007

AbstractA statistic is proposed for testing the hypothesis of equality of the means of a bivariate normal distribution with unknown common variance and correlation coefficient when observations are missing on one of the variates. The distribution of the statistic is approximated by a normal distribution under the null hypothesis. The empirical powers of the statistic are computed and compared with those of the conventional paired t and the other known statistics. The power comparisons support the use of the proposed test.
On Comparing Correlated Means in the Presence of Incomplete Data
Authors: Bhoj Dinesh S. 
Published: May 2007

AbstractTwo statistics are proposed for testing the hypothesis of equality of the means of a bivariate normal distribution with unknown common variance and correlation coefficient when observations are missing on both variates. One of the statistics reduces to the one proposed by Bhoj (1978, 1984) when the unpaired observations on the variates are equal. The distributions of the statistics are approximated by well known distributions under the null hypothesis. The empirical powers of the tests are computed and compared with those of some known statistics. The comparison supports the use of one of the statistics proposed in this paper.
On Model‐Choice in Discrimination with Categorical Variables
Authors: Wernecke K.‐D. , Haerting J. , Kalb G. , Stuerzebecher E. 
Published: May 2007

AbstractAn algorithm for model selection in discrimination with categorical variables is presented. It is based on four models applied hierarchically and linked with a build‐up procedure of feature‐selection. The choice of models and features is ensured by a consequent cross‐validation. Results of an application in medical diagnostics are described.
Comparing Power Behaviours for Some Goodness‐of‐Fit Test Statistics in Sparse Multinomials
Authors: Lawal H. B. 
Published: May 2007

AbstractThis paper is concerned with the power behaviour of four goodness‐of‐fit test statistics in sparse multinomials withkcells. Most previous work has been concerned only with both Pearson'sX2and the likelihood ratio test statistics. We consider in this study, two additional test statistics, namely, the Cressie‐Read test statistic –I(2/3) and the modified Freeman‐Tukey test (FT) statistic. Becausek≥ 10 in this study, a Monte Carlo procedure based on 1000 simulated samples is used to estimate the powers for the four test statistics. Alternatives on various line segments are employed. Results suggest that none of the test statistics completely dominate the other and that the choice of which test to use depends on the nature of the alternative hypothesis. These results are consistent with those obtained by West and Kempthorne (1972), although, the Pearson's χ2test statistic may be preferred because of its closer approximation to the χ2distribution in terms of the attained α levels.
F‐tests for Hypotheses with Block Matrices and Under Conditions of Orthogonality in the General Multivariate Gauss‐Markoff Model
Authors: Oktaba Wiktor 
Published: May 2007

AbstractThe multivariate general Gauss‐Markoff (MGM) model (U, XB, ∑⊗σ2V) when the matrices V ≥ 0 and ∑ > 0 are known and the scalar σ2 > 0 is unknown, is considered. The present paper is a continuation of two earlier works (Oktaba, 1988a, b). If XB = X1Σ + X2Δ, then the F‐test for verification the hypothesis WΣA = 0 is presented. Moreover, under conditions of orthogonality the decomposition of the matrix SA (ŁBCA)′L−(ŁBCA) into the sum of s = r(L) matrices is given, where ŁBCA is the estimator of the parametric estimable functions ŁBCA, Cov (ŁBCA) = A′ ∑⊗σ2L = ŁC4Ł′, B̌ = (X′T−X)−X′T−U, C4 = (X′T−X)−M, where M = M′ is any arbitrary matrix such that R(X) ⊂ R(T), T=V+XMX′; T− is any c‐inverse. R(A) is the linear space generated by the colums of A. Then under additional assumption on normality of U the statistics F for testing ŁBA = 0 is deduced.Under conditions of normality of U and decomposition of SA, the statistics F1, …, Fs for the hypotheses ji BA = 0 (i = 1,…, s) are established.
Cost‐Efficient Density Estimation Based on Nearest Individual Distances in a Natural Forest
Authors: Prayag V. R. , Gore A. P. 
Published: May 2007

AbstractOne useful method of estimating tree density is based on point to individual distances. Estimators are available for data on i‐th nearest individual distances, i = 1, 2, 3,…, etc. This paper provides a method of deciding the optimal value of i in the sense of providing the maximum likelihood estimator of density with minimum mean square error subject to given outlay. The result derived for a Poisson forest is extended to an aggregated forest.
On the Additive and Multiplicative Models of Relative Risk
Authors: Kodell Ralph L. , Gaylor D. W. 
Published: May 2007

AbstractTwo popular models of absence of synergism in epidemiologic cohort studies are analyzed and compared. It is shown that the statistical concept of the union of independent events that traditionally has given rise to the “additive” model of relative risk can also generate the “multiplicative” model of relative risk. In fact, the same set of approximating conditions can be used to generate both models, which suggests a lack of identifiability under the traditional approach. An alternate approach is proposed in this paper. The new approach does not require the assumption that background risk factors are independent from causal agents of interest. The concept of “dose additivity” is discussed.
Selection of Populations: An Overview and Some Recent Results
Authors: Van Laan P. Der , Vebdooren L. R. 
Published: May 2007

AbstractIn practice the problem of comparing several treatments can often be formulated as which treatment can be considered best. Selection procedures have been designed specifically to answer questions like this one. In many situations selection procedures are more realistic than the usual testing procedures. In this paper an introduction and an overview of selection of populations will be given with special reference to Normal, Binomial, Poisson and Multinomial populations. In the field of agricultural applications these distributions play an important role.Bechhofer's and Gupta's approach are presented side by side for a number of problems. After that the relatively new procedures of Somerville are discussed. Finally, some recent results are presented.
Stabilization Coefficient of Random Variable
Authors: Liu Chui‐Yu , Zheng Zhang‐Yuan 
Published: May 2007

AbstractCoefficient of variation, standard deviation divided by mean, has some essential defects. Its density, expectation and variance are too complex to make the statistical inference for such a coefficient. The definition of stabilization coefficient is just the reciprocal of variation coefficient, mean divided by standard deviation. Such a coefficient has a simple expectation and a simple variance, and is an asymptotically unbiased estimator and a consistent estimator of its true value. Furthermore, coefficient of stabilization has an asymptotic normality. Due to its statistical advantages, coefficient of stabilization is easy to be tested statistically. In some applied fields, usually, there is an increasing standard deviation accompanying an increasing mean. Coefficient of stabilization can be practically used for some comparison studies in such fields. Illustrations about comparing microorganism strains are given in this paper. The robustness of stabilization coefficient is satisfactory.
On Some Application of Non‐Homogeneous Semi‐Markov Processes
Authors: Wajda W. 
Published: May 2007

AbstractIn this paper a non‐homogeneous semi‐Markov process is applied to a study of the behaviour of a cell after irradiation. The definition and main properties of the non‐homogeneous semi‐Markov process are reminded. Moreover one assumes that the cell may stay in one of four states namely, normal state, damaged state 1, damaged state 2, state of death, which are defined. Using the results of the theory of non‐homogeneous semi‐Markov processes and Markov renewal theory the characteristics of interest pertaining to the cell behaviour are studied.
On Second Order Neighbourhood Analysis of Mapped Point Patterns
Authors: Doguwa Sani I. 
Published: May 2007

AbstractGetis and Franklin (1987), introduced a technique based on second order methods, called second order neighbourhood method, which is used to quantify clustering at various spatial scales. Variants of this method are introduced for testing whether a spatial point pattern is consistent with the hypothesis of a Poisson process. These variants are applied to point location data for a sample of Ponderosa pine (Pinus ponderosa) trees.
Asymptotic Interval Estimation of Some Cause‐Specific Mortality Risk Measures in Epidemiologic Studies
Authors: Rao B. Raja , Marsh Gary M. , Winwood Joan 
Published: May 2007

AbstractThe present paper describes the algebraic and statistical relationships between the population‐based mortality risk measures, the Standardized Mortality Ratio (SMR) and the Standardized Risk Ratio (SRR), and their respective proportional mortality‐based counterparts, the internally and externally Standardized Proportional Mortality Ratios (SPMR, SePMR). The paper shows, how under some reasonable assumptions, asymptotically precise inferences about population‐based risk measures can be made from studies of proportional mortality. Through application of the asymptotic multivariate normal approximation to the multinomial distribution, shortest confidence intervals for the relative SMR (RSMR) involving the corresponding SPMR are constructed for any cause of death. This same technique is also used to construct asymptotic prediction intervals about the cause‐specific SePMR which, with high probability, contains the corresponding relative SRR (RSRR).The utility of the proportional mortality measures SPMR and SePMR as estimators of the corresponding cause‐specific risks RSMR and RSRR in occupational epidemiologic research is empirically evaluated using data from two recent occupational cohort studies.Asymptotic Bonferroni type simultaneous inferential techniques are also developed for these measures which facilitate the assessment of overall risk in the presence of several competing factors.
A Note on the Application of Martingales in a Problem of Non‐Communicable Epidemics
Authors: Biswas S. , Noor Hamed Saad 
Published: May 2007

AbstractThe present paper is a Martingale approach to some non‐communicable epidemic problem (e.g. cervical cancer). It is assumed the progress of the disease from pre‐cancerous lesions to several grades of dysplasia and ultimately leading to carcinomia in situ and invasive cancer follows by consecutive hittings; and the regression (or the backward movement) from these states to ultimately non cancerous state; may be analogous to consecutive healings. Each hitting and healing thus considered to be a birth and death respectively in the density dependent linear birth and death process. Given that a patient is in some states of dysplasia the problem lies in finding the proportion of patients coming back to noncancerous state and the expected time for the same. Martingales constructed on a linear birth and death process have been employed to answer the problems.
A Response‐Additive Model for Analyzing Mixtures of Two Chemicals in the Salmonella Reversion Assay
Authors: Chen James J. , Heflich Robert H. , Hass Bruce S. 
Published: May 2007

AbstractA statistical model is proposed for analyzing the mutagenic responses produced by mixtures of two chemicals in the Salmonella reversion assay. This model is based on the simplex‐lattice design for mixtures with the total concentration fixed. The dose‐response relationship is expressed as a function of both the proportions of the two chemicals and the total concentration of the chemicals in the mixture. If the joint action of two chemicals can be predicted by response‐additivity, then the response of the mixture at the total concentrationTcan be represented by the weighted average of the responses produced by the individual chemicals at the same concentrationTwith the weights for individual responses being equal to the proportions of the chemicals in the mixture. Two non‐additive models, synergism and antagonism, are discussed. An example is illustrated by analyzing the joint mutagenic effects of 1‐nitrobenzo(a)pyrene (1‐NBP), and 3‐NBP in the Salmonella reversion assay.
General Statistical Issues in the Analysis of Pedigree Data
Authors: Wilson Susan R. 
Published: May 2007

AbstractWhen analysing pedigree data, it is just as important to detect deficiencies in the fitting and application of the current model as to be able to specify the model, estimate its parameters and test appropriate hypotheses. Some of the statistical issues that arise in the fitting and application of genetic models to pedigree data are discussed in this paper. A particular set of data and a non‐controversial characteristic have been chosen to highlight these issues.
A Nonparametric Test for Ordered Alternatives in Two‐Way Layouts with Censored Data
Authors: Magel Rhonda C. 
Published: May 2007

AbstractA nonparametric test is presented to test for ordered alternatives of the treatment effects when the data follow a two‐way layout. The observations are subject to arbitrary right censorship. The test is similar to the one developed by Hettmansperger and Norton (1987) for uncensored observations.
A Comparison of Methods to Deteet Adulteration in Natural Products Under a Restricted Alternative
Authors: Katz B. P. , Brown M. B. 
Published: May 2007

AbstractMultivariate statistical tests used to assess purity of natural food products, such as fruit juices, do not assume prior knowledge about the most likely causes of adulteration. We describe a likelihood ratio test that has a directional alternate hypothesis so that prior knowlege about the most likely method of adulteration can be utilized. Critical regions for this statistic are based on a nonparametric tolerance interval, which makes it insensitive to an assumption of multivariate normality.This new test is compared to two tests currently used for identifying adulterated samples. It is shown that the new test will have increased power relative to the other tests in the direction of the predicted adulteration.
Spatial Data Analysis of Regional Counts
Authors: Cressie Noel , Read Timothy R. C. 
Published: May 2007

AbstractCounts data from spatially continguous regions offer a challenge to the statistician both from the data analytic and the statistical modeling point of view. Important applications include epidemiological studies (e. g., cancer mortality over the counties of the USA) and Census surveys (e. g., undercount over the Census blocks of an urban area). It has long been recognized by time‐series analysts that data close together in time usually exhibit higher dependence than those far apart. Time‐series data analysis relies on methods of data transformation, detrending, and autocorrelation plotting. It is our intention in this article to generalize this approach to a spatial setting. To do this we consider a small spatial data set of 100 observations. Through the use of a square‐root transformation, a weighted median polish and a variogram analysis of the median‐polish residuals, we represent the transformed data as a trend plus stationary error. Thus we show how standard data‐analytic techniques can be modified both to mitigate and to exploit the spatial relationships.
The Effect of Parameters Change of a Semi‐Markov Model on Interval Transition Probabilities
Authors: Jain R. K. 
Published: May 2007

AbstractThis paper examines the changes in parameters of a Semi‐Markov model and its impact on interval transition probabilities. Computational techniques are used to illustrate that the model is sensitive enough to depict changes in the interval transition probabilities if the paramters of the model are modified.
Some Improved Regression Estimators of Heritability
Authors: Verma Davendra , Jain J. P. 
Published: May 2007

AbstractThree new improved regression estimators of heritability viz. modified range restricted estimator, minimum quadratic loss estimator and minimax linear restricted estimator are proposed. In addition, these estimators are illustrated and compared numerically with the existing restricted estimator based on linear stochastic constraint.
On the Behaviour of Fligner‐Wolfe‐Trend Test “Control versus k Treatments” with Application in Toxicology
Authors: Hothobn Ludwig 
Published: May 2007

AbstractThe behaviour of Fligner‐Wolfe‐trend test “control versus k treatments” was characterized by simulation. Sample size estimation took place via non‐central t‐distribution. Some results on asymptotic efficiency were shown and compared with simulation results for small sample sizes. A test version for umbrella alternatives was given and characterized by simulation.
A New Statistical Method for Mixed Variates Analysis in Epidemiological Research
Authors: Wortha H.‐P. 
Published: May 2007

AbstractEpidemiologically oriented research often may not do without observational or only partially controlled studies. In many such situations both qualitative characteristics and quantitative ones are observed. In literature there are different methods of handling such problems. The paper presents a method for analyzing dependencies resp. associations between random variates of any kind. The model concerned fullfills the whole field between analysis of variance, analysis of covariance and contingency table analysis. The method is named MIVA or mixed variates analysis, bases on the class of Conditional Gaussian Distributions of the exponential family and results in a unique system of mixed and unmixed measures of association–of pairwise, partial, multiple and global type. These measures are easy to be estimated and tested on significant deviation from zero. They may be used describing or analyzing dependence structures in many epidemiological studies but also in other fields.