bluuebunny/crossref_metadata_2025_split huggingface dataset:
Monte Carlo Comparison of Multiple Comparison Procedures
Authors: Zieliński Wojciech 
Published: May 2007

AbstractSeven procedures of multiple comparisons: Tukey, Scheffé, Bonferroni, Studentized Maximum Modulus, Duncan, Newman‐Keuls and F are compared with respect to the probability of the correct decision. Monte Carlo simulation shows that there is no the best procedure.AMS 1985 Subject Classification: 62 J 15.
Estimation of Design and Genetic Components of Variance in Double Cross Mating Designs
Authors: Ponnuswamy K. N. , Natarajan S. , Subramani J. 
Published: May 2007

AbstractThe present paper deals with the estimation of design and genetic components of variance based on full double crosses and partial tetra‐allel crosses. We have presented the explicit experissions for the symmetric sums estimators of design components of variance based on Hinkelmann's model. Consequently we have also presented explicitly the genetic components of variance based on full double crosses and partial tetra‐allel crosses. The procedure is also illustrated with the help of a hypothetical data.
Robust Umbrella Tests for a Generalized Behrens‐Fisher Problem
Authors: Chen Yuh‐Ing 
Published: May 2007

AbstractThis paper is concerned with testing for umbrella alternatives in a k‐sample location problem when the underlying populations have possibly different shapes. Following CHEN and WOLFE (1990b), rank‐based modifications of the HETTMANSPERGER‐NORTON (1987) tests are considered for both the settings where the peak of the umbrella is known and where it is unknown. The proposed procedures are exactly distribution‐free when the continuous populations are identical with any shape. Moreover, the modified test for peak‐known umbrella alternatives remains asymptotically distribution‐free when the continuous populations are assumed to be symmetric, even if they differ in shapes. Comparative results of a Monte Carlo study are presented.
Confidence Intervals and Tests of Hypotheses on Variance Components in an Unbalanced Two‐fold Nested Design
Authors: Hernandez Ramon P. , Burdick Richard K. , Birch Nancy J. 
Published: May 2007

AbstractConfidence intervals and tests of hypotheses on variance components are required in studies that employ a random effects design. The unbalanced random two‐fold nested design is considered in this paper and confidence intervals are proposed for the variance components σ2/A and σ2/B. Computer simulation is used to show that even in very unbalanced designs, these intervals generally maintain the stated confidence coefficient. The hypothesis test for σ2/A based on the lower bound of the recommended confidence interval is shown to be better than previously proposed approximate tests.
Using Discrete Distributions to Compute Powers for Goodness‐of‐Fit Test Statistics in a One‐Way Multinomial Setting
Authors: Lawal H. B. 
Published: May 2007

AbstractBoth the Bionomial and Poisson distributions are employed in this study to compute approximate powers for goodness‐of‐fit test statistics. The procedure adopted involves simulating 1000 samples from each of these distributions. These samples are then employed to compute both the randomized nominal critical values and estimated powers. The type I error rates returned from the use of the randomized critical levels Cα fall within the acceptable regions. We illustrate the use of the procedure with the Pearson's X2 test statistic and show that this can readily be extended to any of the other well known goodness‐of‐fit test statistics.
Paradigm Epidemiology?
Authors: Streitberg B. 
Published: May 2007

AbstractThe paper is a first answer to a fundamental critique of epidemiology that has been voiced in a recent book by Immich. Apart from giving an introduction to this important and controversial work, mostly questions of a biostatistical nature are discussed here. A fundamental counter critique from an epidemiological viewpoint is necessary, but is not attempted in this paper.
A Markov Analysis of the Service System for Severe Mental Illness
Authors: Liu Chui‐Yu , Hu Teh‐Wei , Jerrell Jeanette 
Published: May 2007

AbstractBased on one year of services utilization data in Santa Clara County, we modeled the mental health service system as a time‐continuous Markov process with corresponding Kolmogorov differential equations. The dynamics of the system's operations in that year can be expressed by the results derived from the intensity matrix and the explicit solution of Kolmogorov differential equations. The key problem with the system is linked to its emergency service. An unexpectedly high proportion of patients had a certain period in which they did not receive any services. Most of patients desired more intensive services. Some tentative thoughts about the stabilization of the system are discussed.
A Nonrecursive Estimator for the Cox Model
Authors: Schäbe Hendrik 
Published: May 2007

AbstractThe Cox regression model is one of the most widely used models to incorporate covariates. The frequently used partial likelihood estimator of the regression parameter has to be computed iteratively. In this paper we propose a noniterative estimator for the regression parameter and show that under certain conditions it dominates another noniterative estimator derived by Kalbfleish and Prentice. The new estimator is demonstrated on lifetime data of rats having been subject to insult with a carcinogen.
Approximating the Non‐Null Distribution of the Likelihood Ratio Test Statistic
Authors: Lawal H. B. 
Published: May 2007

AbstractThis paper is concerned with investigation into the behavior of the likelihood ratio test statistic G2 when the alternative hypothesis M (QQ) is the true model. Exact moments of G2 are computed empirically and three approximations are considered for approximating the non‐null distribution of G2. Our results show that the two parameter gamma distribution provides a closer approximation to the exact powers of G2. A randomized procedure was employed to obtain critical values based on 1000 simulated samples.
A Maximum Rank‐Sum Test for One‐Pulse Variation in Monthly Data
Authors: Marrero O. 
Published: May 2007

AbstractA nonparametric test to detect a pulse in monthly data is presented. This test is a maximum rank‐sum test. The test statistic can be computed from frequencies or rates. The exact null distribution of the test statistic is tabulated for pulses that last 3, 4, 5, or 6 months. Estimates from a simulation study of the test's type I error rate and power are presented. The statistical modeling of the data is discussed. Several examples are given to illustrate the application of the test and the modeling procedures. Practical matters such as the treatment of tied observations, the effect of unequal lengths in the months, sample‐size calculation, and post‐test power analysis are discussed and illustrated with examples.
Probability Model for Crop Yield Forecasting
Authors: Jain R. C. , Agrawal Ranjana 
Published: May 2007

AbstractMarkov chain approach has been adopted to forecast sugarcane yield. Two years data (1977–78 and 1978–79) on biometrical characters and yield collected by IASRI, New Delhi under the pilot study on pre‐harvest forecasting of sugarcane yield in Meerut district (U.P.) were utilised for the study. Yield forecasts at 7–8 months after planting (about 2–3 months before harvest) were very close to the observed ones, per cent deviations from observed being 4% in 1978–79 and 2% in 1977–78. This study thus reveals that Markov Chain method can be successfully used in crop yield forecasting.
Designing Experiments Having Treatments and Covariates
Authors: Lefkovitch L. P. 
Published: May 2007

AbstractThe use of covariates in the designing of experiments having a block and treatment structure is discussed. It is argued that the replicates of each treatment should cover the complete range of each quantitative covariate, so far as is possible. Two optimality criteria are discussed for this purpose, one based on a neighbourhood distance calculated from the means and standard deviations of the covariate, the other based on an extended definition of the canonical efficiencies of the design.
An Annealing Algorithm for Searching Optimal Block Designs
Authors: Jansen J. , Douven R. C. M. H. , van Berkum E. E. M. 
Published: May 2007

AbstractThis paper is concerned with a computer algorithm for searching optimal block designs. The algorithm uses a technique called simulated annealing. Exchange and interchange steps are defined in a way similar to JONES and ECCLESTON (1980). Two examples are used to show the performance of the algorithm.
Imbalance and its Influence on Variance Component Estimation
Authors: Ahrens Heinz J. , Sánchez Jesús E. , Ahrens H. , Sánchez J. 
Published: May 2007

AbstractA measure of imbalance has been defined for the one‐way classification (Model II) in order to quantify the degree of imbalance. Using the ANOVA and MINQUE procedures we consider the variances and relative efficiencies of the estimators of σ2a as functions of the measure of imbalance. We propose a method for generating all the designs which are possible from a given number of observations and a given number of groups. Numerical results for special types of designs show the influence of imbalance on the variance and relative efficiency of an estimator for σza.
Goodness of Fit Tests for Proportional Hazards Regression Models
Authors: Le Chap T. , Zelterman Daniel , Le Chap T. 
Published: May 2007

AbstractWe present a test of goodness of fit for the proportional hazard regression model. The test is based on a score statistic for testing against local mixture alternatives. Contrary to the findings of several other authors, we detect a significant lack of fit in Freireich's leukemia data.
A Modified “Best Maximum Likelihood” Estimator of Line Regression with Errors in Both Variables: An Application for Estimating Genetic Admixture
Authors: Chakraborty Ranjit , Srinivasan M. R. , Chakraborty Ranajit 
Published: May 2007

AbstractThe problem of estimation when both variables are subject to error in a linear regression model has been discussed in the literature and it has wide applications in econometrics and other social sciences. In this paper we consider the relaxation of the assumption of homoscedasticity and introduce the covariance structure of errors of measurements in the analysis to obtain a Modified Best Maximum Likelihood (MBML) estimator of the regression coefficient.We also provide an application of the above modification to estimate the extent of genetic contribution of a parental population in an admixed population. With data on frequencies of “unique” African and Caucasian alleles in US Blacks, it is shown that US Blacks have 30.9·2.2 percent genes that are of Caucasian origin.
On the Estimation of the Size of a Finite and Closed Population
Authors: Bolfarine Heleno , Leite José G. , Rodrigues Josemar 
Published: May 2007

AbstractIn this paper, we consider the problem of estimating the size N of a finite and closed population, using data obtained from capture‐recapture experiments. By defining an appropriate model, we investigate the maximum of the likelihood, of the profile likelihood and of an orthogonal adjusted profile likelihood (COX and REID, 1987) function. We show that they all may present infinity as the maximum likelihood estimator of N. This seems to be a characteristic of the likelihood approach in this problem. Further, we present a Bayesian approach with minimum prior information as a way of countering this difficulty. Exact analytical expressions for the posterior modes are also obtained.
On Counts of Organisms Able to Signal the Presence of an Observer
Authors: Kemp A. W. 
Published: May 2007

AbstractSuppose that organisms occur as entities that are either singlets or doublets, that entities are able to signal to one another the presence of an observer, and that, as a result, they detect the presence of the observer according to a non‐homogeneous birth process. If the behaviour of individuals (and hence their detection by the observer) is thereafter determined by a damage process, the distribution of the number of individuals seen by the observer is found to be a geometric pseudo‐geometric convolution. Properties of the distribution and maximum‐likelihood estimation are described. The model is shown to give a very good fit to the ruffed grouse data of GATES et al. (1968, Biometrics). Variants of the model am discussed.
Theorems on Binary Responses to Combinations of Stimuli
Authors: Drane J. Wanzer , Fu Chongyau , Postelnicu Tiberiu , Drane Prof. J. Wanzer , Fu Chongyau , Postelnicu Tiberiu 
Published: May 2007

AbstractBinary responses, such as dead or alive, in response to two or more stimuli are considered in this paper. Their joint actions are defined to be either independent, conditionally independent, additive, synergistic or antagonistic. The Bernoulli parameter is replaced by a dose‐response function. Theorems on the probability of a response resulting from the joint actions of the stimuli are stated, and proved. An example is also included.
Handling with Spatial Censored Observations in Statistics of Boolean Models of Random Sets
Authors: Molchanov Ilya S. , Molchanov Ilya S. 
Published: May 2007

AbstractLetAbe a Boolean model. The existing methods of statistical estimation of its parameters are extended to the case when the observation ofAis spoiled by a certain hindrance. It means that we observe another setA'instead ofA.E.g. the setA′may take the formAorAwhere is a stationary random set (assumed to be hindrance) with a known distribution. This construction generalizes the methods of statistics of censored observations to random sets. A brief description of statistical methods for Boolean models is included. All methods rely on the notion of the empirical capacity functional and use Glivenko‐Cantelli theorem for random closed sets.
Subset Selection of an Almost Best Treatment
Authors: Laan Paul Van Der , van der Laan Paul 
Published: May 2007

AbstractA generalized goal using subset selection is discussed for the location parameter case. This goal is to select a non‐empty subset from a set of k (k ≥ 2) treatments that contains at least one ε‐best treatment with confidence level P*. For a set of treatments an ε‐best treatment is defined as a treatment with location parameter on a distance less than or equal to ε(ε ≥o) from the best treatment, where best is defined as largest value of the location parameter. The efficiency of subset selection of an ε‐best treatment relative to subset selection of the best treatment is investigated and is computed for some values of k and the confidence level for the Normal case as well as for the Logistic case.
Mixed Model Discrete Regression
Authors: Zhaorong J. , McGilchrist C. A. , Jorgensen M. A. 
Published: May 2007

AbstractModels and estimention procedures are given for linear regression models in discrete distributions when the regression contains both fixed and random effects. The methods are developed for discrete variables with typically a small number of possible outcomes such as occurs in ordinal regression. The method is applied to a problem arising in the comparison of microbiological test methods.
Validity of the F‐ratio Test for Replicated Latin Square Designs Under Relaxed Conditions
Authors: Monga  , Tardif S. 
Published: May 2007

AbstractIn a replicated latin square design, the F‐ratio test statistic enjoys a noncentral Fisher distribution under the assumption of i.i.d. normal errors. This paper shows that the latter distribution still holds if a certain correlation structure is introduced within the sets of errors associated with each latin square. The correlation structure is characterized by the fact that, put in form of matrices, these sets of errors have exchangeable rows and exchangeable columns.
Monthly Variability of Fibrocystic Breast Disease as Assessed by Palpation1
Authors: Pagano Marcello , Leviton Alan , Allred Elizabeth , Heydn Siegfried 
Published: May 2007

AbstractBreast nodules in women afflicted with “fibrocystic breast disease” show a variability in time. This paper proposes a Markov chain to model this temporal change. The model seems to fit the available data very well, if different parameters are used for pre‐ und post‐menopausal women.
Statistical Model for Locating Fragile Points on Nucleic‐ACID Molecules
Authors: Liu Chui‐Yu , Liu Chui‐Yu 
Published: May 2007

AbstractBreakage is an important behavioral characteristic of nucleic‐acid molecules. This paper concerns the problem of estimating the location of fragile (F) points on biological macromolecules such as DNA or RNA. We assume that the original length of the macromolecules is a random variable, breakage occurs more likely at some points of the macromolecules than others, and the number of breakages, except for those occurring at such points, is according to a Poisson process. Under these assumptions, the density function of the length of a randomly chosen segment has been derived. The Maximum Likelihood Estimation (MLE) is suggested for estimating the parameters. The model is applied to the data available on RNA of Avian Myeloblostosis Virus (AMV), and three possible F‐points have been located. The robustness of the model is discussed.
Some Inference Results in Modified Freund's Bivariate Exponential Distribution
Authors: Hanagal David D. 
Published: May 2007

AbstractThis paper obtains MLE of the parameters and large sample tests for independence as well as symmetry of two components in modified Freund's model of bivariate exponential distribution proposed by PROSCHAN and SULLO (1974). The Modified Freund's model is more general model than the other models like MARSHALL and OLKIN (1967), FREUND (1961) and BASU (1974) which are the sub‐models of Modified Freund's model. It satisfies the most important Loss of Memory Property (LMP) which implies the other three sub‐models also satisfy LMP. We also obtain the confidence interval for the parameters in the above model. We extend the above results in fixed time censored and random censored samples.
On Estimation of Relative Risk in Case Control Studies
Authors: Singh Padam , Aggarwal Abha Rani 
Published: May 2007

AbstractAn estimator of relative risk in a case control study has been proposed in terms of observed cell frequencies and the probability of disease. The bias of the usual estimator i.e odds ratio as compared to the new estimator has been workedout. The expression of Mean Square Error of proposed estimator has been derived in situations where probability of disease is exactly known and when it is estimated through an independent survey. It has been observed that there is a serious error using odds ratio as an estimate of relative risk when probability of disease is not negligible. In such situations the proposed estimator can be used with advantage.
A Within Year Growth Model for Crop Yield Forecasting
Authors: Jain R. C. , Agrawal Ranjana , Singh K. N. 
Published: May 2007

AbstractLogistic growth model can be used to forcast total dry matter at maturity about one month before harvest in wheat and rice whereas forecast of head/panicle weight can be made about 15 days advance of harvest. Total dry matter as well as head weight at maturity were over estimated in general when partial data were used. Modified growth model worked well to adjust for over estimation in total dry matter and head weight at maturity.
Analysis of Survival Data with Two Dependent Competing Risks
Authors: Bagai Isha , Rao B. L. S. Prakasa 
Published: May 2007

AbstractHere we consider a competing risks model where the two risks of interest are not independent. The dependence is due to the additive effect of an independent contaminating risk on two initially independent risks. The problem is identifiable when the three risks fllow independent exponential distributions and also when the two initial risks follow proportional hazards model. Procedures are suggested for estimation and testing hypotheses regarding the parameters of the three exponentials in the first can and the constant of proportionality in the second case, when the information available consists of the times to death and the causes of death of the individuals.
Statistical Comparison of Band Spectral Powers: An Aid in Stochastical Analysis of Brain Electrical Activity. Part 2: Construction of Confidence Intervals of Spectral Band Power and their Application in EEG‐Analysis
Authors: Schack B. , Wagner H. , Grieszbach G. , Witte H. 
Published: May 2007

AbstractBased on the result that the distribution of estimated spectral power in broad frequency bands can be approximated by x2‐distribution the accuracy of the approximation was tested by different models of stochastical processes. Two significance tests for comparison of spectral band powers on the basis of this approximation and the estimation of confidence intervals were developed and tested by application on EEG data. By means of these test statistics, a new basis for statistical comparison between EEG maps of groups as well as between maps of groups and single maps can be suggested.
A Simple Approach to Analyse Non‐Orthogonal Data
Authors: Subramani J. 
Published: May 2007

AbstractThe present paper deals with an alternative and simple procedure to analyse the non‐orthogonal data. The procedure is general in nature but has some advantages for the non‐orthogonal data due to some missing observations. The procedure is applied to (i) two way classification with unequal number of observations per cell; (ii) randomized block designs with some missing observations and (iii) balanced incomplete block designs and also illustrated with the help of numerical examples.
The S‐Distribution A Tool for Approximation and Classification of Univariate, Unimodal Probability Distributions
Authors: Voit Eberhard O. 
Published: May 2007

AbstractIn many statistical applications a data set needs to be evaluated but there is no solid information about which probability distribution might be most appropriate. Typical solutions to this problems are: to make assumptions that facilitate mathematical treatment; to use a family of distribution functions that contains all relevant distributions as special cases; or, to employ nonparametric methods. All three solutions have disadvantages since assumptions are usually difficult to justify, families of distributions contain too many parameters to be of practical use, and nonparametric methods make it difficult to characterize data in a succinct quantitative form. The S‐distribution introduced here is a compromise between the conflicting goals of simplicity in analysis and generality in scope. It is characterized by four parameters, one of which reflects its location, the second one its spread, and the remaining two its shape; transformation to a standard form reduces the number of free parameters to two. Cumulatives and densities are computed numerically in fractions of seconds, key features like quantiles and moments are easily obtained, and results can be presented in terms of parameter values. The S‐distribution rather accurately models different distribution functions, including central and noncentral distributions, and thus competes in flexibility with some distribution families. As an approximation, the S‐distribution provides a graphical method for demonstrating relationships between distributions, such as the relationships between central F, χ2 and χ−2 or central and noncentral t, χ‐1, and normal.