bluuebunny/crossref_metadata_2025_split huggingface dataset:
A model for the spread of an epidemic
Authors: Längeb H. 
Published: May 2007

AbstractA temporally continuous and spatially discrete stochastic model for the spread of an epidemic within some set of holdings is constructed. A recursion formula is given for the probability that a certain set of holdings is infected at a certain moment. Moreover, under an additional condition (which will always be satisfied in practice) a formula for the expected value and the variance of the moment when a certain holding is infected the first time is given.
Application of nonlinear estimation in the exploration of dose‐response relationship
Authors: Postelnicu T. , Ursianu E. 
Published: May 2007

AbstractIn this paper a nonlinear model depending of one modifying factor to study the dependence of the PAPOVA virus reduction factor to the irradiant dose. In this model, least squares estimates of the parameters are obtained using the linearization method with the initial guesses values. An extension of the initial exponential model is proposed when the viral material is influenced by some modifying factors.
Measuring pairwise agreement among many observers
Authors: Schouten H. J. A. 
Published: May 2007

AbstractWeighted kappa is defined as a measure of pairwise inter observer agreement. A weighted intra class kappa coefficient is proposed to measure agreement on a particular response category. An interclass kappa coefficient is proposed for each pair of response categories. Simple estimation procedures are presented for the case where the observers judging one subject are not necessarily the same as those judging another subject. Large sample standard errors are derived and a numerical example is given.
EEG Feature extraction using parcor coefficients
Authors: Jindra R. H. 
Published: May 2007

AbstractThis study presents pattern recognition experiments of the electroencephalogram. The components of the feature vector are built up by Parcor coefficients which provide a simple structure of the covariance matrix. The BAYES classifier is implemented which is theoretically best in minimizing the error rate. The MAHALANOBIS classifier is used too by means of an averaged covariance matrix. The performance of the classifier is tested in experiment by computing the error rate.
Test d'ajustement gigogne
Authors: Chamayou J. M. F. 
Published: May 2007

AbstractPour dépouiller les résultats d'une expérience de biologie, on a mis au point une série de tests d'ajustement qui s'emboǐtent les uns dans les autres. Le premier test, un test mixte (continu, discret) permet de comparer deux échantillons, le second test permet, à l'aide dun estimateur du maximum de vraisemblance, d'ajuster une loi sur un échantillon à l'aide de l'estimateur défini sur le second échantillon et vice versa, lorsque les deux échantillons sont censés appartenir à la měme population.
A study of the effects of truncation in bivariate normal distribution
Authors: Gupta A. K. , Tracy D. S. 
Published: May 2007

AbstractThe moments of bivariate normal distribution, which is truncated with respect to both the random variables, are obtained by using the orthogonal expansion of the distribution and the properties of HERMITE polynomials. In particular the correlation coefficient of the truncated distribution is derived in terms of the actual correlation coefficient. In order to study the effect of truncation tables have been prepared of this correlation coefficient for certain given values of the actual correlation coefficient and for a few selected values of the points of truncation. A listing of the computer program for this purpose is also given.
Inequalities for estimative and predictive probabilities
Authors: Jensen D. R. 
Published: May 2007

AbstractLetμEbe the estimative andμPthe predictive measures for assessing Gaussian probabilities. It is shown for convex symmetric setsAthatμE(A)μP(A) and that similar inequalities hold between members of a sequence of predictive measures. An application yields inequalities for generalized indices of atypicality associated with the two methods.
Discriminant functions for linear comparisons of means when covariance matrices are unequal
Authors: Misra R. K. 
Published: May 2007

AbstractSituations exist, as in the biological example of discriminant analysis for natural hybridization, cited in the text, where (a) not all populations have equal variances, and (b) comparisions based on single degrees of freedom must be planned. This paper presents a statistical methodology of estimating discriminant functions for linear comparisons among k(<2) multivariate normal populations, and of testing their significance, when these populations have unequal covariance matrices.
Entwicklung eines dynamischen modells der stoffproduktion von zuckerrüben mit hilfe der multiplen regressionsanalyse
Authors: Hoffmann F. 
Published: May 2007

AbstractIf data are available which fulfil sufficiently the demands of statistics, and if physiologically plausible hypotheses can be formulated then it is possible to identify models by means of multiple regression analysis which simulate approximatively the behaviour of a crop within the range of the used data. A statistical as well as a physiological evaluation of the numerical results is necessary at every step of the calculation. The model consists of two equations describing the net assimilation rate and the distribution of the produced dry matter to leaves and roots. Some modification of the model are discussed, one of them with time‐dependent parameters. The following influencing variables are taken into account: leaf area or leaf mass, global radiation, nitrogen content of the leaves, and water stress, the latter being calculated from available soil moisture and potential evaporation. One version of the model together with an algorithm of dynamic optimization was used to control the water status of sugar beet in field trials.
Estimation of Linear Regression Model with Random Coefficients Ensuring Almost Non‐Negativity of Variance Estimators
Authors: Srivastava V. K. , Mishra G. D. , Chaturvedi A. 
Published: May 2007

AbstractFor a linear regression model with random coefficients, this paper considers the estimation of the mean of coefficient vector which, in turn, involves the estimation of variances of random coefficients. The conventional estimation methods for it sometimes provides negative estimates. In order to circumvent this kind of difficulty, a proposal is forwarded and is examined in the light of existing ones.
A 2‐step procedure for comparing binomial populations available when given differences are to find out
Authors: Horn M. , Kanka U. 
Published: May 2007

AbstractA two‐step procedure for comparing two binomial probabilities is presented. The method is compared with the exact test of FISHER. In average it needs essentially smaller sample sizes. In situations like drug screening it is desired to minimize the type II error rates for great differences of the binomial probabilities and to maximize it for small differences, simultaneously. In this point the method is superior to the FISHER test. Examples from drug screening demonstrate the advantages of the method.
A Phenomenologic‐mathematical Model of Growth Dynamics
Authors: Peil J. , Helwin H. 
Published: May 2007

AbstractStarting point of the modelling procedure are measured courses of the body length increase of man (inverse problem) reaching from the time of conception up to the end of adolescence. First assumption: The whole growth process can be subdivided into independent partial processes for succeeding time periods of the individual's development each of them producess a more or less marked growth spurt. 2. Superposition of these partial processes means addition of the portions of body length which are generated by the spurts yielding in this manner the measured course of body length increase. 3. There is no change in dynamics for producing the several growth spurts, and this dynamics will be described by the differential equation of the logistic law of growth. These steps will be interpreted in control‐theoretical terms. In this sense growth is a follow‐up control process which is governed by the genetically fixed “biological program of growth” in form of a step function of reference values.
Optimal Immunization and Isolation Policies for the GREENWOOD Chain‐Binomial Epidemic Model
Authors: Lefèvre Cl. 
Published: May 2007

AbstractOptimal control for the GREENWOOD chain‐binomial epidemic model is examined when control action is effected by immunization of susceptibles, isolation of infectives, or a combination of immunization and isolation in a way which minimizes the social cost of the epidemic and the cost of control over a given planning horizon. Optimal policies can be easily found for the deterministic version of the model. For the stochastic version, optimal policies are of much more complicated form and are only described in the situation when the probability of a given susceptible being infected is either small or large. This paper continues and corrects the work of DAYANANDA and HOGARTH (1977, 1978).
On the Use of the Negative Binomial in Epidemiology
Authors: Bennett B. M. 
Published: May 2007

AbstractThe use of the negative binomial, or the method of inverse sampling (e.g. (1), (2)), is suggested for prospective studies. This method usually has the advantage of fostering an early decision on the statistical significance of some etiological factor in examining successive patient records. Several limiting situations for the distribution are discussed regarding the prevalence of a disease. An example illustrates the method.
On the Determination of Numerical Density of Cell Organelles Using Mitochondria as an Example
Authors: Wassilew G. , Guski H. , Meyer R. 
Published: May 2007

AbstractMethods of determining the numerical density of cell organelles described in literature were critically reviewed in a morphometrical and stereological study of muscle cell mitochondria (heart muscle cells, diaphragm cells, sceletal muscle cells). A review of the method described by WEIBEL and GOMEZ showed that the numerical density of the mitochondria depends to a great extent on their shape and not so much on their size distribution. For this reason serial sections should be used to determine the shape factor in biological objects of unknown geometric shape.Generally, the numerical densities of mitochondria determined by using the method proposed by DEHOFF and RHINES were higher than those obtained with the method described by WEIBEL and GOMEZ. This is attributed to certain corrections used in the former method.Elaborate computations are generally involved and the geometric shapes of the object examined must be known in order to determine the numerical density of cell organelles or of other biological structures. The numerous sources of error involved in these methods give this parameter the character of an objective estimate. For this reason it is recommended that the value obtained should be checked by determining a two‐dimensional parameter. Our examinations of heart muscle mitochondria showed good agreement between the two parameters.
Defining and Detecting Second Order Correlations in Repeated Measurements Designs
Authors: Lienert G. A. 
Published: May 2007

AbstractIt is shown that 3‐point response curves from N individuals imply a second order correlation (triplet correlation) between the 3 series of repeated measurements if the curves are clustered as to their shapes. Coefficients are defined and tests are suggested for triplet correlations. Nonparametric alternatives to ANOVA are discussed for repeated measurement designs involving shapeclustered response curves.
A Model for Censored Survival Data When There is an Intervening Event
Authors: Higgins J. E. 
Published: May 2007

AbstractA model is discussed for incorporating information from a time‐dependent covariable (an intervening event) and covariables independent of time into the analysis of survival data. In the model, it is assumed that individuals are potentially subject to two paths to failure, one including the intervening event and the other not. Additional assumptions are that failure times associated with the two paths are independent and that the time to failure subsequent to the intervening event is dependent on the intervening event time. Allowing the underlying hazard rates for the model to follow a WEIBULL form, use of the model and methods for fitting and hypothesis testing are illustrated by application to a follow‐up study involving industrial workers where disability retirement was the intervening event. Extensions of the model to accommodate grouped survival data are presented.
A Stochastic Model for Oviposition Tactics in the Bean Weevil
Authors: Janardan K. G. , Srivastava R. C. , Taneja V. S. 
Published: May 2007

AbstractIn a recent paper, MITCHELL (1975) experimentally investigated the oviposition behavior of weevils on mung beans. In this paper, we develop a stochastic model to study the oviposition tactics of the female weevils on beans.
Formulas for the Computation of the WILCOXON Test and other Rank Statistics
Authors: Kummer G. 
Published: May 2007

AbstractA simple program for the computation of the WILCOXON test is introduced, which is suitable for implementation on a programmable desk computer. The allocation of ranks Ri is not necessary (see BERCHTOLD, 1979).In an analogous manner any ranks respectively rank sums also can be calculated, so that any test criterion or statistical estimate called rank statistic, may be completely constituted by computer programs without explicitely ranging of values.
On Some Nuisance Parameter Free Uniformly Most Powerful Tests
Authors: Kabe D. G. , Laurent A. G. 
Published: May 2007

AbstractTAKEUCHI (1969) provides a uniformly most powerful (UMP) one side test for testing the location parameter of the two parameters exponential model when the scale parameter is unknown. The power of his similar size α test depends, however, on the unknown scale parameter. In this case and in more general situations when there exists a sufficient statistic for the nuisance parameter, the theory of generalized THOMPSON's distributions, more specifically, the Thompsonization of a test statistic, LAURENT (1959, 1972) provides a UMP test whose power does not depend on the nuisance parameter. Examples of application of the general nuisance parameter free test procedure include here the truncated exponential, the inverse Gaussian, and the geometric distributions.
Some New Aspects of 2 × 2 Table Measures and Their Applications
Authors: Börzsönyi L. 
Published: May 2007

AbstractThe paper deals with the analysis of 2 × 2 table measures and describes several new possibilities for applying them. An estimate is given for HELLMICH's asymmetric K‐measure and a possible extension of the contingency concept in a special 2×2 case is presented. The concept introduced and the recommended computer application of the 2×2 table measures is supported by new results obtained for the relationship between jaw joint pains and noises examined earlier by F. SCHMID and C. ZSCHEGE. With a larger number of observations the opportunity arises for a deeper analysis of this dental problem using path analysis. The plan of evaluation and the conncetion between the variables examined is illustrated by a path‐diagram.The association tables determined on the basis of the (C) values introduced during the examination of jaw joint disturbances using association analysis draw attention to the special role of the left side of mastication. The paper raises ideas for further generalisations and indicates where further research is needed.
On the Discrete POISSON‐Inverse GAUSSian Distribution
Authors: Shaban S. A. 
Published: May 2007

AbstractThe discrete POISSON‐inverse GAUssian distribution is obtained by compounding the POISSON distribution with the inverse‐GAUssian distribution. The maximum likelihood of the parameter is discussed. Comparison with the POISSON, negative binomial, POISSON‐LINDLEY and the generalized WARING distribution when fitting a distribution to accident statistics data is given.
Mean Square Error Comparison for MINQUE, ANOVA and Two Alternative Estimators Under the Unbalanced One‐Way Random Model
Authors: Ahrens H. , Kleffe J. , Tenzler R. 
Published: May 2007

AbstractStandard methods for estimating variance components, such as HENDERSON's methods are based on equating well‐known sums of squares to expectation. These methods are computationally simple and therefore widely used.From a theoretical point of view we are not that happy about these methods. There is lack of theoretical background, there is no unique description of these methods and finally, we know very little about optimal properties of HENDERSON's estimates unless the model is totally balanced.It was mainly this trouble which caused statisticians to develop an extensive theory on optimal estimation of variance components and among the mostly known contributors to this line of research was C. E. RAO. We believe that his MINQUE principle gave a new idea to statistical theory. It provides some kind of optimality and does not refer to the normal assumption which is in difference to the majority of alternative proposals made in literature. And even if one thinks of its theoretical background as being not much better than that of HENDERSON's method one has to accept the advantage of having a unique estimation procedure.A difficulty is its calculation. The computational advice given by RAO requires inversion of matrices as high in dimension as the total number of observations is. This is, we believe, the main reason for its neglect by most of the people working on such kind of data. But among all estimates which perform any kind of optimality MINQUE seems still to be among the simplest to calculate. That is why we think, whenever optimal estimates of variance components can be made attractive to users, we should start with MINQUE or related estimates and it would be a good deal to work on more effective ways of its computation.One way is to present explicit and easy handable formulae under several standard ANOVA models of which the simplest one is going to be studied in this paper. Analog results for more complicated cases are given in KLEFFE (1980). Having solved this major problem we may proceed by comparing new and traditional methods to find out about what estimate is to be recommended.
Accuracy Analysis with Special Reference to the Prediction of Grassland Yield
Authors: Allen O. Brian , Raktoe B. L. 
Published: May 2007

AbstractIn this paper techniques are presented which allow the evaluation of accuracy of prediction methods, with special reference to the prediction of grassland yield. The more prominent methods of predicting grassland yield are discussed. Two coefficients, which are normalizations of the mean‐squared‐error, are proposed as measures of accuracy. It is shown that the mean‐squared‐error partitions into the sum of three meaningful components of error. The distribution of the components is discussed under the assumption that the actual and predicted yields have a bivariate normal distribution. The problem of accuracy analysis is addressed when one is only interested in ranking the yields of grassland areas. The ratio of the mean‐squared‐error for two competing prediction methods is investigated. Finally, a multiplicative model is studied which allows predicting ability to differ over time or over regions.
On the robustness of Hotelling'sT2
Authors: Bauer P. 
Published: May 2007

AbstractThe influence of outliers in one of the samples on Hotelling's generalizedT10is investigated by considering the directional derivate of the expected value with respect to the proportion of outliers in the disturbed population at the proportion zero. To make the problem tractable, the estimate of the covariance matrix thereby is replaced by its expected value under the altered distributional assumptions. For the two sample case the influence of outliers on the variance of the test criterion is also investigated. The analytical findings are confirmed by simulation results.
A More Powerful Simultaneous Test Procedure in Configural Frequency Analysis
Authors: Lehmacher W. 
Published: May 2007

AbstractIn the Configural Frequency Analysis (CFA) of KRAUTH and LIENERT (1973 a, b), overfrequented (or underfrequented) cells in multivariate contingency tables are identified by simultaneous binomial tests. As an alternative, finite and asymptotic tests are proposed, which are derived from the (exact conditional) generalized hypergeometrical distribution of the cell frequencies. These tests allow for considerably more powerful decisions than do the conservative binomial tests.
A Multivariate Permutation Test for the Analysis of Arbitrarily Censored Survival Data
Authors: Marcuson R. , Nordbrock E. 
Published: May 2007

AbstractThe exact generalization of GEHAN's (1965) two‐sample test for arbitrarily censored survival data has been overlooked by subsequent work on the multisample problem. We give this general covariance matrix and show how it may be used in test procedures. While this permutation test is less powerful than its competitors in cases where both apply, it may be used on types of data not previously discussed.
The MARKOV Process Approach to Survival Analysis
Authors: Tan W. Y. 
Published: May 2007

AbstractConsider a two organs system for which one of the two organs must function for the individual to survive. This paper derives the survival probability distributions under more general situations by adopting Markov process approach, thus providing extensions of results given in GROSS, CLARK and LIU (1971) and KODLIN (1967). The results obtained are then extended to a k‐organs system.
Effect of Non‐normality on Response to Selection in small Populations
Authors: Rao M. Gopinath , Jain J. P. 
Published: May 2007

AbstractThe expressions for the r‐th largest standardised deviate have been derived for beta, gamma, exponential and log‐normal distributions. Using these expressions the intensities of selection have been computed for different parametric values of the distributions and of p, the proportion saved for samples of size upto 10 and compared with the corresponding values for normal distribution. The normal approximation to beta and gamma distributions is seen to be quite robust in predicting response to selection under moderately heavy and low cullings.
Random Networks of Catalytie Biochemical Reactions
Authors: Sonntag I. , Feistel R. , Ebeling W. 
Published: May 2007

AbstractFormulae for the probabilities of components and cycles and of their numerical distribution are obtained for large random networks by computer experiments and combinatorical considerations. Probability considerations are made and formulae for the occurrence and distribution of cycles and of components of certain number, size and structure are received for random networks.
On the Second‐Order and Orientation Analysis of Planar Stationary Point Processes
Authors: Ohser J. , Stoyan D. 
Published: May 2007

AbstractThe paper presents a method for the second‐order analysis of point processes, and, basing on it, for their orientation analysis. For the reduced second moment measure of stationary and of stationary and isotropic point processes a new estimator is given, which has some advantages in comparison to RIPLEY's estimator. Three examples illustrate the use of the statistical techniques.
GOMPERTZ Model with Heredity in Random Environment
Authors: Prajneshu  
Published: May 2007

AbstractIn this paper we investigate the GOMPERTZ model in a randomly varying environment after incorporating the aspect of heredity. Exact expressions for the first two moments of the logarithm of population size are obtained. Special case of the dichotomic MARKOV process being a white noise process is considered. The stability of the system is also discussed.