bluuebunny/crossref_metadata_2025_split huggingface dataset:
Use of Quantal Method for Estimation of Agricultural Production
Authors: Deo Sheela S. , Pandit Satish J. 
Published: May 2007

AbstractThe usual analysis of quantal response data occurring in diverse fields such as economics, medicine, psychology and toxicology use probit and logit models or their extensions with generalised least squares or the principle of likelihood as the method of statistical inference. The quantal method was first used for estimation of agricultural production by S. M. Vidwans (1991). He has collected the data on yield rate of rabi jowar from the farmers for year 1981 in four villages in Shirur taluka and three villages in Daund taluka of Pune district of Maharashtra in India. The probit regression line is fitted. The fit is not good. The estimation is done using truncation of data. Here we observed that a second degree polynomial is a good fit to the data. This will give correct prediction of agricultural production with smaller standard error.
Prediction of New Cases of Disease or Infection for Small Subgroups
Authors: Solomon P. J. , Wilson S. R. 
Published: May 2007

AbstractFor health planning, a common requirement is to predict future numbers of cases of a disease or infection for which just a few cases have been observed so far. In this paper, the subgroup prediction method is extended to accommodate, from a practical view, two commonly occurring features of epidemic data, namely, variable periods of observation and nonexponential epidemic growth. A table is provided which considerably simplifies application of the method. The work was motivated by a consulting project in connection with the Australian AIDS epidemic, for which some results are presented.
A Selection Procedure for Selecting Good Exponential Populations
Authors: Gill Amar Nath , Sharma Suresh Kumar 
Published: May 2007

AbstractConsider k independent exponential populations with location parameters μ1,…, μk and a common scale parameter or standard deviation θ. Let μ(k) be the largest of the μ's and define a population to be good if its location parameter exceeds μ(k) –Δ1. A selection procedure is proposed to select a subset of the k populations which includes the good populations with probability at least P*, a pre‐assigned value. Simultaneous confidence intervals, that can be derived with the proposed selection procedure, are discussed. Moreover, if populations with locations below μ(k) –δ2, (δ2 > δ1) are “bad”, a selection procedure is proposed and a sample size is determined so that the probability of omitting a “good” population or selecting a “bad” population is at most 1 – P*.
Some Remarks on the Estimation of the Pair‐Correlation Function for Point Processes
Authors: Doguwa Sani I. , Kukoyi Khalid O. 
Published: May 2007

AbstractDoguwa (1990) proposed kernel based pair‐correlation function estimators for Point processes. These estimators transport a part of the probability mass into the negative or left, to a hard‐core distance. The reflection technique is used to provide an alternative estimator of the pair‐correlation function, which drastically reduces the bias inherent in these estimators for the case of random and clustered point patterns. However, the drastic reduction in bias, is at the cost of a much larger variance.
The Design and Use of Variance Component Models in the Analysis of Human Quantitative Pedigree Data
Authors: Schork Nicholas J. 
Published: May 2007

AbstractThe use of variance components and multivariate linear models in genetics applications has a long history that dates back to (at least) Fisher's seminal 1918 paper “The correlation between relatives on the supposition of Mendelian inheritance” [Phil. Trans. 52: 399–433]. Although extensions and elaborations of Fisher's insights have been offered in recent times, relatively few studies exist which examine the theoretical and operational properties variance component models possess in complicated genetic analysis settings. In this paper variance component models, as well as some of their properties (e.g., power, efficiency, and sample size considerations) are discussed in the context of each of the following genetic analysis settings: 1. the detection of general polygenic additive and dominance effects; 2. the detection of genetic effects in the presence of environmental effects (and vice versa); 3. the detection of pleiotropic gene action; 4. aspects of the detection of genotype by environment interaction; and 5. sequential tests for general hypotheses framed in the context of settings 1 through 4. Exposition of the proposed methods and results are facilitated through a special emphasis placed on pedigree covariance structure modeling.
An Integrated Formulation for Comparing Treatments with Binary Response with a Control
Authors: Hsu Lifang 
Published: May 2007

AbstractThe problem of comparing k(≧2) bernoulli rates of success with a control is considered. An one‐stage decision procedure is proposed for either (1) choosing the best among several experimental treatments and the control treatment when the best is significantly superior or (2) selecting a random size subset that contains the best experimental treatment if it is better than the control when the difference between the best and the remaining treatments is not significant. We integrate two traditional formulations, namely, the indifference (IZ) approach and the subset selection (SS) approach, by seperating the parameter space into two disjoint sets, the preference zone (PZ) and the indifference zone (IZ). In the PZ we insist on selecting the best experimental treatment for a correct selection (CS1) but in the IZ we define any selected subset to be correct (CS2) if it contains the best experimental treatment which is also better than the control. We propose a procedure R to guarantee lower bounds P1* for P(CS1≧PZ) and P2* for P(CS2≧IZ) simultaneously. A brief table on the common sample size and the procedure parameters is presented to illustrate the procedure R.
Partial Distributional Canonical Discriminant Analysis
Authors: Feldmann Uwe 
Published: May 2007

AbstractA canonical discriminant technique is proposed which is unrelated to particular distributional assumptions. This approach allows for a data generated choice between ordered, partially ordered and unordered outcomes and remains identifiable in each dimension of the canonical model. The concepts of allocatability and distinguishability of categories are investigated. Maximum likelihood parameter estimation is conducted and inferential tests for the assessment of the dimensionality of the canonical model are derived. The model is applied to the perioperative prediction of risk of death from peritonitis, using real data.
The Power of the Mantel‐Haenszel Test when Adjustment is by the True or Estimated Propensity Score
Authors: Drake Christiana , McQuarrie Allan 
Published: May 2007

AbstractThe power of the Mantel‐Haenszel test for no treatment effect in the case of binary exposure and response variates was examined through simulation studies when subclasses were formed on the basis of the true and estimated propensity scores and by direct stratification on two continuous covariates. The power of these tests was also compared to the score test in a misspecified logistic regression model. In general adjustment by the true propensity score was most likely to reject a false null hypothesis, the score test was more likely to reject a false null hypothesis than the Mantel‐Haenszel test when adjustment is by the estimated propensity score or subclassification on the covariates. There was litte difference in the observed powers of the Mantel‐Haenszel tests between adjustment by the estimated propensity score and subclassification on the covariates.
Estimation of Several Missing Values in F‐Square Designs
Authors: Subramani J. , Aggarwal M. L. 
Published: May 2007

AbstractThe present study deals with the estimation of several missing values in F‐square designs. The estimating equations for the non‐iterative least squares estimation of Missing Values and explicit expressions for the estimators of the particular patterns of Missing Values are presented. This procedure is illustrated with the help of a numerical example.
Non‐Iterative Least Squares Estimation of Missing Values in Hyper‐Graeco‐Latin Square Designs
Authors: Subramani J. 
Published: May 2007

AbstractIn this paper an attempt has been made to obtain explicit expressions for the estimators of the several missing values in hyper‐graeco‐latin square designs. Further it has been shown that the estimates of the missing values in latin square designs and graeco‐latin square designs are obtained as a particular case of the estimates of the missing values in hyper‐graeco‐latin square designs.
Estimation of Relative Risk in Case Control Studies Through Logistic Regression Analysis
Authors: Aggarwal Abha Rani , Singh Padam 
Published: May 2007

AbstractMultiple logistic regression analysis is used to estimate the relative risk in case control studies. The estimators obtained are valid when disease is rare. In this paper an estimator of relative risk in a case control study has been proposed using logistic regression results when the incidence of disease is not small. The bias of the usual estimator through logistic regression as compared to the new estimator has been worked out. The expression of Mean Square Error of proposed estimator has been derived in situations when the incidence of disease is known exactly as well as when estimated through an independent survey. It has been observed that there is a significant bias using the conventional estimator of relative risk when incidence of disease is high. In such situations the proposed estimator can be used with advantage.
Trunk‐Top Relations in a Siberian Pine Forest
Authors: Gavrikov V. L. , Grabarnik P. Ya. , Stoyan D. 
Published: May 2007

AbstractIn this paper we study relations between trunks and tops of pines in natural Siberian forests. By statistical methods we show that the tops tend to deviate in the direction away from the nearest neighbour tree or from a summary of the trees in a given neighbourhood. In an alternative approach we show that the tops deviate into the direction of the empty space near the trunk position defined by the corresponding Voronoi polygon.
Symmetric Bernoulli Distributions and Generalized Binomial Distributions
Authors: Qu Y. , Greene T. , Piedmonte M. R. 
Published: May 2007

AbstractThe generalized binomial distribution is defined as the distribution of a sum of symmetrically distributed Bernoulli random variates. Several two‐parameter families of generalized binomial distributions have received attention in the literature, including the Polya urn model, the correlated binomial model and the latent variable model. Some properties and limitations of the three distributions are described. An algorithm for maximum likelihood estimation for two‐parameter generalized binomial distributions is proposed. The Polya urn model and the latent variable model were found to provide good fits to sub‐binomial data given by Parkes. An extension of the latent variable model to incorporate heterogeneous response probabilities is discussed.
Asymptotic Confidence Intervals for the Relative Relapse Rate Under Random Censorship
Authors: Hentzschel Jürgen 
Published: May 2007

AbstractIn the present paper we determine asymptotic confidence intervals of relative relapse rates (RRR) in the case of stochastically right censorship. A practical problem which promotes this discussion is the investigation of the efficiency of a test medicine: The quotient of the mortality rates (called the RRR) of two groups of acute leukemia patients is used as its indicator whereby the patients of one group received the remedy. With this task we will pursue a recent paper by Rao et al. (1991) who assumed the Weibull distribution to be the model for relapse times in both groups. Furthermore, the observed samples are stochastically right censored with censoring distributions of Weibull type, too. We construct asymptotic confidence intervals for the distribution parameters on the basis of the ml estimator and a pseudo‐ml estimator from which the intervals for RRR follow. For the benefit of accuracy in the case of finite samples a bias correction is proposed. By means of a numerical example with the Freireich data (Freireich (1964)) the resulting intervals are compared with those of the approximative methods in Rao et al. (1991).
Comparisons of Some Chi‐Squared Goodness‐of‐Fit Test Statistics in Sparse One‐Way Multinomials
Authors: Lawal H. Bayo 
Published: May 2007

AbstractThe behavior of Pearson's X2 test, the likelihood ratio test Y2 and the two of its derivatives, G2 and Gk2, the Freeman‐Tukey test (FT) and the Cressie and Read test Statistic I(2/3) are examined in this study. Estimated attained α levels based on 1000 simulated samples when the approximating distribution is χk‐12, are computed for these tests for the various values of k, n and seven null hypotheses. Results from estimated power computations indicate that none of the test statistics has a clear advantage over any others, and that the choice of which test to use must therefore rest mainly on the performances with regards to the attained α levels when the χ2 approximation is invoked. In this respect, the log‐normal approximation proposed by Lawal and Upton (1980) is strongly recommended. This is closely followed by the I(2/3).
Distribution‐free Estimation of a Monotonic pth Quantile Function
Authors: Strömberg U. 
Published: May 2007

AbstractWhen analyzing biological data sets, a frequent problem is to estimate the pth quantile of a distribution, when that quantile is assumed to depend on a covariate; in the present paper the dependence of the quantile on the covariate is assumed to be monotonic. Some properties of an isotonic pth quantile regression, considered as an estimator of an increasing pth quantile function, are presented.
Efficiency in the Use of Hotellings T2
Authors: Jensen D. R. , Ramirez D. E. 
Published: May 2007

AbstractAlternative scale models are compared with regard to the Pitman efficiency of Hotelling's T2 in tests for means. A basic decomposition identifies subspaces of the parameters in which one model is more efficient than another, or the two models have equal efficiency. Local and global bounds for relative Pitman efficiencies are given with reference to these subspaces. Applications include efficiency comparisons of marginal and conditional tests in a given model, and a comparison of the sensitivities of selected T2 tests regarding psychological profiles in treated and control groups of children.
Spectral Estimation by Variable Span Log Periodogram Smoothing: An Application to Annual Lynx Numbers
Authors: Solow Andrew R. 
Published: May 2007

AbstractSpectral estimation is commonly based on smoothing the periodogram or its logarithm. In such a smoothing exercise, it is not entirely sensible to use the same bandwidth for all frequencies, since the smoothness of the underlying spectrum may vary. This note describes the use of a variable span smoother in estimating the log spectrum. An application to annual lynx data is presented.
An Approximate Solution to the Behrens‐Fisher Problem
Authors: Bhoj Dinesh S. 
Published: May 2007

AbstractAn approximate and practical solution is proposed for the Behrens‐Fisher problem. This solution is compared to the solutions considered by Mehta and Srinivasan (1970) and Welch's (1937) approximate t‐test in terms of the stability of the size and magnitude of the power. It is shown that the stability of the size of the new test is better than that of Welch's t when at least one of the sample sizes is small. When the sample sizes are moderately large or large the sizes and powers of all the recommended tests are almost the same.
Estimating Mean Landmark Triangles
Authors: Stoyan D. , Frenz M. 
Published: May 2007

AbstractThe paper deals with the statistical estimation of mean triangles of landmark data. For the model introduced by Bookstein (1986) three methods of estimating the “ideal” triangle are compared: the maximum likelihood method based on the exact distribution given in Mardia and Dryden (1989a), a moment method and an iterative algorithm yielding a mean triangle in the sense of Fréchet. These methods are compared by Monte Carlo simulation applied also to models with variances greater than those required for Bookstein's normal approximation.
An Estimation Procedure for the Joint Distribution of Spatial Direction and Thickness of Flat Bodies Using Vertical Sections Part I: Theoretical Considerations
Authors: Sandau Konrad 
Published: May 2007

AbstractIn soil micromorphology fissures are considered in vertical sections. To get information about the properties of the soil the joint distribution of spatial direction and width is of interest. The fissures are mathematically generalized to flat bodies which form a stationary weighted surface process with the weight “thickness”. Because of stationarity a joint distribution of spatial direction and thickness exists in a “typical point” of the surface process. A suitable parametric family of distributions is assumed. The corresponding parameters can be estimated from measurements on the vertical sections. But on the sections only the visible thickness and the visible angle of a fissure can be measured. Therefore the joint distribution of these variables is expressed by the joint spatial distribution of spatial direction and thickness. This derived distribution depends on the same parameters. The Chi‐Square method is proposed for the parameter estimation. The estimation procedure is demonstrated using the Bingham‐Mardia distribution for the direction and the lognormal distribution for the thickness and by defining a way to correlate the mean thickness and the direction.
An Estimation Procedure for the Joint Distribution of Spatial Direction and Thickness of Flat Bodies Using Vertical Sections. Part II: An application in soil micromorphology
Authors: Sandau Konrad , Vogel Hans‐Jörg 
Published: May 2007

AbstractIn soil micromorphology fissures are considered in vertical sections. To get information about the properties of the soil the joint distribution of spatial direction and width of these fissures is of interest. The fissures are mathematically generalized to flat bodies which are defined as stationary weighted surface processes with the weight “thickness”. In a typical point of the surface process suitable, joint parametric distributions of direction and thickness are assumed. The parameters have to be estimated from measurements on vertical sections which are taken from the soil. On these sections only a visible thickness and a visible angle can be observed. The joint distribution of these variables can be expressed by the joint distribution of spatial direction and thickness with the same parameters and in this indirect way the parameters can be estimated. The paper describes how to randomize the vertical section and how to measure the visible variables on the sections. The Chi‐Square method is proposed for the parameter estimation. Further it is discussed how to derive good starting values for the numerical procedure. All this is demonstrated in a simulation study using the Bingham‐Mardia distribution for the direction and the lognormal distribution for the thickness including a way to correlate the mean thickness and the direction. Finally an application in soil micromorphology is demonstrated for one soil horizon.
Sample Size Determination for Grouped Exponential Observations: A Cost Function Approach
Authors: Lui Kung‐Jong , Steffey Duane , Pugh Jamie K. 
Published: May 2007

AbstractCalculating the required sample size for a desired power at a given type I error level, we often assume that we know the exact time of all subject responses whenever they occur during our study period. It is very common, however, in practice that we only monitor subjects periodically and, therefore, we know only whether responses occur or not during an interval. This paper includes a quantitative discussion of the effect resulting from data grouping or interval censoring on the required sample size when we have two treatment groups. Furthermore, with the goal of exploring the optimum in the number of subjects, the number of examinations per subject for test responses, and the total length of a study time period, this paper also provides a general guideline about how to determine these to minimize the total cost of a study for a desired power at a given α‐level. A specified linear cost function that incorporates the costs of obtaining subjects, periodic examinations for test responses of subjects, and the total length of a study period, is assumed, primarily for illustrative purpose.
On the Comparison of Umbrella Pattern Treatment Means with a Control Mean
Authors: Chen Y. I. 
Published: May 2007

AbstractThis paper is concerned with comparing several increasing dose levels (treatments) with a zero dose control when the prior information about the umbrella pattern treatment means is available. The problem of testing whether there is at least one treatment which is better than the control is considered. Multiple test procedures are then proposed for deciding treatments (if any) which are better than the control. Some approximate criticial values of the proposed tests are reported. The results of a Monte Carlo power study are presented.
Monte Carlo Investigations for Relative Efficiency of Stratified Random Sampling Scheme for Estimation of Relative Risk in Case‐Control Studies
Authors: Singh Padam , Gupta T. C. 
Published: May 2007

AbstractMonte Carlo Investigations have been widely used in Sample Surveys in Comparing the efficiency of various methods when exact mathematical comparisons are not possible. In this paper the same has been used for comparing the efficiency of Stratified Random Sampling with respect to Simple Random Sampling for estimation of Relative Risk in Case‐Control Studies. The data used relate to a Case Control study on peptic ulcer. On the basis of Monte Carlo Investigations on 50 samples of size 10–20 (Cases and Controls), it has been observed that there is considerable gain in efficiency in using Stratified Random Sampling over Simple Random Sampling. The sensitivity of the results with the change in Sample Size has also been investigated.
A Power Transformation for Generalized Logistic Response Function with Application to Quantal Bioassay
Authors: El‐Saidi Mohammed A. 
Published: May 2007

AbstractIt is known that when the logistic response function is generalized by introducing shape parameters, the usual computational simplicity afforded by statistical softwares such as GLIM and S‐Plus may be lost. This fact is illustrated by Prentice (1976), Brown (1982), Stukel (1985, 1988), and El‐Saidi (1986). In this paper, we consider a power transformation of the generalized logistic model and show how the use of such transformation simplifies the computational difficulties associated with generalized logistic models. Furthermore, applying this technique to some data sets previously analyzed by D'Angio et al. (1981) and Brown (1982) shows an improvement in the fit in comparison to other models such as the logistic, the unstratified multiplicative model GMU and the additive model GA described by Storer et al. (1983).
On Moors' Randomized Response Model
Authors: Mangat N. S. , Singh Ravindra , Singh Sarjinder , Singh Balwant 
Published: May 2007

AbstractThis paper considers a modification of famous unrelated question randomized response model due to Moors' (1971). The estimator based on the proposed strategy is shown to be unbiased and always more efficient than the estimator for Moor's model.