Optimal smoothing parameters for multivariate fized and adaptive kernel methods

Except in special cases optimum smoothing parameters of kernel methods are difficult to obtain for small samples, and large sample results are often used. Simulation is used to obtain finite sample optimum smoothing parameters and mean integrated square errors for the bivariate normal density. For this example, comparison is made of finite and asymptotic results, and of fixed and adaptive kernel methods. Further comparisons are made of fixed and adaptive methods by considering four other different types of density. Finally, some examples are given.     

A minimum variance kernel estimator and a discrete frequency polygon estimator for ordinal contingency tables

This paper introduces two estimators, a boundary corrected minimum variance kernel estimator based on a uniform kernel and a discrete frequency polygon estimator, for the cell probabilities of ordinal contingency tables. Simulation results show that the minimum variance boundary kernel estimator has a smaller average sum of squared error than the existing boundary kernel estimators. The discrete frequency polygon estimator is simple and easy to interpret, and it is competitive with the minimum variance boundary kernel estimator. It is proved that both estimators have an optimal rate of convergence in terms of mean sum of squared error, The estimators are also defined for high-dimensional tables.     

Location and scale parameter estimation from randomly censored data

The problem of location and scale parameter estimation from randomly censored data is analyzed through use of a regression model for the Kaplan-Meier quantlle process. Continuous time regression techniques are employed to construct estimators that are both asymptotically normal and efficient. Estimators with a particularly simple form are obtained for the Koziol-Green model for random censorship. In the event of no censoring the regression model, and resulting estimators, reduce to those proposed by Parzen (1979 a, b).     

An application of lomax distributions in receiver operating characteristic(roc)curve analysis

Receiver operating characteristic(ROC)curves are useful for studying the performance of diagnostic tests. ROC curves occur in many fields of applications including psychophysics, quality control and medical diagnostics. In practical situations, often the responses to a diagnostic test are classified into a number of ordered categories. Such data are referred to as ratings data. It is typically assumed that the underlying model is based on a continuous probability distribution. The ROC curve is then constructed from such data using this probability model. Properties of the ROC curve are inherited from the model. Therefore, understanding the role of different probability distributions in ROC modeling is an interesting and important area of research. In this paper the Lomax distribution is considered as a model for ratings data and the corresponding ROC curve is derived. The maximum likelihood estimation procedure for the related parameters is discussed. This procedure is then illustrated in the analysis of a neurological data example.     

Unbiased estimation in type II censored samples from a one-truncation parameter density

We consider uniform minimum variance unbiased estimation of a U-estimable function when the sample is (singly) Type II censored and comes from a one-truncation parameter density f(x;θ) = h(x) q(θ). An explicit expression for the estimator is derived. Shortest length confidence interval for q(θ) is obtained.     

Quality of Fit Measures in the Framework of Quantile Regression

Abstract. In regression experiments, to learn about the strength of the relationship between a covariate vector and a dependent variable, we propose a ‘coefficient of determination’ based on the quantiles. Such a coefficient is a ‘local’ measure in the sense that the strength is measured at a prespecified quantile level. Once estimated, it can be used, for example, to measure the relative importance of a subset of covariates in the quantile regression context. Related to this coefficient, we also propose a new ‘local’ lack‐of‐fit measure of a given parametric model. We provide some asymptotic results of the proposed measures and carry out a Monte Carlo simulation study to illustrate their use and performance in practice.     

Estimates of the parameters of type-II extreme value distribution from censored samples

Maximum likelihood estimators of a Type-II extreme value distribution are derived from doubly censored samples. The asymptotic variances and covariances of the maximum likelihood estimators are discussed and these are numerically evaluated for different censoring proportions q₁ = 0.0(0. l) (0.9) from below and q₂ = 0.0 (0. l) (0.9- q₁) from above. The asymptotic relative efficiencies of the parameter estimates revealed that lower order statistics are more important for estimating the parameters of Type-II extreme value distribution as compared to higher order statistics.     

On accelerating sequential procedures for estimating exponential populations

In this study, we accelerate the purely sequential procedure due to Anscombe(1953), Chow and Robbins(1965) to reduce the number of sampling operations required to carry out the estimation process. The method is proposed while estimating the location parameter(s) of the exponential distribution(s). We also develop theory for the asymptotic characteristic of the associated stopping variables. Our findings are applicable to both point as well as confidence interval estimation problems. Other interesting results are also given.     

Robust statistics for testing equality of means or variances

For testing the equality of means (location parameters) of two populations, Tiku (1980a) defined a statistic Tc (based on symmetrically censored samples) and showed that this statistic is robust to underlying populations and is also remarkably powerful. In this paper, we define a similar statistic T (based on samples s with observations censored only on one side) and show that this stat is tic is more powerful than T and nonparametric statistics, C for skew populations. We also provide a modification of this statistic for testing the equality of two population variances.     

Infant feeding and post-neonatal mortality in Derbyshire,England, in the early twentieth century

This paper examines influences on post-neonatal mortality in Derbyshire (England) in the early twentieth century, by applying multivariate hazard analysis to a rare individual-level data set. The data allow detailed patterns of breastfeeding and weaning to be examined. The role of feeding is given special attention as a mediator between mortality and the other environmental, social, and demographic factors considered. Twins and illegitimate children were more likely to have been hand-fed, but this could explain only a small fraction of their increased vulnerability. Artificial feeding was associated with increased risks of death from diarrhoea, respiratory disease, and wasting diseases. It is suggested that the link with wasting diseases was predominantly the result of the greater likelihood of congenitally weak children being hand-fed. Most of the variation in post-neonatal mortality, particularly from respiratory disease, was explained by environmental influences - population density, altitude, and the presence of mining.