A nonparametric kernel methods is proposed and evaluated performance for estimating annual maximum stream flow quantiles. The bandwidth of the estimator is estimated by using an optimal technique and a cross-validation technique. Results obtained from a limited amount of real data from Malaysia show that quantiles estimated by nonparametric method using these techniques have small root mean squ...

We present an exact test for whether two random variables that have known bounds on their support are negatively correlated. The alternative hypothesis is that they are not negatively correlated. No assumptions are made on the underlying distributions. We show by example that the Spearman rank correlation test as the competing exact test of correlation in nonparametric settings rests on an addi...

Definition A nonparametric procedure is a statistical procedure that has certain desirable properties that hold under relatively mild assumptions regarding the underlying population(s) from which the data are obtained – Hollander and Wolfe. The definition is not only vague but it is also extremely inclusive. “Statistical procedure” can refer to a test or an estimator. There are a number of new ...

We study a semiparametric generalized additive coefficient model, in which linear predictors in the conventional generalized linear models is generalized to unknown functions depending on certain covariates, and approximate the nonparametric functions by using polynomial spline. The asymptotic expansion with optimal rates of convergence for the estimators of the nonparametric part is establishe...

We first note that tests for interaction are missing in virtually all textbooks on nonparametric statistics. We will discuss some reasons why this is so. We then make a case for featuring tests for interaction in the course. By learning how to use median polish and graphical displays students can begin to conceptualize what an interaction means. This will strengthen their understanding of addit...

We develop a novel nonparametric likelihood ratio test for independence between two random variables using a technique that is free of the common constraints of defining a given set of specific dependence structures. Our methodology revolves around an exact density-based empirical likelihood ratio test statistic that approximates in a distribution-free fashion the corresponding most powerful pa...

Testing hypothesis is one of the most important problems in a nonparametric statistic. Various nonparametric test statistics have been proposed and discussed for a long time. We use the exact critical value for testing hypothesis when the sample sizes are small. However, for large sample sizes, it is very difficult to evaluate the exact critical value. Therefore, the limiting distributions of n...

In randomized clinical trials, nonparametric approaches are considered when assumptions of a parametric approach are not reasonable. Nonparametric approaches have typically concentrated on hypothesis testing and, unlike parametric approaches, have not been amenable to providing measures of treatment efficacy. If X and Y denote the random variables representing the responses on two treatments A ...

• In the independent two-sample t-test, we assume normality, independence, and equal variances. • This t-test is robust against nonnormality, but is sensitive to dependence. • If n1 is close to n2, then the test is moderately robust against unequal variance (σ 1 6= σ 2). But if n1 and n2 are quite different (e.g. differ by a ratio of 3 or more), then the test is much less robust. • How to deter...

We consider the decision problem of making causal conclusions from observational data. Typically, using standard matched pairs techniques, there is a source of uncertainty that is not usually quantified, namely the uncertainty due to the choice of the experimenter: two different reasonable experimenters can easily have opposite results. In this work we present an alternative to the standard non...