This paper provided an insight to the effective use of inferential statistics for social and behavioural research. As a theoretical paper, it explored the importance of tests of significance emphasizing the need to use the appropriate test for a particular research. It showed that a parametric test is one that involves the estimation of the value of at list one population parameter and the fact...

A nonparametric model-assisted survey estimator for status estimation based on local polynomial regression is extended to incorporate spatial auxiliary information. Under mild assumptions, this estimator is design-unbiased and consistent. Simulation studies show that the nonparametric regression estimator is competitive with standard parametric techniques when a parametric specification is corr...

This thesis investigates some unaddressed problems in kernel nonparametrichypothesis testing. The contributions are grouped around three main themes:Wild Bootstrap for Degenerate Kernel Tests. A wild bootstrap method for non-parametric hypothesis tests based on kernel distribution embeddings is pro-posed. This bootstrap method is used to construct provably consistent teststh...

Nonparametric statistical methods are useful tools for data analysis when there is reason to believe that the outcome variables of interest may fail certain distributional assumptions required for parametric methods. Variables may be ordered categories in nature and thereby not suitable for analysis methods that assume normally distributed variables, such as t tests or analyses of variance and ...

We develop a novel dimension-reduction approach to consistent checks of parametric regression models when many regressors are present. The principle is to replace the nonparametric alternative by a class of semiparametric alternatives, namely single-index models, that is rich enough to allow detection of any nonparametric alternative. We propose an omnibus test based on the kernel method that p...

The present review introduces nonparametric methods. Three of the more common nonparametric methods are described in detail, and the advantages and disadvantages of nonparametric versus parametric methods in general are discussed.

A system of vector semiparametric nonlinear time series models is studied with possible dependence structures and nonstationarities in the parametric and nonparametric components. The parametric regressors may be endogenous while the nonparametric regressors are strictly exogenous and represent trends. The parametric regressors may be stationary or nonstationary and the nonparametric regressors...

In this paper, a new two-sampling scheme is proposed to construct appropriate confidence intervals for the lower population quantiles. The confidence intervals are determined in the parametric and nonparametric set up and the optimality problem is discussed in each case. Finally, the proposed procedure is illustrated via a real data set. 

global warming has brought about changes in meteorological variable time series. these changes, accompanied with serious human intervention in nature, has resulted in hydrological changes. so the effects can be tracked down by the trend of the time series. the purpose of this study is to analyze and compare the monthly and annual trends of temperature, precipitation as well as stream flow time ...