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Statistical tests (part 3): non-parametric tests
Nursing Standard, 1993The previous two articles in this series (1,2) have considered in some detail the descriptive and parametric approaches to statistical analysis. This final article now considers principally the non-parametric approach to statistical analysis and, briefly, looks at the concepts of dimension reduction, factor analysis and confidence limits.
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Bootstrap non-parametric significance test
Journal of Nonparametric Statistics, 2007In this paper, we consider the problem of testing the significance of covariates in a nonparametric regression model. We propose to use some bootstrap procedures to better approximate the finite sample distribution of the test statistics. We establish the asymptotic validity of the proposed bootstrap procedures.
Jingping Gu, Dingding Li, Dandan Liu
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1980
The corpus of theory on non-parametric approaches to outlier testing is not large, and given the strongly parametric nature of the outlier model, this is really not surprising. Most of the results that there are relate to slippage rather than outlier problems.
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The corpus of theory on non-parametric approaches to outlier testing is not large, and given the strongly parametric nature of the outlier model, this is really not surprising. Most of the results that there are relate to slippage rather than outlier problems.
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1977
In the last chapter we considered a number of parametric hypotheses where a basic distribution was assumed and the hypotheses were on the parameters of this basic distribution. In this chapter we will consider some tests which will not be of this nature.
A. M. Mathai, P. N. Rathie
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In the last chapter we considered a number of parametric hypotheses where a basic distribution was assumed and the hypotheses were on the parameters of this basic distribution. In this chapter we will consider some tests which will not be of this nature.
A. M. Mathai, P. N. Rathie
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Multivariate tri-aspect non-parametric testing
Journal of Nonparametric Statistics, 2007Permutation tests are prized for their lack of assumptions concerning distribution of underlying populations. The (usual) permutation test for the two-sample location problem based on comparison of sample means is generally effective with regular, roughly symmetric, unimodal, and light-tailed distributions, whereas it might not be so with highly ...
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Non-Parametric Tests of Consumer Behaviour
The Review of Economic Studies, 1983Summary: This paper shows how to test demand data for consistency with maximization, homotheticity, various forms of separability, and a rationing model without making any assumptions concerning the parametric form of underlying demand or utility functions.
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Exact Non-Parametric Significance Tests
1988A very rich class of non-parametric two-sample tests are of the form $$d(x) = \sum\limits_{i = 1}^k {{a_i}({m_{i - 1}},{x_i})} $$ where the xi’s are the entries in row 1 of the 2xk contingency table x: Open image in new window mi = x1+ X2+...+xi’ and ai(mi-1, xi) is a real valued function. An important special case arises when ai(mi-1,
Patel, N. R. +2 more
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Non-parametric hypothesis tests
1989There are some hypothesis tests which do not require as many assumptions as the tests described in Chapter 10. However, these ‘non-parametric’ tests are less powerful than the corresponding ‘parametric’ tests, that is we are less likely to reject the null hypothesis and hence accept the alternative hypothesis, when the alternative hypothesis is correct.
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