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The International Journal of Biostatistics, 2009
In multiple simultaneous hypothesis testing (MSHT), a significance thresholding function as a scalar statistic can be designed in an adaptive manner by sharing information among many tests performed simultaneously. By using such an adapted statistic, MSHT has greater detection power than tests using simple individual statistics.
Shigeyuki Oba, Shin Ishii
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In multiple simultaneous hypothesis testing (MSHT), a significance thresholding function as a scalar statistic can be designed in an adaptive manner by sharing information among many tests performed simultaneously. By using such an adapted statistic, MSHT has greater detection power than tests using simple individual statistics.
Shigeyuki Oba, Shin Ishii
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Non-parametric Hypothesis Testing and Confidence Intervals with Doubly Censored Data
Lifetime Data Analysis, 2003The non-parametric maximum likelihood estimator (NPMLE) of the distribution function with doubly censored data can be computed using the self-consistent algorithm (Tumbull, 1974). We extend the self-consistent algorithm to include a constraint on the NPMLE.
Chen, Kun, Zhou, Mai
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The fusion of parametric and non-parametric hypothesis tests
Sixth International Conference of Information Fusion, 2003. Proceedings of the, 2003This paper considers the hypothesis testing problem when two sets of data having significantly different types of prior information are fused. The probability density function of the data in the first set is assumed to be known to within a finite set of parameters so that aparometric test can be used to test the hypothesis.
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Hypothesis Testing and Nonparametric Test
2018It is often required to make some inferences about some parameter of the population on the basis of available data. Such inferences are very important in hydrology and hydroclimatology where the available data is generally limited. This is done through hypothesis testing.
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Using non-parametrics to inform parametric tests of Kuznets' hypothesis
Applied Economics Letters, 2001Simon Kuznets hypothesized that inequality in a country's distribution of income worsens in the early stages of its economic development and that the inequality improves as the country reaches higher stages of development (the ‘inverted U hypothesis’). Empirical support for the inverted U hypothesis has been mixed.
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Nonparametric Hypothesis Testing with Parametric Rates of Convergence
International Economic Review, 1991Nonparametric estimators are frequently criticized for their poor performance in small samples. In this paper, the author considers using kernel methods for the estimation of the expected derivatives of a regression function. The proposed estimators are shown to be asymptotically normal and n-consistent.
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A Bayesian alternative to parametric hypothesis testing
Test, 1992zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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Non-parametric hypothesis testing procedures and applications to demand analysis
Journal of Econometrics, 1985This paper proposes a hypothesis test that a (possibly vector-valued) regression function g lies in a particular family of functions \({\mathcal F}\), not necessarily a finite-dimensional parametric family, where \({\mathcal F}\) is a compact subset of an appropriate topological space of continuous functions.
Epstein, Larry G., Yatchew, Adonis J.
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On basic life testing issues in medical research using non‐parametric hypothesis testing
Quality and Reliability Engineering International, 2023AbstractThe probability life distributions usually describe the time to event or survival time. Therefore, these life distributions play a crucial role in the analysis and projection of the maximum life expectancy using the Laplace transform technique for nonparametric hypothesis testing.
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