Results 221 to 230 of about 43,974 (259)

Bayesian nonparametric hypothesis testing for longitudinal data analysis

Computational Statistics & Data Analysis, 2023
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Luz Adriana Pereira, Luis Gutiérrez, Daniel Taylor-Rodríguez, Ramsés H. Mena   +3 more
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Nonparametric hypothesis testing in clustered survival model

Communications in Statistics - Simulation and Computation, 2017
A nonparametric test for the presence of clustering in survival data is proposed. Assuming a model that incorporates the clustering effect into the Cox Proportional Hazards model, simulation studie...
John de Guzman Eustaquio, Erniel B. Barrios   +1 more
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Nonparametric Hypothesis Testing

2014
A novel presentation of rank and permutation tests, with accessible guidance to applications in R. Nonparametric testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. This book summarizes traditional rank techniques and more recent developments in permutation testing as robust
BONNINI, Stefano, CORAIN, Livio, MAROZZI, Marco, SALMASO, Luigi   +3 more
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Nonparametric Hypothesis Testing for a Spatial Signal

Journal of the American Statistical Association, 2002
Summary form only given. Nonparametric hypothesis testing for a spatial signal can involve a large number of hypotheses. For instance, two satellite images of the same scene, taken before and after an event, could be used to test a hypothesis that the event has no environmental impact.
Shen, Xiaotong, Huang, Hsincheng, Cressie, Noel A   +2 more
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Nonparametric Hypothesis Tests

2016
The parametric methods of the previous chapter required data measured at the interval or ratio levels and which is normally distributed. Business data are not always at these levels of measurement. Market research regularly produces data at the nominal (e.g. “agree” versus “disagree” with a proposition about a product) and ordinal (e.g.
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Nonparametric Hypothesis Testing with Parametric Rates of Convergence

International Economic Review, 1991
Nonparametric 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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Hypothesis Testing and Nonparametric Test

2018
It 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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Nonparametric Estimation and Hypothesis Testing in Econometric Models

Empirical Economics, 1988
In this paper we systematically review and develop nonparametric estimation and testing techniques in the context of econometric models. The results are discussed under the settings of regression model and kernel estimation, although as indicated in the paper these results can go through for other econometric models and for the nearest neighbor ...
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A General Approach to Hypothesis Testing for Nonparametric Tests

The Journal of Experimental Education, 1990
Researchers have preferred normal-theory tests over nonparametric procedures, despite evidence that the statistical properties of the latter are sometimes superior for variables and subject populations frequently encountered in educational and psychological research.
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Support Vector Machine for Nonparametric Binary Hypothesis Testing

1999
The Support Vector Machine, introduced in [1] as a practical implementation of the principle of structural risk minimization, constitutes one of the most promising methods for constructing a mathematical model only on the base of a limited amount of measured data. In this paper, we consider the application of this method to the problem of nonparametric
MATTERA D., PALMIERI, Francesco
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