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Showing 1 to 6 of 6 for “"Nonparametric density estimation"”.
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A Mixture-based Framework for Nonparametric Density Estimation
… is to provide a mixture-based framework for nonparametric density estimation. This framework advocates the use of a mixture model with a nonparametric mixing distribution to approximate the distribution of the data. The implementation of a mixture-based nonparametric density estimator …
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Least squares mixture decomposition estimation
… Mixture Decomposition Estimator (LSMDE) is a new nonparametric density estimation technique developed by modifying the ordinary kernel density estimators. While the ordinary kernel density estimator assumes equal weight (l/<i>n</i>) for each data point, LSMDE assigns the optimized weight to each …
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Markov Chain Marginal Bootstrap for Generalized Estimating Equations
… matrix may depend on unknown error density functions. Direct estimation of this matrix can be difficult and unreliable since it depends quite heavily on the nonparametric density estimation. Resampling methods provide an alternative way for estimating the variance of the regression …
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Nonparametric Modelling for Directional Data
… is provided. In this thesis, we present density estimators for the above three categories of directional observations under the framework of mixture models. Density estimation is a vital aspect in data analysis. It examines important properties for a random variable including …
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Computational knowledge discovery techniques and their application to options market databases
… testing. A contribution to the field of nonparametric density estimation is made, by an application of neural nets to the recovery of risk-neutral distributions from put option prices. The findings are also new contributions for finance. Finally, in a discussion of software implementation …
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Fisher Information Test of Normality
… utilizes the above property by finding that density of maximum likelihood constrained on having the expected Fisher Information under normality based on the sample variance. The test statistic is then constructed as a ratio of the resulting likelihood against that of normality. Since the …