The Experts below are selected from a list of 1506 Experts worldwide ranked by ideXlab platform
Chuang Zheng - One of the best experts on this subject based on the ideXlab platform.
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generalized edge Frequency Polygon for density estimation
Statistics & Probability Letters, 2001Co-Authors: Jianping Dong, Chuang ZhengAbstract:Abstract Jones et al. (Biometrica 85, 235) proposed an edge Frequency Polygon estimator to estimate a probability density function. Their estimator has a smaller asymptotic mean integrated squared error than that of the Frequency Polygon estimator. In this paper we introduce a generalized edge Frequency Polygon estimator. Instead of averaging heights of two bins at each bin edge, we take weighted averages of the heights in the neighboring 2k (k⩾1) bins, which further reduces the asymptotic mean integrated squared error.
Xin Yang - One of the best experts on this subject based on the ideXlab platform.
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the rate of asymptotic normality of Frequency Polygon density estimation for spatial random fields
Open Journal of Statistics, 2018Co-Authors: Shanchao Yang, Xin Yang, Guodong XingAbstract:This paper is to investigate the convergence rate of asymptotic normality of Frequency Polygon estimation for density function under mixing random fields, which include strongly mixing condition and some weaker mixing conditions. A Berry-Esseen bound of Frequency Polygon is established and the convergence rates of asymptotic normality are derived. In particularly, for the optimal bin width , it is showed that the convergence rate of asymptotic normality reaches to when mixing coefficient tends to zero exponentially fast.
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Frequency Polygon estimation of density function for dependent samples
Journal of The Korean Statistical Society, 2015Co-Authors: Xin YangAbstract:The Frequency Polygon, which is a density estimator based on histogram technique, has the advantages of computational simplicity and is widely used in many fields. The purpose of this article is to further investigate the uniform strong consistency of Frequency Polygon under strong mixing samples. Our conclusions make the conditions on mixing coefficient and bin width are more simplified and weaker than those of Carbon et al. (1997).
Jianping Dong - One of the best experts on this subject based on the ideXlab platform.
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generalized edge Frequency Polygon for density estimation
Statistics & Probability Letters, 2001Co-Authors: Jianping Dong, Chuang ZhengAbstract:Abstract Jones et al. (Biometrica 85, 235) proposed an edge Frequency Polygon estimator to estimate a probability density function. Their estimator has a smaller asymptotic mean integrated squared error than that of the Frequency Polygon estimator. In this paper we introduce a generalized edge Frequency Polygon estimator. Instead of averaging heights of two bins at each bin edge, we take weighted averages of the heights in the neighboring 2k (k⩾1) bins, which further reduces the asymptotic mean integrated squared error.
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A minimum variance kernel estimator and a discrete Frequency Polygon estimator for ordinal contingency tables
Communications in Statistics - Theory and Methods, 1996Co-Authors: Jianping DongAbstract:This paper introduces two estimators, a boundary corrected minimum variance kernel estimator based on a uniform kernel and a discrete Frequency Polygon estimator, for the cell probabilities of ordinal contingency tables. Simulation results show that the minimum variance boundary kernel estimator has a smaller average sum of squared error than the existing boundary kernel estimators. The discrete Frequency Polygon estimator is simple and easy to interpret, and it is competitive with the minimum variance boundary kernel estimator. It is proved that both estimators have an optimal rate of convergence in terms of mean sum of squared error, The estimators are also defined for high-dimensional tables.
Pieter H F M Van Casteren - One of the best experts on this subject based on the ideXlab platform.
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the Frequency Polygon reconsidered
Computational Statistics & Data Analysis, 1997Co-Authors: Pieter H F M Van CasterenAbstract:The Frequency Polygon is not compatible with the given Frequency distribution in the sense that the areas within the classes are not proportional to the frequencies. As a consequence, the Polygon is too flat. Therefore, an alternative Polygon is constructed as a continuous version of the histogram by area-matching within each class. The result is a useful tool for visualizing Frequency distributions, which is continuous, compatible with the frequencies and easy to understand.
Jose Munozperez - One of the best experts on this subject based on the ideXlab platform.
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probability density function estimation with the Frequency Polygon transform
Information Sciences, 2015Co-Authors: Ezequiel Lopezrubio, Jose MunozperezAbstract:A probability density function estimator is proposed, which is based on Frequency Polygons.Its convergence to the true density is formally proved.A mode finding algorithm is also proposed, as an alternative to mean-shift.Our approach outperforms histogram and kernel based estimators in synthetic and real datasets.Our proposal is shown to be suitable to object tracking in video sequences. Most current nonparametric approaches to probability density function estimation are based on the kernel density estimator, also known as the Parzen window estimator. A usual alternative is the multivariate histogram, which features a low computational complexity. Multivariate Frequency Polygons have often been neglected, even though they share many of the advantages of the histograms, while they are continuous unlike the histograms. Here we build on our previous work on histograms in order to propose a new probability density estimator which is based on averaging multivariate Frequency Polygons. The convergence of the estimator is formally proved. Experiments are carried out with synthetic and real machine learning datasets. Finally, image denoising and object tracking applications are also considered.