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Kernel Density Estimator

Some Improvement on Convergence Rates of Kernel Density Estimator

Some Improvement on Convergence Rates of Kernel Density Estimator

... two kernel density estimators are introduced and ...ordinary kernel density estimator. The second proposed density estimator is a geometric extrapolation of the first bias ...

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A Berry-Esseen Type Bound for the Kernel Density Estimator of Length-Biased Data

A Berry-Esseen Type Bound for the Kernel Density Estimator of Length-Biased Data

... the density function of a random ...the kernel density estimator of the population density function of such ...the kernel density estimator when the data are ...

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Some Asymptotic Results of Kernel Density 
Estimator in Length-Biased Sampling

Some Asymptotic Results of Kernel Density Estimator in Length-Biased Sampling

... In this paper, we prove the strong uniform consistency and asymptotic normality of the kernel density estimator proposed by Jones [12] for length-biased data.The approach is based on the invariance ...

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Asymptotic Behaviors of Nearest Neighbor Kernel Density Estimator in Left-truncated Data

Asymptotic Behaviors of Nearest Neighbor Kernel Density Estimator in Left-truncated Data

... Kernel density estimators are the basic tools for density estimation in non- parametric ...neighbor kernel estimators represent a special form of kernel density estimators, in ...

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Analytical Plug-In Method for Kernel Density Estimator Applied to Genetic Neutrality Study

Analytical Plug-In Method for Kernel Density Estimator Applied to Genetic Neutrality Study

... The plug-in method enables optimization of the bandwidth of the kernel density estimator in order to estimate probability density functions (pdfs). Here, a faster procedure than that of the ...

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The Berry Esséen bounds for kernel density estimator under dependent sample

The Berry Esséen bounds for kernel density estimator under dependent sample

... the density estimator under a mixing dependent sample and Roussas [] ob- tained the asymptotic normality for the kernel density estimator under an association ...

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A Berry Esseen type bound for the kernel density estimator based on a weakly dependent and randomly left truncated data

A Berry Esseen type bound for the kernel density estimator based on a weakly dependent and randomly left truncated data

... In many applications, the available data come from a sampling scheme that causes loss of information in terms of left truncation. In some cases, in addition to left truncation, the data are weakly dependent. In this ...

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Density Estimation methods based on Mass

Density Estimation methods based on Mass

... conventional kernel density estimator which shows a bias-variance trade off—the bias decreases as the kernel bandwidth b decreases but this increases the variance; and the reverse is true if ...

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Low Complexity Algorithm for Probability Density Estimation Applied in Big Data Analysis

Low Complexity Algorithm for Probability Density Estimation Applied in Big Data Analysis

... probability density estimation methods and affirms that density estimation gives better results than traditional tools of data analysis like Principal Component ...that kernel density with ...

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Density Estimation methods based on Mass

Density Estimation methods based on Mass

... 1708 density-based algorithms, of what previously thought ...new density estimator has the same characteristic as kernel density estimator, ...new density ...

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PERFORMANCE ANALYSIS OF DENSITY ESTIMATION AND MASS ESTIMATION Khushabu Trivedi 1, Parvati Bhurani2

PERFORMANCE ANALYSIS OF DENSITY ESTIMATION AND MASS ESTIMATION Khushabu Trivedi 1, Parvati Bhurani2

... data. Density estimation is the ubiquitous base modelling mechanism employed for many tasks including clustering, classification, anomaly detection and information ...used density estimation methods such as ...

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A sparse kernel density estimation algorithm using forward constrained regression

A sparse kernel density estimation algorithm using forward constrained regression

... sparse kernel density estimator ˆ p(x; g, σ) is sufficiently close to that of the PW density estimator ˆ p(x; g P ar , σ P ar ...

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Probability density function estimation using orthogonal forward regression

Probability density function estimation using orthogonal forward regression

... Outline o Motivations/overview for sparse kernel density estimation o Proposed sparse kernel density estimator: m Convert unsupervised density learning into constrained regression by ado[r] ...

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Minimum Density Hyperplanes

Minimum Density Hyperplanes

... In this subsection we analyse the computational complexity of MDP 2 . At each iteration the algorithm projects the data sample onto v(θ) which involves O(nd) operations. To compute the projection index, φ(θ), we need to ...

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Density Estimation in Infinite Dimensional Exponential Families

Density Estimation in Infinite Dimensional Exponential Families

... Our estimator provides a computationally efficient alternative to maximum likelihood based estimators, which suffer from the computational intractability of the log-partition ...proposed estimator is also ...

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On the convergence rates of kernel estimator and hazard estimator for widely dependent samples

On the convergence rates of kernel estimator and hazard estimator for widely dependent samples

... of estimator of fixed design regression model for WOD ...of kernel density estimator for identically distributed WOD ...recursive kernel estimator based WOD ...

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Bandwidth selection for kernel density estimation with length-biased data

Bandwidth selection for kernel density estimation with length-biased data

... This paper is organised as follows. In Section 2 we develop asymptotic theory for the kernel density estimator of Jones (1991) for length-biased data, and we also define two different consistent ...

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Deconvoluting kernel density estimators

Deconvoluting kernel density estimators

... The first term in (2.6) can be much larger than the variance component of the integrated mean squared error of an ordinary kernel density estimator.. This is the price paid for not measu[r] ...

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Inference for likelihood based estimators of generalized long memory processes

Inference for likelihood based estimators of generalized long memory processes

... CSS estimator, noting two things regarding the GHR ...GHR estimator, the underlying parameter space is ...the estimator unquestionably performs well, this discretization can naturally result in small ...

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nihms935418.pdf

nihms935418.pdf

... one-step estimator yields a larger estimate of the effect size than the generalized Buckley– James estimator, with a slightly larger standard error estimate, resulting in a slightly smaller ...

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