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Multivariate kernel density estimation

Kernel density estimation on the torus

Kernel density estimation on the torus

... variables with simple transformations such as taking the day of the year and multiplying by 2π/365, or taking month of the year and multiplying by 2π/12. Again in metereology, parametric families of multivariate ...

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Using conditional kernel density estimation for wind power density forecasting

Using conditional kernel density estimation for wind power density forecasting

... in density forecasts generated from GARCH models (Reeves 2005; Pascual et ...the multivariate GARCH model in this ...producing density forecasts from our VARMA-GARCH model, we did not account for the ...

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Bandwidth selection for kernel density estimation with length-biased data

Bandwidth selection for kernel density estimation with length-biased data

... the estimation of the mean is √ n−consistent. Density estimation for this type of data started in the 80’s when Bhattacharyya and Richardson (1988) defined the first density estimator for ...

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New Gradient Methods for Bandwidth Selection in Bivariate Kernel Density Estimation

New Gradient Methods for Bandwidth Selection in Bivariate Kernel Density Estimation

... the kernel density estimates (graphs) is the bimodality of the data but this is hidden as presented by the ...most multivariate statistical analysis, the data should be standardized in order to make ...

8

Adaptive Minimum Bit Error Rate Beamforming

Adaptive Minimum Bit Error Rate Beamforming

... or kernel density estimation technique [20]–[22] is adopted for approximating the probability density function (pdf) of the beamformer’s output, and a block-data adaptive MBER algorithm is ...

8

Regularized Data-Based Nonparametric Filtration of Stochastic Signals

Regularized Data-Based Nonparametric Filtration of Stochastic Signals

... estimate the derivative and density separately. For large n , i.e., for a long realization of the sequence ( X n ) , a dimension of the multivariate density in (10) is very high. Therefore, taking ...

5

California’s Citizen Redistricting Commission, Electoral Competitiveness and Political Polarization

California’s Citizen Redistricting Commission, Electoral Competitiveness and Political Polarization

... The regression model is now used to simulate the effect of the CRC’s redistricting plan on political polarization. To conduct the simulations, the fitted values from the regression are obtained and the distribution of ...

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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 ...

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A sparse kernel density estimation algorithm using forward constrained regression

A sparse kernel density estimation algorithm using forward constrained regression

... probability density function (pdf) from observed data samples ...probability density function estimation is the finite mixture model ...sparse density estimators include the support vector ...

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Kernel Density Estimation of Tropical Cyclone Frequencies in the North Atlantic Basin

Kernel Density Estimation of Tropical Cyclone Frequencies in the North Atlantic Basin

... Previous research has identified specific areas of frequent tropical cyclone activity in the North Atlantic ba- sin. This study examines long-term and decadal spatio-temporal patterns of Atlantic tropical cyclone fre- ...

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Nonparametric Identification of the Production Functions

Nonparametric Identification of the Production Functions

... Abstract—A class of semi-recursive kernel plug-in estimates of functions depending on multivariate density functionals and their derivatives is considered. The approach enables to estimate the ...

5

Bias reduction in kernel density estimation via Lipschitz condition

Bias reduction in kernel density estimation via Lipschitz condition

... results on its uniform consistency, asymptotic normality and convergence rates. We emphasize that this is the main theoretical advantage of our estimator. Its rates of convergence are true for all bandwidths and sample ...

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Uncovering Causality from Multivariate Hawkes Integrated Cumulants

Uncovering Causality from Multivariate Hawkes Integrated Cumulants

... a multivariate Hawkes ...an estimation of this matrix without any parametric modeling and estimation of the kernels ...an estimation of causality relationships between nodes (or users), based ...

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Sample size and the multivariate kernel density likelihood ratio : how many speakers are enough?

Sample size and the multivariate kernel density likelihood ratio : how many speakers are enough?

... the multivariate kernel density LR approach, these data were used to investigate: (1) the number of development (training) speakers required for adequate calibration, (2) the number of test speakers ...

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Adaptive Minimum Bit Error Rate Beamforming Assisted Receiver for QPSK Wireless Communication

Adaptive Minimum Bit Error Rate Beamforming Assisted Receiver for QPSK Wireless Communication

... This paper considers interference limited communication systems where the desired user and in- terfering users are symbol-synchronized. A novel adaptive beamforming technique is proposed for quadrature phase shift keying ...

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Adaptive bandwidth kernel density estimation for next generation sequencing data

Adaptive bandwidth kernel density estimation for next generation sequencing data

... KDE-based density estimates can be thought of as denoting the probability of finding a read at a given base pair ...the density esti- mates obtained by these algorithms are in general smoother and more ...

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Aggregating Density Estimators: An Empirical Study

Aggregating Density Estimators: An Empirical Study

... Very few developments exist for ensemble learning in the unsupervised framework, clustering analysis and den- sity estimation. Our work concerns the latter case which may be seen as a fundamental problem in ...

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Nonparametric estimation of time varying covariance matrix in a slowly changing vector random walk model

Nonparametric estimation of time varying covariance matrix in a slowly changing vector random walk model

... parametric multivariate GARCH (generalized ARCH) models for condi- tional covariance matrix (see ...metric estimation of conditional covariance matrix (H¨ ardle et ...the estimation of covariance ...

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Anomaly detection using local kernel density estimation and context based regression

Anomaly detection using local kernel density estimation and context based regression

... local density-based anomaly detection methods are limited in that the local density estimation and the neighborhood density estimation are not accurate enough for complex and large ...

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Problems in density estimation for independent and dependent data

Problems in density estimation for independent and dependent data

... By choosing different values of the parameters a and ß we can generate a wide range of stochastic processes representing varying degrees of roughness. Variance functions with values of a = 0.6, 1.0,1.95 and ß = ...

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