The Experts below are selected from a list of 19203 Experts worldwide ranked by ideXlab platform
Jeffrey L. Solka - One of the best experts on this subject based on the ideXlab platform.
-
A qualitative analysis of the resistive grid Kernel Estimator
Pattern Recognition Letters, 1994Co-Authors: Wendy L. Poston, George W. Rogers, Carey E. Priebe, Jeffrey L. SolkaAbstract:Abstract The ability to estimate a probability density function from random data has applications in discriminant analysis and pattern recognition problems. A resistive grid Kernel Estimator (RGKE) is described which is suitable for hardware implementation. The one-dimensional linear RGKE is compared to a Kernel estimate using Gaussian Kernels, and simulations are presented using both continuous and quantized data. The nonlinear form of the RGKE is shown to have desirable properties, such as the ability to detect discontinuities in the density function.
-
Resistive Grid Kernel Estimator (RGKE)
1992Co-Authors: Wendy L. Poston, George W. Rogers, Carey E. Priebe, Jeffrey L. SolkaAbstract:Abstract : The ability to estimate a probability density function from random data has applications in discriminant analysis and pattern recognition problems. A resistive grid Kernel Estimator (RGKE) is described that is suitable for hardware implementation. The one-dimensional linear RGKE is compared to a Kernel estimate using Gaussian Kernels, and simulations are presented using both continuous and quantized data. The nonlinear form of the RGKE is shown to have desirable properties, such as the ability to detect discontinuities in the density function.
Mohamed Lemdani - One of the best experts on this subject based on the ideXlab platform.
-
asymptotic results for an l 1 norm Kernel Estimator of the conditional quantile for functional dependent data with application to climatology
Sankhya A, 2011Co-Authors: Ali Laksaci, Mohamed Lemdani, Elias Ould SaidAbstract:In this paper, we study an L1-norm Kernel Estimator of the conditional quan- tile (CQ) of a scalar response variable Y given a random variable (rv) X taking values in a semi-metric space. The almost complete (a.co.) consis- tency and the asymptotic normality of this estimate are obtained when the sample is an α-mixing sequence. We illustrate our methodology by applying the Estimator to climatological data.
-
uniform rate of strong consistency for a smooth Kernel Estimator of the conditional mode for censored time series
Journal of Statistical Planning and Inference, 2011Co-Authors: Salah Khardani, Mohamed Lemdani, Elias Ould SaidAbstract:Abstract Let ( T n ) n ⩾ 1 be a sequence random variables (rvs) of interest distributed as T . In censorship models the rv T is subject to random censoring by another rv C . We consider the problem of estimating its conditional mode function, given a vector of covariates X . Let θ ( x ) be the mode of the density of T given X = x . In this paper we consider a Kernel Estimator θ ^ n ( x ) of θ ( x ) and establish its almost sure convergence with rate under an α - mixing condition.
-
some asymptotic properties for a smooth Kernel Estimator of the conditional mode under random censorship
Journal of The Korean Statistical Society, 2010Co-Authors: Salah Khardani, Mohamed Lemdani, Elias Ould SaidAbstract:Abstract Let ( T i ) 1 ≤ i ≤ n be a sample of independent and identically distributed (iid) random variables (rv) of interest and ( X i ) 1 ≤ i ≤ n be a corresponding sample of covariates. In censorship models the rv T is subject to random censoring by another rv C . Let θ ( x ) be the conditional mode function of the density of T given X = x . In this work we define a new smooth Kernel Estimator θ ˆ n ( x ) of θ ( x ) and establish its almost sure convergence and asymptotic normality. An application to prediction and confidence bands is also given. Simulations are drawn to lend further support to our theoretical results for finite sample sizes.
-
a generalized l1 approach for a Kernel Estimator of conditional quantile with functional regressors consistency and asymptotic normality
Statistics & Probability Letters, 2009Co-Authors: Ali Laksaci, Mohamed Lemdani, Elias OuldsaidAbstract:A Kernel Estimator of the conditional quantile is defined for a scalar response variable, given a covariate taking values in a semi-metric space. The approach generalizes the median's L1-norm Estimator. The almost complete consistency and asymptotic normality are stated.
-
A generalized -approach for a Kernel Estimator of conditional quantile with functional regressors: Consistency and asymptotic normality
Statistics and Probability Letters, 2009Co-Authors: Ali Laksaci, Mohamed Lemdani, Elias Ould-saïdAbstract:A Kernel Estimator of the conditional quantile is defined for a scalar response variable given a covariate taking values in a semi-metric space. The approach generalizes the median's -norm Estimator. The almost complete consistency and asymptotic normality are stated.
Ali Laksaci - One of the best experts on this subject based on the ideXlab platform.
-
asymptotic results for an l 1 norm Kernel Estimator of the conditional quantile for functional dependent data with application to climatology
Sankhya A, 2011Co-Authors: Ali Laksaci, Mohamed Lemdani, Elias Ould SaidAbstract:In this paper, we study an L1-norm Kernel Estimator of the conditional quan- tile (CQ) of a scalar response variable Y given a random variable (rv) X taking values in a semi-metric space. The almost complete (a.co.) consis- tency and the asymptotic normality of this estimate are obtained when the sample is an α-mixing sequence. We illustrate our methodology by applying the Estimator to climatological data.
-
a generalized l1 approach for a Kernel Estimator of conditional quantile with functional regressors consistency and asymptotic normality
Statistics & Probability Letters, 2009Co-Authors: Ali Laksaci, Mohamed Lemdani, Elias OuldsaidAbstract:A Kernel Estimator of the conditional quantile is defined for a scalar response variable, given a covariate taking values in a semi-metric space. The approach generalizes the median's L1-norm Estimator. The almost complete consistency and asymptotic normality are stated.
-
A generalized -approach for a Kernel Estimator of conditional quantile with functional regressors: Consistency and asymptotic normality
Statistics and Probability Letters, 2009Co-Authors: Ali Laksaci, Mohamed Lemdani, Elias Ould-saïdAbstract:A Kernel Estimator of the conditional quantile is defined for a scalar response variable given a covariate taking values in a semi-metric space. The approach generalizes the median's -norm Estimator. The almost complete consistency and asymptotic normality are stated.
Wendy L. Poston - One of the best experts on this subject based on the ideXlab platform.
-
A qualitative analysis of the resistive grid Kernel Estimator
Pattern Recognition Letters, 1994Co-Authors: Wendy L. Poston, George W. Rogers, Carey E. Priebe, Jeffrey L. SolkaAbstract:Abstract The ability to estimate a probability density function from random data has applications in discriminant analysis and pattern recognition problems. A resistive grid Kernel Estimator (RGKE) is described which is suitable for hardware implementation. The one-dimensional linear RGKE is compared to a Kernel estimate using Gaussian Kernels, and simulations are presented using both continuous and quantized data. The nonlinear form of the RGKE is shown to have desirable properties, such as the ability to detect discontinuities in the density function.
-
Resistive Grid Kernel Estimator (RGKE)
1992Co-Authors: Wendy L. Poston, George W. Rogers, Carey E. Priebe, Jeffrey L. SolkaAbstract:Abstract : The ability to estimate a probability density function from random data has applications in discriminant analysis and pattern recognition problems. A resistive grid Kernel Estimator (RGKE) is described that is suitable for hardware implementation. The one-dimensional linear RGKE is compared to a Kernel estimate using Gaussian Kernels, and simulations are presented using both continuous and quantized data. The nonlinear form of the RGKE is shown to have desirable properties, such as the ability to detect discontinuities in the density function.
Elias Ouldsaid - One of the best experts on this subject based on the ideXlab platform.
-
a generalized l1 approach for a Kernel Estimator of conditional quantile with functional regressors consistency and asymptotic normality
Statistics & Probability Letters, 2009Co-Authors: Ali Laksaci, Mohamed Lemdani, Elias OuldsaidAbstract:A Kernel Estimator of the conditional quantile is defined for a scalar response variable, given a covariate taking values in a semi-metric space. The approach generalizes the median's L1-norm Estimator. The almost complete consistency and asymptotic normality are stated.
