Results 331 to 340 of about 3,403,709 (354)

Bandwidth selection for kernel binomial regression

Journal of Nonparametric Statistics, 2006
In nonparametric binomial regression, the weighted kernel estimator of the regression function and its efficient bias-adjusted version have been proposed by Okumura and Naito (2004) with consideration to differences of variances of observed response proportions at covariates.
Hidenori Okumura, Kanta Naito
openaire   +1 more source

Bayesian sampling for bandwidth selection

2017
This thesis aims to investigate three main topics, which are the density-based multiple variance ratio test, Bayesian estimation of partially linear models, and Bayesian estimation of Tobit models. All three topics involve a kernel density estimator whose performance is mainly determined by the choice of bandwidth.
openaire   +1 more source

Efficient Bandwidth Selection in Non‐parametric Regression

Scandinavian Journal of Statistics, 2003
In this paper we use non‐parametric local polynomial methods to estimate the regression function, m(x). Y may be a binary or continuous response variable, and X is continuous with non‐uniform density. The main contributions of this paper are the weak convergence of a bandwidth process for kernels of order (0,k), k=2j, j≥1 and the proposal of a local ...
openaire   +2 more sources

Bandwidth Selection in Density Estimation

1995
The motivation for density estimation in statistics and data analysis is to realize where observations occur more frequently in a sample. The aim of density estimation is to approximate a “true” probability density function f(x) from a sample information {X i }n i=1 of independent and identically distributed observations.
openaire   +1 more source

Local Bandwidth Selection for Kernel Estimates

Journal of the American Statistical Association, 1989
Abstract A kernel estimate of a curve that uses an adaptive procedure for local selection of the bandwidth is considered here. A two-step procedure is proposed for estimating the local bandwidth that minimizes the mean squared error (MSE) of a kernel estimator for nonparametric regression.
openaire   +1 more source

Adaptive Configuration Selection and Bandwidth Allocation for Edge-Based Video Analytics

IEEE/ACM Transactions on Networking, 2022
Shenmin Zhang, Can Wang, Yibo Jin, Jie Wu, Zhuzhong Qian, Mingjun Xiao, Sanglu Lu   +6 more
semanticscholar   +1 more source

Optimal rates for local bandwidth selection

Journal of Nonparametric Statistics, 1996
The problem of bandwidth selection for kernel density estimation at a point is considered. Asymptotic lower bounds are established for the relative rate of convergence of data-driven bandwidth selectors to their optimal values. It is noted that some existing methods of local bandwidth selection, using high order kernel functions, attain these rates ...
openaire   +1 more source

Local Bandwidth Selection for Density Estimation

1992
The problem of choosing a local value of the bandwidth h for a kernel density estimate is considered. Estimates of the density f at a given point are needed in the estimation of the asymptotic standard error or sample quantiles and in some kernel regression estimators based on random design points.
Lori A. Thombs, Simon J. Sheather
openaire   +1 more source

A reliable data-based bandwidth selection method for kernel density estimation

, 1991
S. Sheather, M. C. Jones
semanticscholar   +1 more source

Speech Bandwidth Compression through Spectrum Selection

The Journal of the Acoustical Society of America, 1960
PB word and sentence intelligibility tests were conducted with unfiltered speech and with speech filtered (1) by a 100–7000 cps bandpass filter, (2) by a 100–1600 cps bandpass filter, (3) by a 500 2000 cps bandpass filter, (4) by a 1000–2500 cps bandpass filter, and (5) by various configurations of one, two, or three bandpass filters, each 500 cps wide.
openaire   +1 more source