The Experts below are selected from a list of 255 Experts worldwide ranked by ideXlab platform
Robert M Price - One of the best experts on this subject based on the ideXlab platform.
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statistical inference for a linear function of Medians confidence intervals hypothesis testing and Sample size requirements
Psychological Methods, 2002Co-Authors: Douglas G Bonett, Robert M PriceAbstract:When the distribution of the response variable is skewed, the population Median may be a more meaningful measure of centrality than the population mean, and when the population distribution of the response variable has heavy tails, the Sample Median may be a more efficient estimator of centrality than the Sample mean. The authors propose a confidence interval for a general linear function of population Medians. Linear functions have many important special cases including pairwise comparisons, main effects, interaction effects, simple main effects, curvature, and slope. The confidence interval can be used to test 2-sided directional hypotheses and finite interval hypotheses. Sample size formulas are given for both interval estimation and hypothesis testing problems.
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Estimating the variance of the Sample Median
Journal of Statistical Computation and Simulation, 2001Co-Authors: Robert M Price, Douglas G BonettAbstract:The small-Sample bias and root mean squared error of several distribution-free estimators of the variance of the Sample Median are examined. A new estimator is proposed that is easy to compute and tends to have the smallest bias and root mean squared error.
Douglas G Bonett - One of the best experts on this subject based on the ideXlab platform.
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statistical inference for a linear function of Medians confidence intervals hypothesis testing and Sample size requirements
Psychological Methods, 2002Co-Authors: Douglas G Bonett, Robert M PriceAbstract:When the distribution of the response variable is skewed, the population Median may be a more meaningful measure of centrality than the population mean, and when the population distribution of the response variable has heavy tails, the Sample Median may be a more efficient estimator of centrality than the Sample mean. The authors propose a confidence interval for a general linear function of population Medians. Linear functions have many important special cases including pairwise comparisons, main effects, interaction effects, simple main effects, curvature, and slope. The confidence interval can be used to test 2-sided directional hypotheses and finite interval hypotheses. Sample size formulas are given for both interval estimation and hypothesis testing problems.
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Estimating the variance of the Sample Median
Journal of Statistical Computation and Simulation, 2001Co-Authors: Robert M Price, Douglas G BonettAbstract:The small-Sample bias and root mean squared error of several distribution-free estimators of the variance of the Sample Median are examined. A new estimator is proposed that is easy to compute and tends to have the smallest bias and root mean squared error.
Debapriya Sengupta - One of the best experts on this subject based on the ideXlab platform.
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A note on robust estimation of location
Statistics & Probability Letters, 1993Co-Authors: Probal Chaudhuri, Debapriya SenguptaAbstract:Abstract A modified version of the usual M-estimation problem is proposed, and Sample Median is shown to be a solution of this problem for a wide range of choices of the score function. It exposes certain universality in the robustness of Sample Median in the univariate case, and this property continues to hold even in multivariate set-ups if we consider the multivariate L 1 -Median. Some interesting facts related to this ‘modified M-estimation’ are discussed, and the consequences of a similar modification of the traditional maximum likelihood approach are explored.
Joon-hyuk Chang - One of the best experts on this subject based on the ideXlab platform.
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Robust time-of-arrival source localization employing error covariance of Sample mean and Sample Median in line-of-sight/non-line-of-sight mixture environments
EURASIP Journal on Advances in Signal Processing, 2016Co-Authors: Chee-hyun Park, Joon-hyuk ChangAbstract:We propose a line-of-sight (LOS)/non-line-of-sight (NLOS) mixture source localization algorithm that utilizes the weighted least squares (WLS) method in LOS/NLOS mixture environments, where the weight matrix is determined in the algebraic form. Unless the contamination ratio exceeds 50 %, the asymptotic variance of the Sample Median can be approximately related to that of the Sample mean. Based on this observation, we use the error covariance matrix for the Sample mean and Median to minimize the weighted squared error (WSE) loss function. The WSE loss function based on the Sample Median is utilized when statistical testing supports the LOS/NLOS state, while the WSE function using the Sample mean is employed when statistical testing indicates that the sensor is in the LOS state. To testify the superiority of the proposed methods, the mean square error (MSE) performances are compared via simulation.
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robust time of arrival source localization employing error covariance of Sample mean and Sample Median in line of sight non line of sight mixture environments
EURASIP Journal on Advances in Signal Processing, 2016Co-Authors: Chee-hyun Park, Joon-hyuk ChangAbstract:We propose a line-of-sight (LOS)/non-line-of-sight (NLOS) mixture source localization algorithm that utilizes the weighted least squares (WLS) method in LOS/NLOS mixture environments, where the weight matrix is determined in the algebraic form. Unless the contamination ratio exceeds 50 %, the asymptotic variance of the Sample Median can be approximately related to that of the Sample mean. Based on this observation, we use the error covariance matrix for the Sample mean and Median to minimize the weighted squared error (WSE) loss function. The WSE loss function based on the Sample Median is utilized when statistical testing supports the LOS/NLOS state, while the WSE function using the Sample mean is employed when statistical testing indicates that the sensor is in the LOS state. To testify the superiority of the proposed methods, the mean square error (MSE) performances are compared via simulation.
J.s. Huang - One of the best experts on this subject based on the ideXlab platform.
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Estimating the variance of the Sample Median, discrete case
Statistics & Probability Letters, 1991Co-Authors: J.s. HuangAbstract:Abstract Variance of the Sample Median from discrete distributions is estimated by the bootstrap and by the jackknife methods. For asymmetric Bernoulli distributions, it is shown that both estimators grossly overestimate the true variance, with the bootstrap estimator getting progressively worse than the jackknife as the Sample size gets larger. For the symmetric Bernoulli, the jackknife continues to overestimate the variance, while the bootstrap underestimates, tending to two thirds the true variance in the limit. Numerical evidence suggests that the same limiting behavior is shared by a large class of discrete distributions including the binomial, Poisson and geometric.
