The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform

D. E. Ramirez - One of the best experts on this subject based on the ideXlab platform.

  • Noncentralities Induced in Regression Diagnostics
    Journal of Statistical Theory and Practice, 2014
    Co-Authors: D. R. Jensen, D. E. Ramirez
    Abstract:

    Anomalies persist in the use oF deletion diagnostics in regression. Tests For outliers under subset deletions utilize the R -Fisher F _ I statistics, each having a noncentral F -Distribution with noncentrality parameter λ as a Function oF shiFts only at deleted rows in the index set I . Numerous studies examine empirical outcomes oF these diagnostics in random experiments. In contrast, studies here are probabilistic, examining Distributions behind those empirical outcomes and tracking the eFFects oF shiFts at nondeleted rows. By allowing shiFts at nondeleted rows in a set J , in addition to traditional shiFts at deleted rows in I , F _ I is shown to have a doubly noncentral F -Distribution. By removing the unnecessary restriction that shiFts occur only at deleted rows, these Findings support constructs akin to power curves in tracking probabilities oF masking or swamping as shiFts evolve. In addition, “regression eFFects” among outliers may have unForeseen consequences. A dichotomy oF shiFts is discovered as projections into the “regressor” and “error” spaces oF a model. Hidden shiFts at nondeleted rows can obFuscate not only meanings ascribed to traditional outlier diagnostics, but also to subset inFluence diagnostics corresponding one-to-one with F _ I . In short, despite wide usage abetted by soFtware support, deletion diagnostics in current vogue no longer can be recommended to achieve objectives traditionally cited. Case studies illustrate the debilitating eFFects oF these anomalies in practice, together with conclusions misleading to prospective users.

  • noncentralities induced in regression diagnostics
    Journal of statistical theory and practice, 2014
    Co-Authors: D. R. Jensen, D. E. Ramirez
    Abstract:

    Anomalies persist in the use oF deletion diagnostics in regression. Tests For outliers under subset deletions utilize the R–Fisher statistics, each having a noncentral F-Distribution with noncentrality parameter as a Function oF shiFts only at deleted rows in the index set I. Numerous studies examine empirical outcomes oF these diagnostics in random experiments. In contrast, studies here are probabilistic, examining Distributions behind those empirical outcomes and tracking the eFFects oF shiFts at nondeleted rows. By allowing shiFts at nondeleted rows in a set J, in addition to traditional shiFts at deleted rows in I, is shown to have a doubly noncentral F-Distribution. By removing the unnecessary restriction that shiFts occur only at deleted rows, these Findings support constructs akin to power curves in tracking probabilities oF masking or swamping as shiFts evolve. In addition, “regression eFFects” among outliers may have unForeseen consequences. A dichotomy oF shiFts is discovered as projections into...

Augustine C. M. Wong - One of the best experts on this subject based on the ideXlab platform.

  • A Chi-Square Approximation For the F Distribution
    Open Journal of Statistics, 2018
    Co-Authors: L. Jiang, Augustine C. M. Wong
    Abstract:

    F Distribution is one oF the most Frequently used Distributions in statistics. For example, it is used For testing: equality oF variances oF two independent normal Distributions, equality oF means in the one-way ANOVA setting, overall signiFicance oF a normal linear regression model, and so on. In this paper, a simple chi-square approximation For the cumulative Distribution oF the F-Distribution is obtained via an adjusted log-likelihood ratio statistic. This new approximation exhibits remarkable accuracy even when the degrees oF Freedom oF the F Distribution are small.

  • Approximating the F Distribution via a general version oF the modiFied signed log-likelihood ratio statistic
    Computational Statistics & Data Analysis, 2008
    Co-Authors: Augustine C. M. Wong
    Abstract:

    A simple normal approximation For the cumulative Distribution Function oF the F Distribution is obtained via a general version oF the modiFied signed log-likelihood ratio statistic. This approximation exhibits remarkable accuracy even when the degrees oF Freedom are small. Using the same methodology, but with a simpler set up, simple and accurate normal approximations to the cumulative Distribution Functions oF the Student t and @g^2 Distributions can also be obtained.

D. R. Jensen - One of the best experts on this subject based on the ideXlab platform.

  • Noncentralities Induced in Regression Diagnostics
    Journal of Statistical Theory and Practice, 2014
    Co-Authors: D. R. Jensen, D. E. Ramirez
    Abstract:

    Anomalies persist in the use oF deletion diagnostics in regression. Tests For outliers under subset deletions utilize the R -Fisher F _ I statistics, each having a noncentral F -Distribution with noncentrality parameter λ as a Function oF shiFts only at deleted rows in the index set I . Numerous studies examine empirical outcomes oF these diagnostics in random experiments. In contrast, studies here are probabilistic, examining Distributions behind those empirical outcomes and tracking the eFFects oF shiFts at nondeleted rows. By allowing shiFts at nondeleted rows in a set J , in addition to traditional shiFts at deleted rows in I , F _ I is shown to have a doubly noncentral F -Distribution. By removing the unnecessary restriction that shiFts occur only at deleted rows, these Findings support constructs akin to power curves in tracking probabilities oF masking or swamping as shiFts evolve. In addition, “regression eFFects” among outliers may have unForeseen consequences. A dichotomy oF shiFts is discovered as projections into the “regressor” and “error” spaces oF a model. Hidden shiFts at nondeleted rows can obFuscate not only meanings ascribed to traditional outlier diagnostics, but also to subset inFluence diagnostics corresponding one-to-one with F _ I . In short, despite wide usage abetted by soFtware support, deletion diagnostics in current vogue no longer can be recommended to achieve objectives traditionally cited. Case studies illustrate the debilitating eFFects oF these anomalies in practice, together with conclusions misleading to prospective users.

  • noncentralities induced in regression diagnostics
    Journal of statistical theory and practice, 2014
    Co-Authors: D. R. Jensen, D. E. Ramirez
    Abstract:

    Anomalies persist in the use oF deletion diagnostics in regression. Tests For outliers under subset deletions utilize the R–Fisher statistics, each having a noncentral F-Distribution with noncentrality parameter as a Function oF shiFts only at deleted rows in the index set I. Numerous studies examine empirical outcomes oF these diagnostics in random experiments. In contrast, studies here are probabilistic, examining Distributions behind those empirical outcomes and tracking the eFFects oF shiFts at nondeleted rows. By allowing shiFts at nondeleted rows in a set J, in addition to traditional shiFts at deleted rows in I, is shown to have a doubly noncentral F-Distribution. By removing the unnecessary restriction that shiFts occur only at deleted rows, these Findings support constructs akin to power curves in tracking probabilities oF masking or swamping as shiFts evolve. In addition, “regression eFFects” among outliers may have unForeseen consequences. A dichotomy oF shiFts is discovered as projections into...

Tingyu Zhu - One of the best experts on this subject based on the ideXlab platform.

  • gas phase and particle phase pcdd F congener Distributions in the Flue gas From an iron ore sintering plant
    Journal of Environmental Sciences-china, 2017
    Co-Authors: Xiaolong Liu, Xue Wang, Wen Liu, Tingyu Zhu
    Abstract:

    The activated carbon injection-circulating Fluidized bed (ACI-CFB)-bag Filter coupling technique was studied in an iron ore sintering plant. For comparison, the removal eFFiciencies under the conditions without or with ACI technology were both evaluated. It was Found that the polychlorinated dibenzo-p-dioxins and dibenzoFuran (PCDD/F) removal eFFiciency For total international toxic equivalence quantity (I-TEQ) concentration was improved From 91.61% to 97.36% when ACI was employed, revealing that ACI was very conducive to Further controlling the PCDD/F emissions. Detailed congener Distributions oF PCDD/Fs in the gas-phase and particle-phase oF the Inlet and Outlet samples were determined. Additionally, the PCDD/F Distribution For the Fly ash-with ACI sample oF was also studied.

Elaine Martin - One of the best experts on this subject based on the ideXlab platform.

  • An approximation to the F Distribution using the chi-square Distribution
    Computational Statistics & Data Analysis, 2002
    Co-Authors: Elaine Martin
    Abstract:

    For the cumulative Distribution Function (c.d.F.) oF the F Distribution, F(x; k, n), with associated degrees oF Freedom, k and n, a shrinking Factor approximation (SFA), G(λkx;k), is proposed For large n and any Fixed k, where G(x;k) is the chi-square c.d.F. with degrees oF Freedom, k, and λ = λ(kx; n) is the shrinking Factor. Numerical analysis indicates that For n/k ≥ 3, approximation accuracy oF the SFA is to the Fourth decimal place For most small values oF k. This is a substantial improvement on the accuracy that is achievable using the normal, ordinary chi-square, and ScheFFe-Tukey approximations. In addition, it is shown that the theoretical approximation error oF the SFA, |F(x; k, n)- G(λkx;k)|, is O(1/n2) uniFormly over x.