The Experts below are selected from a list of 2766 Experts worldwide ranked by ideXlab platform
Ya-wen Yang - One of the best experts on this subject based on the ideXlab platform.
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Two advanced methods for adjusting the main coefficient in logistic regression
Computational Statistics, 2011Co-Authors: Ya-wen YangAbstract:A binary disease outcome is commonly modeled with continuous covariates (e.g., biochemical concentration) in medical research, and the corresponding exploration may employ a normal discrimination approach. The covariate relationship affects the estimated association between binary outcome and the interesting covariate. The method of value deviated from a fitted value (fractional polynomial), which is abbreviated as VDFV, may reduce the estimation bias especially when the relationship between the covariates is nonlinear. However, when the Extraneous Variable relates to the outcome, the pooled data (cases and controls) are replaced by the control data only for the purpose of fitting values. Based on two association patterns, the Extraneous Variable unrelated to the outcome (I) and that related to the outcome (II), the simulation study reveals that VDFV-p (using pooled data) is reliable, with less bias and a smaller mean square error (MSE) in pattern (I) and that VDFV-c (using control data) shows less bias in pattern (II). The conventional covariate adjustment performs worse in (I) but fairly well in (II). Note that a huge MSE is never observed in VDFV-p or VDFV-c, while this is a common issue related to small sample size or sparse data in logistic regression. Two fetal studies are illustrated--one for pattern (I) and one for pattern (II).
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a comparison of binary models dealing with an Extraneous effect relating to the main risk factor but not relating to the outcome Variable
Statistics in Medicine, 2009Co-Authors: Ya-wen YangAbstract:In a binary model relating a response Variable Y to a risk factor X, account may need to take of an Extraneous effect Z that is related to X, but not Y. This is known as the association pattern Y−X−Z. The Extraneous Variable Z is commonly included in models as a covariate. This paper concerns binary models, and investigates the use of deviation from the group mean (D-GM) and deviation from the fitted fractional polynomial value (D-FP) for removing the Extraneous effect of Z. In a simulation study, D-FP performed excellently, while the performance of D-GM was slightly worse than the traditional method of treating Z as a covariate. In addition, estimators with excessive mean square errors or standard errors cannot occur when D-GM or D-FP is employed, even in small or sparse data sets. The Y−X−Z association pattern studied here often occurs in fetal studies, where the fetal measurement (X) varies with the gestation age (Z), but gestation age does not relate to the outcome Variable (Y; e.g. Down's syndrome). D-GM and D-FP perform well with illustrative data from fetal studies, although there is a weak association between X and Z with a lower proportion of case subjects (e.g. 11:1, control to case). It is not necessary to add a new covariate when a model deals with the Extraneous effect. The D-FP or D-GM methods perform well with the real data studied here, and moreover, D-FP demonstrated excellent performance in simulations. Copyright © 2009 John Wiley & Sons, Ltd.
Noah A Smith - One of the best experts on this subject based on the ideXlab platform.
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better hypothesis testing for statistical machine translation controlling for optimizer instability
Meeting of the Association for Computational Linguistics, 2011Co-Authors: Jonathan H Clark, Chris Dyer, Alon Lavie, Noah A SmithAbstract:In statistical machine translation, a researcher seeks to determine whether some innovation (e.g., a new feature, model, or inference algorithm) improves translation quality in comparison to a baseline system. To answer this question, he runs an experiment to evaluate the behavior of the two systems on held-out data. In this paper, we consider how to make such experiments more statistically reliable. We provide a systematic analysis of the effects of optimizer instability---an Extraneous Variable that is seldom controlled for---on experimental outcomes, and make recommendations for reporting results more accurately.
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ACL (Short Papers) - Better Hypothesis Testing for Statistical Machine Translation: Controlling for Optimizer Instability
2011Co-Authors: Jonathan H Clark, Chris Dyer, Alon Lavie, Noah A SmithAbstract:In statistical machine translation, a researcher seeks to determine whether some innovation (e.g., a new feature, model, or inference algorithm) improves translation quality in comparison to a baseline system. To answer this question, he runs an experiment to evaluate the behavior of the two systems on held-out data. In this paper, we consider how to make such experiments more statistically reliable. We provide a systematic analysis of the effects of optimizer instability---an Extraneous Variable that is seldom controlled for---on experimental outcomes, and make recommendations for reporting results more accurately.
Michihiro Yoshimura - One of the best experts on this subject based on the ideXlab platform.
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Path model A: Explanatory drawing of possible cascade from BMI to IHD directly and via the low reactivity of BNP, dyslipidemia, hypertension, and HbA1c.
2017Co-Authors: Joshi Tsutsumi, Kosuke Minai, Makoto Kawai, Kazuo Ogawa, Yasunori Inoue, Satoshi Morimoto, Toshikazu Tanaka, Tomohisa Nagoshi, Takayuki Ogawa, Michihiro YoshimuraAbstract:This path has a coefficient showing the standardized coefficient of regressing independent Variables on the dependent Variable of the relevant path. These Variables indicate standardized regression coefficients (direct effect) [simple capitals], squared multiple correlations [narrow italic capitals] and correlations among exogenous Variables [capitals inside round brackets]. A two-way arrow between two Variables indicates a correlation between those two Variables. The total variance in a dependent Variable for every regression is theorized to be caused by either independent Variables of the model or Extraneous Variables (e). BMI: body mass index; BNP: B-type natriuretic peptide; e: Extraneous Variable.
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Path models B1 and B2: explanatory drawing of Ppossible cascade from BMI to BNP and further to IHD.
2017Co-Authors: Joshi Tsutsumi, Kosuke Minai, Makoto Kawai, Kazuo Ogawa, Yasunori Inoue, Satoshi Morimoto, Toshikazu Tanaka, Tomohisa Nagoshi, Takayuki Ogawa, Michihiro YoshimuraAbstract:This path has a coefficient showing the standardized coefficient of regressing independent Variables on the dependent Variable of the relevant path. These Variables indicate standardized regression coefficients (direct effect) [simple capitals], squared multiple correlations [narrow italic capitals] and correlations among exogenous Variables [capitals inside round brackets]. BMI; body mass index; BNP: B-type natriuretic peptide; IHD: ischemic heart disease; e: Extraneous Variable. B1. Path model B1: a simple path model for the connection between BNP as a cause and IHD as an effect. B2. Path model B2: directional paths between BNP and IHD to distinguish between cause and effect.
Jonathan H Clark - One of the best experts on this subject based on the ideXlab platform.
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better hypothesis testing for statistical machine translation controlling for optimizer instability
Meeting of the Association for Computational Linguistics, 2011Co-Authors: Jonathan H Clark, Chris Dyer, Alon Lavie, Noah A SmithAbstract:In statistical machine translation, a researcher seeks to determine whether some innovation (e.g., a new feature, model, or inference algorithm) improves translation quality in comparison to a baseline system. To answer this question, he runs an experiment to evaluate the behavior of the two systems on held-out data. In this paper, we consider how to make such experiments more statistically reliable. We provide a systematic analysis of the effects of optimizer instability---an Extraneous Variable that is seldom controlled for---on experimental outcomes, and make recommendations for reporting results more accurately.
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ACL (Short Papers) - Better Hypothesis Testing for Statistical Machine Translation: Controlling for Optimizer Instability
2011Co-Authors: Jonathan H Clark, Chris Dyer, Alon Lavie, Noah A SmithAbstract:In statistical machine translation, a researcher seeks to determine whether some innovation (e.g., a new feature, model, or inference algorithm) improves translation quality in comparison to a baseline system. To answer this question, he runs an experiment to evaluate the behavior of the two systems on held-out data. In this paper, we consider how to make such experiments more statistically reliable. We provide a systematic analysis of the effects of optimizer instability---an Extraneous Variable that is seldom controlled for---on experimental outcomes, and make recommendations for reporting results more accurately.
Joshi Tsutsumi - One of the best experts on this subject based on the ideXlab platform.
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Path model A: Explanatory drawing of possible cascade from BMI to IHD directly and via the low reactivity of BNP, dyslipidemia, hypertension, and HbA1c.
2017Co-Authors: Joshi Tsutsumi, Kosuke Minai, Makoto Kawai, Kazuo Ogawa, Yasunori Inoue, Satoshi Morimoto, Toshikazu Tanaka, Tomohisa Nagoshi, Takayuki Ogawa, Michihiro YoshimuraAbstract:This path has a coefficient showing the standardized coefficient of regressing independent Variables on the dependent Variable of the relevant path. These Variables indicate standardized regression coefficients (direct effect) [simple capitals], squared multiple correlations [narrow italic capitals] and correlations among exogenous Variables [capitals inside round brackets]. A two-way arrow between two Variables indicates a correlation between those two Variables. The total variance in a dependent Variable for every regression is theorized to be caused by either independent Variables of the model or Extraneous Variables (e). BMI: body mass index; BNP: B-type natriuretic peptide; e: Extraneous Variable.
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Path models B1 and B2: explanatory drawing of Ppossible cascade from BMI to BNP and further to IHD.
2017Co-Authors: Joshi Tsutsumi, Kosuke Minai, Makoto Kawai, Kazuo Ogawa, Yasunori Inoue, Satoshi Morimoto, Toshikazu Tanaka, Tomohisa Nagoshi, Takayuki Ogawa, Michihiro YoshimuraAbstract:This path has a coefficient showing the standardized coefficient of regressing independent Variables on the dependent Variable of the relevant path. These Variables indicate standardized regression coefficients (direct effect) [simple capitals], squared multiple correlations [narrow italic capitals] and correlations among exogenous Variables [capitals inside round brackets]. BMI; body mass index; BNP: B-type natriuretic peptide; IHD: ischemic heart disease; e: Extraneous Variable. B1. Path model B1: a simple path model for the connection between BNP as a cause and IHD as an effect. B2. Path model B2: directional paths between BNP and IHD to distinguish between cause and effect.
