The Experts below are selected from a list of 255 Experts worldwide ranked by ideXlab platform
Cunhui Zhang - One of the best experts on this subject based on the ideXlab platform.
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scaled sparse linear regression
Biometrika, 2012Co-Authors: Cunhui ZhangAbstract:Scaled sparse linear regression jointly estimates the regression Coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the noise level via the mean residual square and scaling the penalty in proportion to the Estimated noise level. The iterative algorithm costs little beyond the computation of a path or grid of the sparse regression estimator for penalty levels above a proper threshold. For the scaled lasso, the algorithm is a gradient descent in a convex minimization of a penalized joint loss function for the regression Coefficients and noise level. Under mild regularity conditions, we prove that the scaled lasso simultaneously yields an estimator for the noise level and an Estimated Coefficient vector satisfying certain oracle inequalities for prediction, the estimation of the noise level and the regression Coefficients. These inequalities provide sufficient conditions for the consistency and asymptotic normality of the noise-level estimator, including certain cases where the number of variables is of greater order than the sample size. Parallel results are provided for least-squares estimation after model selection by the scaled lasso. Numerical results demonstrate the superior performance of the proposed methods over an earlier proposal of joint convex minimization. Copyright 2012, Oxford University Press.
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scaled sparse linear regression
arXiv: Machine Learning, 2011Co-Authors: Cunhui ZhangAbstract:Scaled sparse linear regression jointly estimates the regression Coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the noise level via the mean residual square and scaling the penalty in proportion to the Estimated noise level. The iterative algorithm costs little beyond the computation of a path or grid of the sparse regression estimator for penalty levels above a proper threshold. For the scaled lasso, the algorithm is a gradient descent in a convex minimization of a penalized joint loss function for the regression Coefficients and noise level. Under mild regularity conditions, we prove that the scaled lasso simultaneously yields an estimator for the noise level and an Estimated Coefficient vector satisfying certain oracle inequalities for prediction, the estimation of the noise level and the regression Coefficients. These inequalities provide sufficient conditions for the consistency and asymptotic normality of the noise level estimator, including certain cases where the number of variables is of greater order than the sample size. Parallel results are provided for the least squares estimation after model selection by the scaled lasso. Numerical results demonstrate the superior performance of the proposed methods over an earlier proposal of joint convex minimization.
Nashiru Billa - One of the best experts on this subject based on the ideXlab platform.
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Estimated Coefficient of variation values for sample size planning in bioequivalence studies
Principles and Practice of Constraint Programming, 2001Co-Authors: K H Yuen, J W Wong, Nashiru BillaAbstract:OBJECTIVE: The aim of the present communication is to provide information regarding the intrasubject coefficent of variation obtained from 30 bioequivalence studies covering 16 drugs which can be used for estimation of sample size. Additionally, an attempt was also made to estimate the test power of each of the studies conducted. METHODS: The intrasubject Coefficient of variation was Estimated from the residual mean square error obtained from analysis of variance of the parameters AUC0-infinity, Cmax and Cmax/AUC0-infinity after logarithmic transformation. The test power in the analyses of the above parameters was subsequently Estimated using nomograms provided by Diletti et al. [1991]. RESULTS AND CONCLUSION: Thirty products covering 16 drugs were studied in which 22 were immediate-release (including one dispersible tablet) and 8 were sustained-release formulations. The intrasubject Coefficient of variation for the parameter AUC0-infinity was smaller than Cmax, and hence considerably more studies were able to attain a power of greater than 80% using 12 volunteers for the AUC0-infinity, compared to the Cmax. However, the variability in the Cmax could be reduced by using the parameter Cmax/ AUC0-infinity, and thus, provide a more realistic estimation of sample size, since the latter reflects only the rate of absorption and not both the rate and extent as in the case of Cmax [Endrenyi et al. 1991].
Rodney F Weiher - One of the best experts on this subject based on the ideXlab platform.
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heat watch warning systems save lives Estimated costs and benefits for philadelphia 1995 98
Bulletin of the American Meteorological Society, 2004Co-Authors: Thomas J Teisberg, Laurence S Kalkstein, Lawrence Robinson, Rodney F WeiherAbstract:Abstract The Philadelphia, Pennsylvania, Hot Weather–Health Watch/Warning System was initiated in 1995 to alert the city's population to take precautionary actions when hot weather posed risks to health. The number of lives saved and the economic benefit of this system were Estimated using data from 1995 to 1998. Excess mortality in people 65 yr of age and older was defined as reported mortality minus mortality predicted by a historical trend line developed over the period of 1964–88. Excess mortality during heat waves was explained using multiple linear regression. Two variables were convincingly associated with mortality: the time of season when a particular heat wave started, and a warning variable indicating whether or not a heat wave warning had been issued. The Estimated Coefficient of the warning variable was about −2.6, suggesting that when a warning was issued, 2.6 lives were saved, on average, for each warning day and for 3 days after the warning ended. Given the number of warnings issued over t...
Stephen J. Redmond - One of the best experts on this subject based on the ideXlab platform.
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EMBC - An eight-legged tactile sensor to estimate Coefficient of static friction
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2015Co-Authors: Wei Chen, Sura Rodpongpun, Nathan Isaacson, Lauren Kark, Heba Khamis, Stephen J. RedmondAbstract:It is well known that a tangential force larger than the maximum static friction force is required to initiate the sliding motion between two objects, which is governed by a material constant called the Coefficient of static friction. Therefore, knowing the Coefficient of static friction is of great importance for robot grippers which wish to maintain a stable and precise grip on an object during various manipulation tasks. Importantly, it is most useful if grippers can estimate the Coefficient of static friction without having to explicitly explore the object first, such as lifting the object and reducing the grip force until it slips. A novel eight-legged sensor, based on simplified theoretical principles of friction is presented here to estimate the Coefficient of static friction between a planar surface and the prototype sensor. Each of the sensor's eight legs are straight and rigid, and oriented at a specified angle with respect to the vertical, allowing it to estimate one of five ranges (5 = 8/2 + 1) that the Coefficient of static friction can occupy. The Coefficient of friction can be Estimated by determining whether the legs have slipped or not when pressed against a surface. The Coefficients of static friction between the sensor and five different materials were Estimated and compared to a measurement from traditional methods. A least-squares linear fit of the sensor Estimated Coefficient showed good correlation with the reference Coefficient with a gradient close to one and an r2 value greater than 0.9.
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An eight-legged tactile sensor to estimate Coefficient of static friction
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015Co-Authors: Wei Chen, Sura Rodpongpun, Nathan Isaacson, Lauren Kark, Heba Khamis, Stephen J. RedmondAbstract:It is well known that a tangential force larger than the maximum static friction force is required to initiate the sliding motion between two objects, which is governed by a material constant called the Coefficient of static friction. Therefore, knowing the Coefficient of static friction is of great importance for robot grippers which wish to maintain a stable and precise grip on an object during various manipulation tasks. Importantly, it is most useful if grippers can estimate the Coefficient of static friction without having to explicitly explore the object first, such as lifting the object and reducing the grip force until it slips. A novel eight-legged sensor, based on simplified theoretical principles of friction is presented here to estimate the Coefficient of static friction between a planar surface and the prototype sensor. Each of the sensor's eight legs are straight and rigid, and oriented at a specified angle with respect to the vertical, allowing it to estimate one of five ranges (5 = 8/2 + 1) that the Coefficient of static friction can occupy. The Coefficient of friction can be Estimated by determining whether the legs have slipped or not when pressed against a surface. The Coefficients of static friction between the sensor and five different materials were Estimated and compared to a measurement from traditional methods. A least-squares linear fit of the sensor Estimated Coefficient showed good correlation with the reference Coefficient with a gradient close to one and an r2 value greater than 0.9.
K H Yuen - One of the best experts on this subject based on the ideXlab platform.
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Estimated Coefficient of variation values for sample size planning in bioequivalence studies
Principles and Practice of Constraint Programming, 2001Co-Authors: K H Yuen, J W Wong, Nashiru BillaAbstract:OBJECTIVE: The aim of the present communication is to provide information regarding the intrasubject coefficent of variation obtained from 30 bioequivalence studies covering 16 drugs which can be used for estimation of sample size. Additionally, an attempt was also made to estimate the test power of each of the studies conducted. METHODS: The intrasubject Coefficient of variation was Estimated from the residual mean square error obtained from analysis of variance of the parameters AUC0-infinity, Cmax and Cmax/AUC0-infinity after logarithmic transformation. The test power in the analyses of the above parameters was subsequently Estimated using nomograms provided by Diletti et al. [1991]. RESULTS AND CONCLUSION: Thirty products covering 16 drugs were studied in which 22 were immediate-release (including one dispersible tablet) and 8 were sustained-release formulations. The intrasubject Coefficient of variation for the parameter AUC0-infinity was smaller than Cmax, and hence considerably more studies were able to attain a power of greater than 80% using 12 volunteers for the AUC0-infinity, compared to the Cmax. However, the variability in the Cmax could be reduced by using the parameter Cmax/ AUC0-infinity, and thus, provide a more realistic estimation of sample size, since the latter reflects only the rate of absorption and not both the rate and extent as in the case of Cmax [Endrenyi et al. 1991].