The Experts below are selected from a list of 11769 Experts worldwide ranked by ideXlab platform
Mahmoud Torabi - One of the best experts on this subject based on the ideXlab platform.
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Sumca: simple, unified, Monte-Carlo-assisted approach to second-order unbiased mean-Squared Prediction Error estimation
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2020Co-Authors: Jiming Jiang, Mahmoud TorabiAbstract:We propose a simple, unified, Monte‐Carlo‐assisted approach (called ‘Sumca’) to second‐order unbiased estimation of the mean‐Squared Prediction Error (MSPE) of a small area predictor. The MSPE estimator proposed is easy to derive, has a simple expression and applies to a broad range of predictors that include the traditional empirical best linear unbiased predictor, empirical best predictor and post‐model‐selection empirical best linear unbiased predictor and empirical best predictor as special cases. Furthermore, the leading term of the MSPE estimator proposed is guaranteed positive; the lower order term corresponds to a bias correction, which can be evaluated via a Monte Carlo method. The computational burden for the Monte Carlo evaluation is much less, compared with other Monte‐Carlo‐based methods that have been used for producing second‐order unbiased MSPE estimators, such as the double bootstrap and Monte Carlo jackknife. The Sumca estimator also has a nice stability feature. Theoretical and empirical results demonstrate properties and advantages of the Sumca estimator.
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Estimation of mean Squared Prediction Error of empirically spatial predictor of small area means under a linear mixed model
Journal of Statistical Planning and Inference, 2020Co-Authors: Mahmoud Torabi, Jiming JiangAbstract:Abstract Policy decisions regarding allocation of resources to subgroups in a population, called small areas, are based on reliable predictors of their underlying parameters. However, the information is collected at a different scale than these subgroups. Hence, we need to predict characteristics of the subgroups based on the coarser scale data. In view of this, there is a growing demand for reliable small area predictors by borrowing information from other related sources. For this purpose, mixed models have been commonly used in small area estimation assuming independent small areas. There are many situations, however, that the small area parameters are related to their locations. For instance, it is an interest of policy makers (and public) to know the spatial pattern of a chronic disease (e.g., asthma) to identify small areas with high risk of disease for possible preventions. In this paper, we propose small area models in the class of spatial linear mixed models to be able to predict small area parameters and also to obtain corresponding mean Squared Prediction Error (MSPE). We also provide unbiased estimators of MSPE of small area predictors using Taylor series expansion and parametric bootstrap methods. In our simulations, we show that our MSPE estimators using Taylor expansion and parametric bootstrap perform very well in terms of precision of small area predictors. Performance of our proposed approach is also evaluated through a real application of physician visits for Total Respiratory Morbidity conditions in Manitoba, Canada.
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empirical bayes estimation of small area means under a nested Error linear regression model with measurement Errors in the covariates
Scandinavian Journal of Statistics, 2009Co-Authors: Mahmoud Torabi, Gauris S Datta, J N K RaoAbstract:. Previously, small area estimation under a nested Error linear regression model was studied with area level covariates subject to measurement Error. However, the information on observed covariates was not used in finding the Bayes predictor of a small area mean. In this paper, we first derive the fully efficient Bayes predictor by utilizing all the available data. We then estimate the regression and variance component parameters in the model to get an empirical Bayes (EB) predictor and show that the EB predictor is asymptotically optimal. In addition, we employ the jackknife method to obtain an estimator of mean Squared Prediction Error (MSPE) of the EB predictor. Finally, we report the results of a simulation study on the performance of our EB predictor and associated jackknife MSPE estimators. Our results show that the proposed EB predictor can lead to significant gain in efficiency over the previously proposed EB predictor.
Michael S. Feld - One of the best experts on this subject based on the ideXlab platform.
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feasibility of measuring blood glucose concentration by near infrared raman spectroscopy
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 1997Co-Authors: Andrew J. Berger, Irving Itzkan, Michael S. FeldAbstract:Abstract We report the determinations of glucose concentration in human whole blood samples made using near-infrared Raman spectroscopy. Raman spectra of blood samples with above-physiological levels of glucose were acquired for 5 min through the wall of a cuvette via fiber optics. Partial least squares analysis was used to predict glucose concentrations in the samples. A root mean Squared Prediction Error of 3.6 mM glucose was achieved with a correlation coefficient of 0.99 between reference and predicted values. This result is the first step in evaluating the potential of near-infrared Raman spectroscopy to perform blood glucose measurements with clinical accuracy. The technique is capable of measuring the concentration of other Raman-active blood constituents; as an example, bicarbonate was also measured. The method could eventually be useful for direct measurement of tissue analytes.
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Rapid, noninvasive concentration measurements of aqueous biological analytes by near-infrared Raman spectroscopy.
Applied Optics, 1996Co-Authors: Andrew J. Berger, Yang Wang, Michael S. FeldAbstract:Accurate concentration measurements of glucose, lactic acid, and creatinine in saline solution have been achieved with near-IR Raman spectroscopy and a partial least-squares analysis. The Raman spectra were acquired remotely through optical fibers. A root-mean-Squared Prediction Error of 1.2 mM for glucose concentration was achieved in 100 s. Concentrations of other analytes were predicted with similar accuracy.
Ching-kang Ing - One of the best experts on this subject based on the ideXlab platform.
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analysis of high dimensional regression models using orthogonal greedy algorithms
2018Co-Authors: Hsiangling Hsu, Ching-kang Ing, Tze Leung LaiAbstract:We begin by reviewing recent results of Ing and Lai (Stat Sin 21:1473–1513, 2011) on the statistical properties of the orthogonal greedy algorithm (OGA) in high-dimensional sparse regression models with independent observations. In particular, when the regression coefficients are absolutely summable, the conditional mean Squared Prediction Error and the empirical norm of OGA derived by Ing and Lai (Stat Sin 21:1473–1513, 2011) are introduced. We then explore the performance of OGA under more general sparsity conditions. Finally, we obtain the convergence rate of OGA in high-dimensional time series models, and illustrate the advantage of our results compared to those established for Lasso by Basu and Michailidis (Ann Stat 43:1535–1567, 2015) and Wu and Wu (Electron J Stat 10:352–379, 2016).
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On Estimating Conditional Mean-Squared Prediction Error in Autoregressive Models
Social Science Research Network, 2003Co-Authors: Ching-kang IngAbstract:Zhang and Shaman considered the problem of estimating the conditional mean-Squared prediciton Error (CMSPE) for a Gaussian autoregressive (AR) process. They used the final Prediction Error (FPE) of Akaike to estimate CMSPE and proposed that FPE's effectiveness be judged by its asymptotic correlation with CMSPE. However, as pointed out by Kabaila and He, the derivation of this correlation by Zhang and Shaman is incomplete, and the performance of FPE in estimating CMSPE is also poor in Kabaila and He's simulation study. Kabaila and He further proposed an alternative estimator of CMSPE, V, in the stationary AR(1) model. They reported that V has a larger normalized correlation with CMSPE through Monte Carlo simulation results. In this paper, we propose a generalization of V, V, in the higher-order AR model, and obtain the asymptotic correlation of FPE and V with CMSPE. We show that the limit of the normalized correlation of V with CMSPE is larger than that of FPE with CMSPE, and hence Kabaila and He's finding is justified theoretically. In addition, the performances of the above estimators of CMSPE are re-examined in terms of mean-Squared Errors (MSE). Our main conclusion is that from the MSE point of view, V is the best choice among a family of asymptotically unbiased estimators of CMSPE including FPE and V as its special cases.
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On Estimating Conditional Mean‐Squared Prediction Error in Autoregressive Models
Journal of Time Series Analysis, 2003Co-Authors: Ching-kang IngAbstract:. Zhang and Shaman considered the problem of estimating the conditional mean-Squared prediciton Error (CMSPE) for a Gaussian autoregressive (AR) process. They used the final Prediction Error (FPE) of Akaike to estimate CMSPE and proposed that FPE's effectiveness be judged by its asymptotic correlation with CMSPE. However, as pointed out by Kabaila and He, the derivation of this correlation by Zhang and Shaman is incomplete, and the performance of FPE in estimating CMSPE is also poor in Kabaila and He's simulation study. Kabaila and He further proposed an alternative estimator of CMSPE, V, in the stationary AR(1) model. They reported that V has a larger normalized correlation with CMSPE through Monte Carlo simulation results. In this paper, we propose a generalization of V, V˜, in the higher-order AR model, and obtain the asymptotic correlation of FPE and V˜ with CMSPE. We show that the limit of the normalized correlation of V˜ with CMSPE is larger than that of FPE with CMSPE, and hence Kabaila and He's finding is justified theoretically. In addition, the performances of the above estimators of CMSPE are re-examined in terms of mean-Squared Errors (MSE). Our main conclusion is that from the MSE point of view, V˜ is the best choice among a family of asymptotically unbiased estimators of CMSPE including FPE and V˜ as its special cases.
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A Note on Mean‐Squared Prediction Errors of the Least Squares Predictors in Random Walk Models
Journal of Time Series Analysis, 2001Co-Authors: Ching-kang IngAbstract:An asymptotic expression for the mean-Squared Prediction Error (MSPE) of the least squares predictor is obtained in the random walk model. It is shown that the term of order 1/n in this Error, where n is the sample size, is twice as large as the one obtained from the first-order autoregressive (AR(1)) model satisfying the stationary assumption. Moreover, while the correlation between the squares of the (normalized) regressor variable and normalized least squares estimator is asymptotically negligible in the stationary AR(1) model, we have found that the correlation has significantly negative value in the random walk model. To obtain these results, a new methodology, which is found to be useful in dealing with the moment properties of a strongly dependent process, is introduced.
Jingtan Cui - One of the best experts on this subject based on the ideXlab platform.
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sensor fault detection diagnosis and estimation for centrifugal chiller systems using principal component analysis method
Applied Energy, 2005Co-Authors: Shengwei Wang, Jingtan CuiAbstract:An online strategy is developed to detect, diagnose and validate sensor faults in centrifugal chillers. Considering thermophysical characteristics of the water-cooled centrifugal chillers, a dozen sensors of great concern in the chiller-system monitoring and controls were assigned into two models based on principal-component analysis. Each of the two models can group a set of correlated variables and capture the systematic trends of the chillers. The Q-statistic and Q-contribution plot were used to detect and diagnose the sensor faults, respectively. In addition, an approach based on the minimization of Squared Prediction Error of reconstructed vector of variables was used to reconstruct the identified faulty-sensors, i.e., estimate their bias magnitudes. The sensor-fault detection, diagnosis and estimation strategy was validated using an existing building chiller plant while various sensor faults were introduced.
Joe S Qin - One of the best experts on this subject based on the ideXlab platform.
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joint diagnosis of process and sensor faults using principal component analysis
Control Engineering Practice, 1998Co-Authors: Ricardo Dunia, Joe S QinAbstract:Abstract This paper presents a unified approach to process and sensor fault detection, identification, and reconstruction via principal component analysis. The principal component analysis model partitions the measurement space into a principal component subspace where normal variation occurs, and a residual subspace that faults may occupy. Both process faults and sensor faults are characterized by a direction vector, which describes the behavior of the fault. Fault reconstruction is accomplished by sliding the sample vector as close as possible to the principal component subspace. When the actual fault is assumed, the maximum reduction in the Squared Prediction Error is achieved. A fault-identification index is defined in terms of the reconstructed Squared Prediction Error. Fault detectability, reconstructability, and identifiability conditions are derived and demonstrated with a geometric interpretation. Numerous examples are provided to verify the method and conditions derived in the paper. An unreconstructed variance is defined and used to determine the number of principal components for best reconstruction. The proposed approach is applied to a data set from an industrial boiler process.
