In this article, a family of feasible generalized double k-class estimator in a linear regression model with non-spherical disturbances is considered. The performance of this estimator is judged with feasible generali...
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In this article, a family of feasible generalized double k-class estimator in a linear regression model with non-spherical disturbances is considered. The performance of this estimator is judged with feasible generalized least-squares and feasible generalized Stein-rule estimators under balanced loss function using the criteria of quadratic risk and general Pitman closeness. A Monte-Carlo study investigates the finite sample properties of several estimators arising from the family of feasible double k-class estimators. (C) 2003 Elsevier Inc. All rights reserved.
The asymptotic distribution of S-estimators in the linear regression model with long-memory error terms is obtained under mild regularity conditions to the regressors which are sufficiently weak to cover, for example,...
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The asymptotic distribution of S-estimators in the linear regression model with long-memory error terms is obtained under mild regularity conditions to the regressors which are sufficiently weak to cover, for example, polynomial trends and i.i.d. carriers, It turns out that S-estimators are asymptotically normal in the case of deterministic regressors with a variance-covariance structure similar to the structure in the i.i.d. case. Also, the rate of convergence for S-estimators is the same as for the least-squares estimator (LSE) and the best linear unbiased estimator (BLUE).
作者：
Wu, CBUniv Waterloo
Dept Stat & Actuarial Sci Waterloo ON N2L 3G1 Canada
A weighted empirical likelihood approach is proposed to take account of the heteroscedastic structure of the data. The resulting weighted empirical likelihood ratio statistic is shown to have a limiting chisquare dist...
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A weighted empirical likelihood approach is proposed to take account of the heteroscedastic structure of the data. The resulting weighted empirical likelihood ratio statistic is shown to have a limiting chisquare distribution. A limited simulation study shows that the associated confidence intervals for a population mean or a regression coefficient have more accurate coverage probabilities and more balanced two-sided tail errors when the sample size is small or moderate. The proposed weighted empirical likelihood method also provides more efficient point estimators for a population mean in the presence of side information. Large sample resemblances between the weighted and the unweighted empirical likelihood estimators are characterized through high-order asymptotics and small sample discrepancies of these estimators are investigated through simulation. The proposed weighted approach reduces to the usual unweighted empirical likelihood method under a homogeneous variance structure. (C) 2003 Elsevier B.V. All rights reserved.
Lineament density maps can be used for the quantitative evaluation of relationships between lineaments and the occurrence of groundwater. This paper reports the usefulness of the representative elementary area (REA) c...
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Lineament density maps can be used for the quantitative evaluation of relationships between lineaments and the occurrence of groundwater. This paper reports the usefulness of the representative elementary area (REA) concept for lineament analysis. This concept refers to the area of the unit circle needed to calculate the lineament density factors distributed within the circle: length, counts and cross-points counts. The circle is a unit circle where one calculates the sum of the lineament length, lineament counts and the number of cross-points within it. The REA is needed to obtain the best representative lineament density map prior to the analysis of relation between lineaments and groundwater well yield or other groundwater characteristics. A lineament map for the Yongsangang-Seomjingang watershed of Korea was used for demonstrating the concept. It is shown that the REA concept can be efficiently applied to lineament density analysis and mapping. In the demonstration case, the lineament densities are inversely proportional to the size of the REA, and the REA can be calculated with this inversely linear regression model. If the average lineament density values for the whole study area are known, the most accurate density maps can be drawn using the REAs obtained from each linear regression model.
In this paper, a BP model for short-term price forecasting and a linear regress model for mid/long-term price forecasting are described together with forecasting results. A detailed discussion on the choice of forecas...
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ISBN:
(纸本)0780382374
In this paper, a BP model for short-term price forecasting and a linear regress model for mid/long-term price forecasting are described together with forecasting results. A detailed discussion on the choice of forecast models and forecast variables is reported. A salient feature of the reported methods is that the forecast models can take into account the influence of market power on electricity prices. Specifically, a market power index, namely Must Run Ratio (MRR), is judiciously selected as an input to the price forecast models. An added advantage of presented models is that the number of required input variables can be reduced significantly without perceived loss of accuracy. The suggested methods are being utilized, by the transmission company, to forecast short-term, mid/long-term prices in Zhejiang Electricity Market. The results show that the proposed forecast models meet the basic requirement of Zhejiang electricity market operation.
The pressure pulse is amplified between the aorta and peripheral sites. This study compares two methods to estimate pressure pulse amplification (PPA) between the aorta and the brachial artery. Method 1: PPA was deter...
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The pressure pulse is amplified between the aorta and peripheral sites. This study compares two methods to estimate pressure pulse amplification (PPA) between the aorta and the brachial artery. Method 1: PPA was determined from a multi-parameter linearregression of subject parameters (gender, age, height, weight, heart rate (HR), brachial systolic pressure (BSP), diastolic pressure (BDP), mean pressure (MP)). Method 2: PPA was calculated from central aortic pressure waveforms (CW) estimated from the same subject parameters. The sample population (1421 male, 992 female) was selected from a database where aortic pressure was estimated by mathematical transformation of a peripheral (radial) pulse calibrated to sphygmomanometric BSP and BDP. The two methods were consistent in showing HR and MP as the most important parameters to estimate PPA. Correlation coefficients (R-2) of 0.48 (method 1) and 0.44 (method 2) were obtained using height, weight, HR, BSP, BDP and age. Inclusion of MP increased R-2 to 0.77 (method 1) and 0.71 (method 2). This study shows that databases containing peripheral and central aortic pressure waveforms can be used to construct multiple regressionmodels for PPA estimation. These models could be applied to studies of similar subject groups where peripheral waveforms may not be available.
The pressure pulse is amplified between the aorta and peripheral sites. This study compares two methods to estimate pressure pulse amplification (PPA) between the aorta and the brachial artery. Method 1: PPA was deter...
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The pressure pulse is amplified between the aorta and peripheral sites. This study compares two methods to estimate pressure pulse amplification (PPA) between the aorta and the brachial artery. Method 1: PPA was determined from a multi-parameter linearregression of subject parameters (gender, age, height, weight, heart rate (HR), brachial systolic pressure (BSP), diastolic pressure (BDP), mean pressure (MP)). Method 2: PPA was calculated from central aortic pressure waveforms (CW) estimated from the same subject parameters. The sample population (1421 male, 992 female) was selected from a database where aortic pressure was estimated by mathematical transformation of a peripheral (radial) pulse calibrated to sphygmomanometric BSP and BDP. The two methods were consistent in showing HR and MP as the most important parameters to estimate PPA. Correlation coefficients (R-2) of 0.48 (method 1) and 0.44 (method 2) were obtained using height, weight, HR, BSP, BDP and age. Inclusion of MP increased R-2 to 0.77 (method 1) and 0.71 (method 2). This study shows that databases containing peripheral and central aortic pressure waveforms can be used to construct multiple regressionmodels for PPA estimation. These models could be applied to studies of similar subject groups where peripheral waveforms may not be available.
A linear regression model is developed to predict spring ice conditions in Hudson Bay 3 months in advance. The model has 8 atmospheric, oceanic and sea ice predictors that together explain 90% of the variability in th...
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ISBN:
(纸本)0780387422
A linear regression model is developed to predict spring ice conditions in Hudson Bay 3 months in advance. The model has 8 atmospheric, oceanic and sea ice predictors that together explain 90% of the variability in the predicted time series with a MAE of 0.26. The categorical forecast accuracy ranges from 73% to 77%. A series of diagnostics including Monte Carlo simulations are used to ensure the model is statistically significant and is not adversely affected by serial correlation or multicolinearity. The results suggest that for long-lead forecasts, sea ice severity may be forecasted using a simple statistical model that does not require any information regarding the future climate.
Maximum likelihood estimation is investigated in the context of linear regression models under partial independence restrictions. These restrictions aim to assume a kind of completeness of a set of predictors Z in the...
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Maximum likelihood estimation is investigated in the context of linear regression models under partial independence restrictions. These restrictions aim to assume a kind of completeness of a set of predictors Z in the sense that they are sufficient to explain the dependencies between an outcome Y and predictors X: L(Y\ Z, X) = Z,(Y\Z), where L(.\.) stands for the conditional distribution. From a practical point of view, the former model is particularly interesting in a double sampling scheme where Y and Z are measured together on a first sample and Z and X on a second separate sample. In that case, estimation procedures are close to those developed in the study of double-regression by Engel & Walstra (1991) and Causeur & Dhorne (1998). Properties of the estimators are derived in a small sample framework and in an asymptotic one, and the procedure is illustrated by an example from the food industry context.
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