Estimation of the mean squared error of predictors of small area linear parameters under a logistic mixed model

2007 ◽  
Vol 51 (5) ◽  
pp. 2720-2733 ◽  
Author(s):  
W. González-Manteiga ◽  
M.J. Lombardía ◽  
I. Molina ◽  
D. Morales ◽  
L. Santamaría
Keyword(s):  
Small Area ◽  
Mixed Model ◽  
Mean Squared Error ◽  
Squared Error ◽  
The Mean

scholarly journals Small Area Estimation under Spatial Multivariate Fay–Herriot Model

2021 ◽  
Author(s):  
hukum chandra ◽  
Saurav Guha
Keyword(s):  
Small Area ◽  
Mean Squared Error ◽  
National Sample ◽  
Sample Survey ◽  
Consumer Expenditure ◽  
Squared Error ◽  
Food And Nutrition ◽  
Empirical Performance ◽  
Nutrition Intake ◽  
The Mean

Spatial version of multivariate Fay–Herriot model is introduced and small area predictor under this model is proposed. The mean squared error (MSE) estimation of the proposed small area predictor is also developed. The empirical performance of the proposed small area predictor and the MSE estimator are evaluated through simulation studies. The empirical results clearly show that the proposed small area predictor outperforms the existing predictors. The proposed MSE estimator tracks the actual value of MSE reasonably well with acceptable coverage rate. An application to estimate the disparities in food and nutrition intake from the 2011–12 Household Consumer Expenditure Survey data collected by the national sample survey office of India is also presented.

The Estimation of the Mean Squared Error of Small-Area Estimators

1990 ◽  
Vol 85 (409) ◽  
pp. 163-171 ◽  
Author(s):  
N. G. N. Prasad ◽  
J. N. K. Rao
Keyword(s):  
Small Area ◽  
Mean Squared Error ◽  
Squared Error ◽  
The Mean

scholarly journals Small Area Estimation under Spatial Multivariate Fay–Herriot Model

2021 ◽  
Author(s):  
hukum chandra ◽  
Saurav Guha
Keyword(s):  
Small Area ◽  
Mean Squared Error ◽  
National Sample ◽  
Sample Survey ◽  
Consumer Expenditure ◽  
Squared Error ◽  
Food And Nutrition ◽  
Empirical Performance ◽  
Nutrition Intake ◽  
The Mean

Spatial version of multivariate Fay–Herriot model is introduced and small area predictor under this model is proposed. The mean squared error (MSE) estimation of the proposed small area predictor is also developed. The empirical performance of the proposed small area predictor and the MSE estimator are evaluated through simulation studies. The empirical results clearly show that the proposed small area predictor outperforms the existing predictors. The proposed MSE estimator tracks the actual value of MSE reasonably well with acceptable coverage rate. An application to estimate the disparities in food and nutrition intake from the 2011–12 Household Consumer Expenditure Survey data collected by the national sample survey office of India is also presented.

The Mean Squared Error of the Instrumental Variables Estimator When the Disturbance Has an Elliptical Distribution

2006 ◽  
Vol 25 (1) ◽  
pp. 117-138 ◽  
Author(s):  
Fernanda P. M. Peixe ◽  
Alastair R. Hall ◽  
Kostas Kyriakoulis
Keyword(s):  
Instrumental Variables ◽  
Mean Squared Error ◽  
Elliptical Distribution ◽  
Squared Error ◽  
The Mean

Computing trade-offs in robust design: Perspectives of the mean squared error

2011 ◽  
Vol 60 (2) ◽  
pp. 248-255 ◽  
Author(s):  
Sangmun Shin ◽  
Funda Samanlioglu ◽  
Byung Rae Cho ◽  
Margaret M. Wiecek
Keyword(s):  
Robust Design ◽  
Mean Squared Error ◽  
Squared Error ◽  
Trade Offs ◽  
The Mean

scholarly journals Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

2018 ◽  
Vol 10 (12) ◽  
pp. 4863 ◽  
Author(s):  
Chao Huang ◽  
Longpeng Cao ◽  
Nanxin Peng ◽  
Sijia Li ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Power Plants ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Multilayer Perception ◽  
Squared Error ◽  
The Mean ◽  
Effectiveness And Efficiency ◽  
Mlp Network ◽  
Grid Operation ◽  
Better Than

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

scholarly journals On double stage minimax-shrinkage estimator for generalized Rayleigh model

2016 ◽  
Vol 5 (1) ◽  
pp. 39 ◽  
Author(s):  
Abbas Najim Salman ◽  
Maymona Ameen
Keyword(s):  
Sample Size ◽  
Shape Parameter ◽  
Mean Squared Error ◽  
Scale Parameter ◽  
Rayleigh Distribution ◽  
Shrinkage Estimator ◽  
Squared Error ◽  
Expected Sample Size ◽  
Generalized Rayleigh Distribution ◽  
The Mean

<p>This paper is concerned with minimax shrinkage estimator using double stage shrinkage technique for lowering the mean squared error, intended for estimate the shape parameter (a) of Generalized Rayleigh distribution in a region (R) around available prior knowledge (a<sub>0</sub>) about the actual value (a) as initial estimate in case when the scale parameter (l) is known .</p><p>In situation where the experimentations are time consuming or very costly, a double stage procedure can be used to reduce the expected sample size needed to obtain the estimator.</p><p>The proposed estimator is shown to have smaller mean squared error for certain choice of the shrinkage weight factor y(<strong>×</strong>) and suitable region R.</p><p>Expressions for Bias, Mean squared error (MSE), Expected sample size [E (n/a, R)], Expected sample size proportion [E(n/a,R)/n], probability for avoiding the second sample and percentage of overall sample saved  for the proposed estimator are derived.</p><p>Numerical results and conclusions for the expressions mentioned above were displayed when the consider estimator are testimator of level of significanceD.</p><p>Comparisons with the minimax estimator and with the most recent studies were made to shown the effectiveness of the proposed estimator.</p>

scholarly journals Performance Analysis of AOA-Based Localization Using the LS Approach: Explicit Expression of Mean-Squared Error

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Byung-Kwon Son ◽  
Do-Jin An ◽  
Joon-Ho Lee
Keyword(s):  
Explicit Expression ◽  
Mean Squared Error ◽  
Weighted Least Squares ◽  
Localization Algorithm ◽  
Angle Of Arrival ◽  
Squared Error ◽  
Distance Weighted ◽  
The Mean ◽  
Passive Localization ◽  
Location Estimate

In this paper, a passive localization of the emitter using noisy angle-of-arrival (AOA) measurements, called Brown DWLS (Distance Weighted Least Squares) algorithm, is considered. The accuracy of AOA-based localization is quantified by the mean-squared error. Various estimates of the AOA-localization algorithm have been derived (Doğançay and Hmam, 2008). Explicit expression of the location estimate of the previous study is used to get an analytic expression of the mean-squared error (MSE) of one of the various estimates. To validate the derived expression, we compare the MSE from the Monte Carlo simulation with the analytically derived MSE.

Estimation of critical torque using intermittent isometric maximal voluntary contractions of the quadriceps in humans

2009 ◽  
Vol 106 (3) ◽  
pp. 975-983 ◽  
Author(s):  
Mark Burnley
Keyword(s):  
Laboratory Tests ◽  
Mean Squared Error ◽  
Voluntary Activation ◽  
Peripheral Fatigue ◽  
Contraction Time ◽  
Twitch Interpolation ◽  
Squared Error ◽  
The Mean ◽  
Duration Relationship ◽  
Voluntary Contractions

To determine whether the asymptote of the torque-duration relationship (critical torque) could be estimated from the torque measured at the end of a series of maximal voluntary contractions (MVCs) of the quadriceps, eight healthy men performed eight laboratory tests. Following familiarization, subjects performed two tests in which they were required to perform 60 isometric MVCs over a period of 5 min (3 s contraction, 2 s rest), and five tests involving intermittent isometric contractions at ∼35–60% MVC, each performed to task failure. Critical torque was determined using linear regression of the torque impulse and contraction time during the submaximal tests, and the end-test torque during the MVCs was calculated from the mean of the last six contractions of the test. During the MVCs voluntary torque declined from 263.9 ± 44.6 to 77.8 ± 17.8 N·m. The end-test torque was not different from the critical torque (77.9 ± 15.9 N·m; 95% paired-sample confidence interval, −6.5 to 6.2 N·m). The root mean squared error of the estimation of critical torque from the end-test torque was 7.1 N·m. Twitch interpolation showed that voluntary activation declined from 90.9 ± 6.5% to 66.9 ± 13.1% ( P < 0.001), and the potentiated doublet response declined from 97.7 ± 23.0 to 46.9 ± 6.7 N·m ( P < 0.001) during the MVCs, indicating the development of both central and peripheral fatigue. These data indicate that fatigue during 5 min of intermittent isometric MVCs of the quadriceps leads to an end-test torque that closely approximates the critical torque.

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