scholarly journals A Least Squares Method for Variance Estimation in Heteroscedastic Nonparametric Regression

2014 ◽  
Vol 2014 ◽  
pp. 1-14
Author(s):  
Yuejin Zhou ◽  
Yebin Cheng ◽  
Tiejun Tong
Keyword(s):  
Least Squares ◽  
Nonparametric Regression ◽  
Variance Estimation ◽  
Least Squares Method ◽  
Asymptotic Properties ◽  
Error Variance ◽  
Simulation Studies ◽  
Least Squares Estimators ◽  
Slope Estimator ◽  
Better Than

Interest in variance estimation in nonparametric regression has grown greatly in the past several decades. Among the existing methods, the least squares estimator in Tong and Wang (2005) is shown to have nice statistical properties and is also easy to implement. Nevertheless, their method only applies to regression models with homoscedastic errors. In this paper, we propose two least squares estimators for the error variance in heteroscedastic nonparametric regression: the intercept estimator and the slope estimator. Both estimators are shown to be consistent and their asymptotic properties are investigated. Finally, we demonstrate through simulation studies that the proposed estimators perform better than the existing competitor in various settings.

scholarly journals Asymptotic normality of a simple linear EV regression model with martingale difference errors

Filomat ◽  
2014 ◽  
Vol 28 (9) ◽  
pp. 1817-1825
Author(s):  
Guo-Liang Fan ◽  
Tian-Heng Chen
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Measurement Errors ◽  
Least Squares Method ◽  
Error Variance ◽  
Martingale Difference ◽  
Errors In Variables ◽  
Ev Regression Model ◽  
Biased Estimators ◽  
Better Than

This paper considers the estimation of a linear EV (errors-in-variables) regression model under martingale difference errors. The usual least squares estimations lead to biased estimators of the unknown parametric when measurement errors are ignored. By correcting the attenuation we propose a modified least squares estimator for a parametric component and construct the estimators of another parameter component and error variance. The asymptotic normalities are also obtained for these estimators. The simulation study indicates that the modified least squares method performs better than the usual least squares method.

scholarly journals Method of determining the distance to the object by analyzing its image blur

Vestnik MGSU ◽  
2015 ◽  
pp. 140-151 ◽  
Author(s):  
Aleksey Alekseevich Loktev ◽  
Daniil Alekseevich Loktev
Keyword(s):  
Least Squares ◽  
Statistical Approach ◽  
Evaluation Method ◽  
Least Squares Method ◽  
Focal Length ◽  
Error Variance ◽  
Method Of Least Squares ◽  
Automated Control ◽  
Image Blur

In modern integrated monitoring systems and systems of automated control of technological processes there are several essential algorithms and procedures for obtaining primary information about an object and its behavior. The primary information is characteristics of static and moving objects: distance, speed, position in space etc. In order to obtain such information in the present work we proposed to use photos and video detectors that could provide the system with high-quality images of the object with high resolution. In the modern systems of video monitoring and automated control there are several ways of obtaining primary data on the behaviour and state of the studied objects: a multisensor approach (stereovision), building an image perspective, the use of fixed cameras and additional lighting of the object, and a special calibration of photo or video detector.In the present paper the authors develop a method of determining the distances to objects by analyzing a series of images using depth evaluation using defocusing. This method is based on the physical effect of the dependence of the determined distance to the object on the image from the focal length or aperture of the lens. When focusing the photodetector on the object at a certain distance, the other objects both closer and farther than a focal point, form a spot of blur depending on the distance to them in terms of images. Image blur of an object can be of different nature, it may be caused by the motion of the object or the detector, by the nature of the image boundaries of the object, by the object’s aggregate state, as well as by different settings of the photo-detector (focal length, shutter speed and aperture).When calculating the diameter of the blur spot it is assumed that blur at the point occurs equally in all directions. For more precise estimates of the geometrical parameters determination of the behavior and state of the object under study a statistical approach is used to determine the individual parameters and estimate their accuracy. A statistical approach is used to evaluate the deviation of the dependence of distance from the blur from different types of standard functions (logarithmic, exponential, linear). In the statistical approach the evaluation method of least squares and the method of least modules are included, as well as the Bayesian estimation, for which it is necessary to minimize the risks under different loss functions (quadratic, rectangular, linear) with known probability density (we consider normal, lognormal, Laplace, uniform distribution). As a result of the research it was established that the error variance of a function, the parameters of which are estimated using the least squares method, will be less than the error variance of the method of least modules, that is, the evaluation method of least squares is more stable. Also the errors’ estimation when using the method of least squares is unbiased, whereas the mathematical expectation when using the method of least modules is not zero, which indicates the displacement of error estimations. Therefore it is advisable to use the least squares method in the determination of the parameters of the function.In order to smooth out the possible outliers we use the Kalman filter to process the results of the initial observations and evaluation analysis, the method of least squares and the method of least three standard modules for the functions after applying the filter with different coefficients.

scholarly journals GM(1,1;λ) with Constrained Linear Least Squares

Axioms ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 278
Author(s):  
Ming-Feng Yeh ◽  
Ming-Hung Chang
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Model Fitting ◽  
Ordinary Least Squares ◽  
Linear Least Squares ◽  
Unknown Parameters ◽  
Constrained Problems ◽  
Linear Least Squares Problems ◽  
Ordinary Least Squares Method ◽  
Better Than

The only parameters of the original GM(1,1) that are generally estimated by the ordinary least squares method are the development coefficient a and the grey input b. However, the weight of the background value, denoted as λ, cannot be obtained simultaneously by such a method. This study, therefore, proposes two simple transformation formulations such that the unknown parameters, and can be simultaneously estimated by the least squares method. Therefore, such a grey model is termed the GM(1,1;λ). On the other hand, because the permission zone of the development coefficient is bounded, the parameter estimation of the GM(1,1) could be regarded as a bound-constrained least squares problem. Since constrained linear least squares problems generally can be solved by an iterative approach, this study applies the Matlab function lsqlin to solve such constrained problems. Numerical results show that the proposed GM(1,1;λ) performs better than the GM(1,1) in terms of its model fitting accuracy and its forecasting precision.

The Optimization of the PID Controller Using the Least Squares Method and the Improved Lbest PSO Algorithm

2013 ◽  
Vol 397-400 ◽  
pp. 1296-1303 ◽  
Author(s):  
Chuan Gui Yang ◽  
Zhao Jun Yang ◽  
Fei Chen ◽  
Yan Zhu ◽  
Ying Nan Kan ◽  
...  
Keyword(s):  
Least Squares ◽  
Pid Controller ◽  
Least Squares Method ◽  
Dynamic Performance ◽  
Pso Algorithm ◽  
Pid Tuning ◽  
Loading System ◽  
Hydraulic Servo ◽  
Tuning Scheme ◽  
Better Than

A self-adaptive PID tuning scheme is presented for the electro-hydraulic servo loading system. It requires the least squares method to identify the parameters of the transfer function of the electro-hydraulic servo loading system and utilizes the improved lbest PSO algorithm to optimize the PID controller. The scheme can provide the optimal PID parameters so that the dynamic performance and stability of the electro-hydraulic servo loading system are improved. Results show the fact that the dynamic performance and stability of the system are improved by the scheme. And in terms of optimization of PID controller, the improved lbest PSO algorithm is better than the lbest PSO algorithm and Ziegler-Nichols method.

scholarly journals A note non Nonparametric Regression Modeling using a Density Function

2020 ◽  
Vol 7 (2) ◽  
pp. 993-1000
Author(s):  
Jakperik Dioggban
Keyword(s):  
Regression Model ◽  
Density Function ◽  
Nonparametric Regression ◽  
Model Simulation ◽  
Smoothing Splines ◽  
Bias Reduction ◽  
Simulation Studies ◽  
Lack Of Fit ◽  
Classical Regression ◽  
Better Than

The nonparametric regression offers alternative to classical regression analysis when the data are not well behaved or when the classical regression model shows significant lack of fit. In recent years, It has been applied using Kernel estimators and the smoothing splines, but these methods wields some bias of estimation. In this study, a semi-parametric multiplicative bias reduction density function was used to develop a non parametric regression model. Simulation studies conducted showed that the proposed estimator performs better than both the Kernel and the smoothing splines estimators especially with large samples

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