PARAMETERS ESTIMATION OF AUTOREGRESSION WITH STRUCTURED TOTAL LEAST SQUARES
Hu Chuan 1) ; and Chen Yi1,2)
1)College of Surveying and Geoinformatics, Tongji University, Shanghai 200092;2)Key Laboratory of Modern Engineering Surveying, SBSM, Shanghai 200092
Abstract The structured total least squares not only take into account the errors of coefficient but also consider the identical elements have attained the same correction value in coefficient or in argument of vector of observation L and coefficient matrix A. This method make the adjustment theory more strict than others such as least squares and total least squares.In this contribution that we give an simply iteration algorithm for structured total least squares,and give a numerical example to demonstrate the efficiency and feasibility,as well as the advantages of the structured total least squares for AutoRegresion parameters estimation at last .
Key words :
autoregresion
structured total least squares
parameters estimation
observation vector
coefficient matrix
Received: 01 January 1900
Corresponding Authors:
Hu Chuan
Cite this article:
Hu Chuan ,and Chen Yi. PARAMETERS ESTIMATION OF AUTOREGRESSION WITH STRUCTURED TOTAL LEAST SQUARES[J]. , 2013, 33(1): 45-47.
Hu Chuan ,and Chen Yi. PARAMETERS ESTIMATION OF AUTOREGRESSION WITH STRUCTURED TOTAL LEAST SQUARES[J]. jgg, 2013, 33(1): 45-47.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2013/V33/I1/45
[1]
XIA Yuguo,SONG Yingchun,ZHAO Shaojie. An Iterative Algorithm for Adjustment Model with Uncertainty and Inequality Constraints [J]. jgg, 2020, 40(2): 152-155.
[2]
WU Guangming,LU Tieding. New Methods of Ill-Posed Total Least-Squares with Targeting Singular Value Corrections [J]. jgg, 2019, 39(8): 856-862.
[3]
WANG Leyang,DING Rui,WU Lulu. Partial EIV Model Variance Component Estimation and Accuracy Evaluation of SUT Method by Deviation Correction [J]. jgg, 2019, 39(7): 711-716.
[4]
WU Guangming,LU Tieding,DENG Xiaoyuan,QIU Dechao. A New Method of Constructing Regularized Matrix [J]. jgg, 2019, 39(1): 61-65.
[5]
DENG Xingsheng,HUANG Xiaopeng,PENG Sichun. Weighted Total Least-Squares Adjustment with Partial Prior Random Parameter [J]. jgg, 2018, 38(9): 968-973.
[6]
XIAO Zhaobing,SONG Yingchun,XIE Xuemei. Uncertainty Analysis on Improved Nonlinear Time Series Evolution System of Slope Displacements [J]. jgg, 2018, 38(2): 136-140.
[7]
WANG Leyang,WEN Guisen. A Kind of Polynomial Fitting Method Based on Partial EIV Model [J]. jgg, 2017, 37(7): 737-742.
[8]
WANG Leyang,CHEN Hanqing,WEN Yangmao. Analysis of Crust Deformation Based on Total Least Squares Collocation [J]. jgg, 2017, 37(2): 163-168.
[9]
WANG Qisheng. The New Algorithm for Multivariate Weighted Total Least Squares [J]. jgg, 2017, 37(12): 1281-1284.
[10]
HU Ming,ZHANG Weimin,YANG Meng,TIAN Wei,ZHONG Min. Robust Trend Estimation and Its Application in the Free Fall Absolute Gravimeter [J]. jgg, 2016, 36(9): 833-836.
[11]
FAN Qian. Parameter Estimation Method for Nonlinear Model Based on Improved Fruit Fly Optimization Algorithm [J]. jgg, 2016, 36(12): 1092-1096.
[12]
WANG Qisheng,YANG Dehong,YANG Tengfei. Structured Total Least Squares for Space Straight Line Fitting [J]. jgg, 2015, 35(3): 433-435.
[13]
CHEN Xiaolin,SONG Yingchun,ZOU Bo. Adjustment of Part Bounded Uncertain Data [J]. jgg, 2015, 35(1): 118-121.
[14]
ZHANG Jun,DU Zhixing,DU Ning,ZHANG Xianyun. Penalized Least Square Estimator for Non-Linear Models [J]. jgg, 2015, 35(1): 122-125.
[15]
Cang Guihua,Li Mingfeng,Yue Jianping. STUDY ON POINT CLOUDS PLANE FITTING WITH WEIGHTED TOTAL
LEAST SQUARES BASED ON INCIDENCE ANGLE WEIGHTING [J]. jgg, 2014, 34(3): 95-98.