Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (4): 115-125.doi: 10.16088/j.issn.1001-6600.2021091402

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Principal Component Liu Estimation Method of the Equation    Constrained Ⅲ-Conditioned Least Squares

WENG Ye1, SHAO Desheng1,2*, GAN Shu1,3   

  1. 1. Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming Yunnan 650093, China;
    2. Yunnan Earthquake Agency, Kunming Yunnan 650041, China;
    3. Plateau Mountain Spatial Information SurveyTechnique Application Engineering Research Center at Yunnan Province’s University, Kunming Yunnan 650093, China
  • Published:2022-08-05

Abstract: For the parameter estimation of the Ⅲ-conditioned adjustment problem, reasonable equality prior information among parameters helps to improve the accuracy of the model solution. The joint calculation is carried out under the sample information and the prior information of the equation. Based on the morbid least squares adjustment criterion, a new biased estimation algorithm, principal component Liu estimation, is constructed through the principal component estimation and Liu estimation. The parameter solution of theprincipal component Liu estimation with equality constrained least squares is derived, and the formula of correction factor is derived by using the principle of mean square error minimization. The effectiveness and reliability of the proposed method are verified by an example, which can be applied to the problem of solving Ⅲ-conditioned least squares parameters with equality constraints.

Key words: equation prior information, principal component estimation, Liu estimation, principal component Liu estimate, correction factor

CLC Number: 

  • O212
[1] 杨元喜, 曾安敏. 大地测量数据融合模式及其分析[J].武汉大学学报(信息科学版), 2008, 33(8): 771-774.
[2]舒红, 史文中. 浅谈测量平差到空间数据分析的可靠性理论延伸[J].武汉大学学报(信息科学版), 2018, 43(12): 1979-1985,1993.
[3]杨元喜, 曾安敏, 景一帆. 函数模型和随机模型双约束的GNSS数据融合及其性质[J].武汉大学学报(信息科学版), 2014, 39(2): 127-131.
[4]HOERL A E, KENNARD R W. Ridge regression: biased estimation for non-orthogonal problems[J].Technometrics, 2000, 42(1): 80-86.
[5]HOERL A E, KENNARD R W. Ridge regression: applications to nonorthogonal problems[J].Technometrics, 1970, 12(1): 69-82.
[6]MASSY W F. Principal compoents regression in exploratory statistical research[J].Journal of the American Statistical Association,1965,60(309):234-256.
[7]LIU K J. A new class of blased estimate in linear regression[J].Communications in Statistics-Theory and Methods, 1993, 22(2): 393-402.
[8]雷庆祝. 线性模型中回归系数岭估计的相合性[J].广西师范大学学报(自然科学版), 1992, 10(1): 21-24.
[9]雷庆祝, 王成名, 王炜炘. 线性模型中误差方差岭估计的大样本性质[J].广西师范大学学报(自然科学版), 1994, 12(2): 8-14.
[10]郭双, 王肖南. 线性测量误差模型的Liu估计[J].统计与决策, 2015(17): 68-70.
[11]吴平, 李开丁. 线性回归模型Liu估计的新研究[J].应用数学, 2008, 21(S1): 37-39.
[12]廖勋. 线性等式约束的奇异线性模型的Liu型估计[J].重庆工商大学学报(自然科学版), 2012, 29(3): 7-11.
[13]黄文焕, 戚佳金, 黄南天. 带线性约束的回归模型参数的Liu估计方法[J].系统科学与数学, 2009, 29(7): 937-946.
[14]黄荣臻, 朱宁, 邓超海, 等. 线性回归模型的一类新约束型LIU估计[J].西南师范大学学报(自然科学版), 2019, 44(11): 1-10.
[15]BAYE M R, PARKER D F. Combining ridge and principal component regression:a money demand illustration[J].Communications in Statistics-Theory and Methods, 1984, 13(2): 195-205.
[16]武汉大学测绘学院测量平差学科组. 误差理论与测量平差基础[M].第3版.武汉:武汉大学出版社, 2014.
[17]崔希璋,放宗铸,陶本藻,等. 广义测量平差[M].武汉: 武汉大学出版社,2009.
[18]GOLUB G H, VAN LOAN C F. An analysis of the total least squares problem[J].SIAM Journal on Numerical Analysis, 1980, 17(6): 883-893.
[19]鲁铁定. 总体最小二乘平差理论及其在测绘数据处理中的应用[D].武汉: 武汉大学,2010.
[20]林东方, 朱建军, 宋迎春, 等. 正则化的奇异值分解参数构造法[J].测绘学报, 2016, 45(8): 883-889.
[21]姜兆英, 刘国林, 于胜文. 病态方程基于Liu估计的一种迭代估计新方法[J].武汉大学学报(信息科学版), 2017, 42(8): 1172-1178.
[22]魏文科, 王昕. 部分线性回归模型的主成分Liu估计[J].北京信息科技大学学报(自然科学版), 2018, 33(4): 47-53.
[23]王乐洋, 于冬冬. 病态总体最小二乘问题的虚拟观测解法[J].测绘学报, 2014, 43(6): 575-581.
[24]朱英刚, 闫有朋, 王良. 带等式约束的加权最小二乘法参数估计[J].中国电力, 2008, 41(7): 25-27.
[25]嵇昆浦. 等式约束病态总体最小二乘模型的正则化解及其精度评定[J].大地测量与地球动力学, 2019, 39(12): 1304-1309.
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