Note on Stepwise Least Squares

Goldberger, Arthur S.; Jochems, D. B.

doi:10.2307/2282335

Cited by 14 publications

(14 citation statements)

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“…The stepwise regression theory of TRICOR is described in numerous publications (Dixon, 1968;Draper & Smith, 1966;Efroymson, 1960;Goldberger, 1961;Goldberger & Jochems, 1961;Pope & Webster, 1972), and the maximum likelihood estimation and utility theory of MAP is documented in AFMPC publications (Dempsey & Fast, 1976: Dempsey, Fast, & Sellman, 1977. A brief description of the important characteristics of BAYS will be presented here, and a more detailed description is available in the computer-based SMSM program documentation library at AFHRL.…”

Section: I! Description Of Statistical Methodologiesmentioning

confidence: 99%

Predicting involuntary separation of enlisted personnel.

Walter¹

1980

PsycEXTRA Dataset

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Section: I! Description Of Statistical Methodologiesmentioning

confidence: 99%

Predicting involuntary separation of enlisted personnel.

Walter¹

1980

PsycEXTRA Dataset

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“…; tage leas t squares estimation which has recently been discussed by Freund, Vail, and Clunies·Ross (1961) and Goldberger and Jockems (1961). W e s hall con· s ider so me furt her generaliz ations and di scuss the matter more fully .…”

Section: Two Stage Least Squaresmentioning

confidence: 98%

Weak generalized inverses and minimum variance linear unbiased estimation

Goldman¹,

Zelen²

1964

J. RES. NATL. BUR. STAN. SECT. B. MATH. MATH. PHYS.

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This paper prese nts a unifi ed account of the theory of least square s and its adaptations to statis· tic al mode ls mo re compli cated than th e classical one. Firs t co mes a d e velopme nt of the prop e rties of weak ge ne ral ized matrix inve rses, a useful variant of th e more familiar pseudo·inverse. These properties are e mpl oyed in a proof of th e usual Gauss theorem, a nd in analyzin g th e case in whic h known linea r res traints are obeye d by the para me te rs. Anothe r situ ation treated is that of a s ingular varia nce-co variance matrix for the observati ons . Appli cations in clude the case of equi-co rrelated variabl es (i ncludin g es timation d es pite igno rance of th e co rre lation), lin ea r " res tra ints" s ubj ect to random error, and step wi se linear es timatio n.

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“…Firstly, solving the over-parameterization by selecting significant RPCs with stepwise selection. Goldberger and Jochems (Goldberger and Jochems 1961) had shown the detailed iteration of stepwise selection. Then, solving the RFM coefficients through orthogonal distance regression and fitting the residual with Fourier series.…”

Section: Rfm Based On Stepwise Regression and Orthogonal Distance Regmentioning

confidence: 99%

A New Optimized RFM of High-Resolution Satellite Imagery

Li¹,

Liu²,

Deng³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Over-parameterization and over-correction are two of the major problems in the rational function model (RFM). A new approach of optimized RFM (ORFM) is proposed in this paper. By synthesizing stepwise selection, orthogonal distance regression, and residual systematic error correction model, the proposed ORFM can solve the ill-posed problem and over-correction problem caused by constant term. The least square, orthogonal distance, and the ORFM are evaluated with control and check grids generated from satellite observation Terre (SPOT-5) high-resolution satellite data. Experimental results show that the accuracy of the proposed ORFM, with 37 essential RFM parameters, is more accurate than the other two methods, which contain 78 parameters, in cross-track and along-track plane. Moreover, the over-parameterization and over-correction problems have been efficiently alleviated by the proposed ORFM, so the stability of the estimated RFM parameters and its accuracy have been significantly improved.

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Note on Stepwise Least Squares

Cited by 14 publications

References 0 publications

Predicting involuntary separation of enlisted personnel.

Predicting involuntary separation of enlisted personnel.

Weak generalized inverses and minimum variance linear unbiased estimation

A New Optimized RFM of High-Resolution Satellite Imagery

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