A projector oriented approach to the best linear unbiased estimator

Statistical Papers ◽

10.1007/s00362-009-0252-6 ◽

2009 ◽

Vol 50 (4) ◽

pp. 721-733 ◽

Author(s):

Oskar Maria Baksalary ◽

Götz Trenkler

Keyword(s):

Unbiased Estimator ◽

Best Linear Unbiased Estimator ◽

Best Linear Unbiased ◽

Oriented Approach

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Best linear unbiased estimator of the Rayleigh scale parameter based on fairly large censored samples

IEEE Transactions on Reliability ◽

10.1109/24.387386 ◽

1995 ◽

Vol 44 (2) ◽

pp. 302-309 ◽

Author(s):

A. Adatia

Keyword(s):

Unbiased Estimator ◽

Scale Parameter ◽

Best Linear Unbiased Estimator ◽

Censored Samples ◽

Best Linear Unbiased

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Quality control and gap-filling of PM10 daily mean concentrations with the best linear unbiased estimator

Environmental Monitoring and Assessment ◽

10.1007/s10661-017-6273-z ◽

2017 ◽

Vol 189 (11) ◽

Author(s):

R. Sozzi ◽

A. Bolignano ◽

S. Ceradini ◽

M. Morelli ◽

I. Petenko ◽

...

Keyword(s):

Quality Control ◽

Unbiased Estimator ◽

Best Linear Unbiased Estimator ◽

Gap Filling ◽

Best Linear Unbiased

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A Best Linear Unbiased Estimator of Rβ with a Scalar Variance Matrix

Econometric Theory ◽

10.1017/s0266466600005569 ◽

1990 ◽

Vol 6 (4) ◽

pp. 490-490

Author(s):

R.W. Farebrother

Keyword(s):

Unbiased Estimator ◽

Best Linear Unbiased Estimator ◽

Variance Matrix ◽

Scalar Variance ◽

Best Linear Unbiased

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Improving the best linear unbiased estimator for the scale parameter of symmetric distributions by using the absolute value of ranked set samples

Journal of Applied Statistics ◽

10.1080/0266476022000030039 ◽

2003 ◽

Vol 30 (3) ◽

pp. 253-265 ◽

Author(s):

Gang Zheng ◽

Mohammad Al-Saleh

Keyword(s):

Unbiased Estimator ◽

Scale Parameter ◽

Best Linear Unbiased Estimator ◽

Symmetric Distributions ◽

Absolute Value ◽

Best Linear Unbiased ◽

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Obtaining the Best Linear Unbiased Estimator of Noisy Signals by Non-Gaussian Component Analysis

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings ◽

10.1109/icassp.2006.1660727 ◽

2006 ◽

Author(s):

M. Sugiyama ◽

M. Kawanabe ◽

G. Blanchard ◽

V. Spokiny ◽

K.-R. Muller

Keyword(s):

Unbiased Estimator ◽

Component Analysis ◽

Gaussian Component ◽

Best Linear Unbiased Estimator ◽

Noisy Signals ◽

Best Linear Unbiased ◽

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On equality of ordinary least squares estimator, best linear unbiased estimator and best linear unbiased predictor in the general linear model

Journal of Statistical Planning and Inference ◽

10.1016/j.jspi.2008.08.015 ◽

2009 ◽

Vol 139 (4) ◽

pp. 1522-1529 ◽

Author(s):

Yonghui Liu

Keyword(s):

Least Squares ◽

Linear Model ◽

Unbiased Estimator ◽

General Linear Model ◽

Ordinary Least Squares ◽

Least Squares Estimator ◽

Best Linear Unbiased Estimator ◽

Best Linear Unbiased Predictor ◽

Ordinary Least Squares Estimator ◽

Best Linear Unbiased

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A bound for the Euclidean norm of the difference between the best linear unbiased estimator and a linear unbiased estimator

Journal of Geodesy ◽

10.1007/s00190-002-0262-9 ◽

2002 ◽

Vol 76 (6-7) ◽

pp. 317-322 ◽

Author(s):

J. Mäkinen

Keyword(s):

Unbiased Estimator ◽

Euclidean Norm ◽

Best Linear Unbiased Estimator ◽

Best Linear Unbiased ◽

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Best Linear Unbiased Estimator of the Parameter of the Rayleigh Distribution - Part I: Small Sample Theory for Censored Order Statistics

IEEE Transactions on Reliability ◽

10.1109/tr.1973.5216019 ◽

1973 ◽

Vol R-22 (1) ◽

pp. 27-34 ◽

Author(s):

Danny D. Dyer ◽

Charles W. Whisenand

Keyword(s):

Order Statistics ◽

Unbiased Estimator ◽

Rayleigh Distribution ◽

Small Sample ◽

Best Linear Unbiased Estimator ◽

Best Linear Unbiased

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Estimation of the Parameters of the Uniform Distribution Over [kθ, (k+ 1)θ]

Calcutta Statistical Association Bulletin ◽

10.1177/0008068319960310 ◽

1996 ◽

Vol 46 (3-4) ◽

pp. 263-268 ◽

Author(s):

P. Yageen Thomas

Keyword(s):

Uniform Distribution ◽

Unbiased Estimator ◽

Subject Classification ◽

Best Linear Unbiased Estimator ◽

Primary 62H12 ◽

Secondary 62G30 ◽

Best Linear Unbiased ◽

Improved Estimators

From the available literature on estimation of the parameters of the uniform distribution over [ kθ, kθ + θ], we find the necessity to construct improved estimators of the parameter θ when k is known. In this paper a new estimator of θ is proposed when k is known and its performance is compared with best linear unbiased estimator of θ based on two extreme observations. A MS Subject Classification: Primary: 62H12; Secondary: 62G30, 62B05.

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PERMASALAHAN AUTOKORELASI PADA ANALISIS REGRESI LINIER SEDERHANA

Jurnal Matematika UNAND ◽

10.25077/jmu.2.2.26-34.2013 ◽

2013 ◽

Vol 2 (2) ◽

pp. 26

Author(s):

Nadia Utika ◽

Maiyastri . ◽

Hazmira Yozza

Keyword(s):

Monte Carlo ◽

Unbiased Estimator ◽

Best Linear Unbiased Estimator ◽

Best Linear Unbiased

Pada analisis regresi linier sederhana model yang terbentuk harus memenuhibeberapa asumsi yang dikenal dengan asumsi linier klasik. Dalam asumsi linier klasikdinyatakan bahwa tidak terdapat korelasi antar galat yang disebut autokorelasi. Bila au-tokorelasi terjadi pada model regresi linier sederhana maka pengaruhnya terlihat padapenduga parameter yang tidak lagi BLUE (Best Linear Unbiased Estimator), yaitu pen-duga tidak bias dan linier, namun tidak lagi memiliki ragam yang minimum. Pengaruhautokorelasi juga dilihat menggunakan simulasi Monte Carlo. Simulasi Monte Carlomenggunakan nilai bias dan Kuadrat Tengah Galat (KTG) dengan berbagai ukuransampel dan koesien autokorelasi yang bervariasi. Hasil simulasi menunjukkan semakinbesar ukuran sampel maka bias dan KTG semakin kecil dan semakin besar nilai koesienautokorelasi maka bias dan KTG semakin besar.

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