error in variables
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Nonparametric regression with error-in-variables model based on different kernel functions

Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler ◽

10.20290/estubtdb.1015940 ◽

2021 ◽

Author(s):

Dursun AYDIN ◽

Ersin YILMAZ

Keyword(s):

Nonparametric Regression ◽

Kernel Functions ◽

Error In Variables ◽

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Block coordinate descent algorithm improves variable selection and estimation in error‐in‐variables regression

Genetic Epidemiology ◽

10.1002/gepi.22430 ◽

2021 ◽

Author(s):

Célia Escribe ◽

Tianyuan Lu ◽

Julyan Keller‐Baruch ◽

Vincenzo Forgetta ◽

Bowei Xiao ◽

...

Keyword(s):

Variable Selection ◽

Coordinate Descent ◽

Block Coordinate Descent ◽

Descent Algorithm ◽

Error In Variables ◽

Coordinate Descent Algorithm ◽

Variable Selection And Estimation

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Variable Selection in High-Dimensional Error-in-Variables Models via Controlling the False Discovery Proportion

Communications in Mathematics and Statistics ◽

10.1007/s40304-020-00233-4 ◽

2021 ◽

Author(s):

Xudong Huang ◽

Nana Bao ◽

Kai Xu ◽

Guanpeng Wang

Keyword(s):

Variable Selection ◽

High Dimensional ◽

Dimensional Error ◽

Error In Variables ◽

False Discovery ◽

False Discovery Proportion

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Error-in-variables model of Malacca wind direction data with the von Mises distribution in southwest monsoon

Applied Mathematical Sciences ◽

10.12988/ams.2021.914521 ◽

2021 ◽

Vol 15 (9) ◽

pp. 471-479

Author(s):

Nurkhairany Amyra Mokhtar ◽

Basri Badyalina ◽

Kerk Lee Chang ◽

Fatin Farazh Ya'acob ◽

Ahmad Faiz Ghazali ◽

...

Keyword(s):

Wind Direction ◽

Southwest Monsoon ◽

Von Mises Distribution ◽

Error In Variables ◽

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Calibrated zero-norm regularized LS estimator for high-dimensional error-in-variables regression

Statistica Sinica ◽

10.5705/ss.202018.0507 ◽

2021 ◽

Author(s):

Ting Tao ◽

Shaohua Pan ◽

Shujun Bi

Keyword(s):

High Dimensional ◽

Dimensional Error ◽

Error In Variables ◽

Ls Estimator ◽

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Competitive Algorithms for Large Scale Positive Definite Linear Systems of Equations Using an Error in Variables Optimization Model

Journal of Zankoy Sulaimani - Part A ◽

10.17656/jzs.10821 ◽

2020 ◽

Vol 22 (2) ◽

pp. 199-216

Author(s):

Nezam Mahdavi-Amiri ◽

◽

Negin Bagherpour ◽

Keyword(s):

Linear Systems ◽

Optimization Model ◽

Large Scale ◽

Positive Definite ◽

Systems Of Equations ◽

Error In Variables ◽

Competitive Algorithms ◽

Linear Systems Of Equations

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Scalable interpretable learning for multi-response error-in-variables regression

Journal of Multivariate Analysis ◽

10.1016/j.jmva.2020.104644 ◽

2020 ◽

Vol 179 ◽

pp. 104644

Author(s):

Jie Wu ◽

Zemin Zheng ◽

Yang Li ◽

Yi Zhang

Keyword(s):

Response Error ◽

Error In Variables

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Reactivity Ratios Estimation of the Free‐Radical Polymerization of Itaconic Acid and N ‐Vinyl‐2‐Pyrrolidone by the Error‐in‐Variables Methodology

Macromolecular Reaction Engineering ◽

10.1002/mren.202000026 ◽

2020 ◽

Vol 14 (6) ◽

pp. 2000026

Author(s):

Bruno Vasconcellos Krieger ◽

Pedro Henrique Hermes Araujo ◽

Claudia Sayer

Keyword(s):

Free Radical ◽

Radical Polymerization ◽

Itaconic Acid ◽

Free Radical Polymerization ◽

Reactivity Ratios ◽

Error In Variables

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Efficient Identification of Error-in-Variables Switched Systems via a Sum-of-Squares Polynomial Based Subspace Clustering Method

2019 IEEE 58th Conference on Decision and Control (CDC) ◽

10.1109/cdc40024.2019.9029570 ◽

2019 ◽

Author(s):

B. Ozbay ◽

O. Camps ◽

M. Sznaier

Keyword(s):

Switched Systems ◽

Subspace Clustering ◽

Sum Of Squares ◽

Clustering Method ◽

Error In Variables

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Convergence rates in the weak law of large numbers for weighted sums of i.i.d. random variables and applications in errors-in-variables models

Stochastics and Dynamics ◽

10.1142/s0219493719500412 ◽

2019 ◽

Vol 19 (06) ◽

pp. 1950041 ◽

Author(s):

Mingyang Zhang ◽

Pingyan Chen ◽

Soo Hak Sung

Keyword(s):

Regression Models ◽

Law Of Large Numbers ◽

Convergence Rates ◽

Sufficient Conditions ◽

Random Variables ◽

Weighted Sums ◽

Errors In Variables ◽

Error In Variables ◽

Large Numbers ◽

Necessary And Sufficient

We prove necessary and sufficient conditions for the convergence rates in the weak law of large numbers for weighted sums of independent and identically distributed (i.i.d.) random variables. This result is applied to simple linear error-in-variables regression models.

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