检索结果-南通市图书馆

Deleting Outliers in robust regression with Mixed Integer Programming

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Acta Mathematicae Applicatae Sinica 2005年第2期21卷 323-334页

作者： Georgios Zioutas Antonios Avramidis Division of Computational Methods and Computer Programming General Department Faculty of Technology Aristotle University of Thessaloniki 541 24 Thessaloniki GreeceDivision of Computational Methods and Computer Programming General Department Faculty of Technology Aristotle University of Thessaloniki 541 24 Thessaloniki Greece

In robust regression we often have to decide how many are the unusualobservations, which should be removed from the sample in order to obtain better fitting for the restof the observations. Generally, we use the basic principle of LTS, which is to fit the majority ofthe data, identifying as outliers those points that cause the biggest damage to the robust ***, in the LTS regression method the choice of default values for high break down-point affectsseriously the efficiency of the estimator. In the proposed approach we introduce penalty cost fordiscarding an outlier, consequently, the best fit for the majority of the data is obtained bydiscarding only catastrophic observations. This penalty cost is based on robust design weights andhigh break down-point residual scale taken from the LTS estimator. The robust estimation is obtainedby solving a convex quadratic mixed integer programming problem, where in the objective functionthe sum of the squared residuals and penalties for discarding observations is minimized. Theproposed mathematical programming formula is suitable for small-sample data. Moreover, we conduct asimulation study to compare other robust estimators with our approach in terms of their efficiencyand robustness.

关键词： robust regression quadratic mixed integer programming least trimmedsquares deleting outliers penalty methods

A COMPARISON BETWEEN TWO robust regression ESTIMATORS BY MEANS OF robust COVARIANCES

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Applied Mathematics(A Journal of Chinese Universities) 1998年第2期13卷 207-214页

作者： MA JIANGHONG, WEI GUANGSHENG AND WANG KANMIN

Abstract Two classes of Mallows GM estimators with invariance are considered in the stochastic linear regression model. Some of their asymptotic properties are described, and the fitted value influence and variance co... 详细信息

关键词： robust regression fitted-value influence robust covariance

Bayesian regularized quantile regression:A robust alternative for genome-based prediction of skewed data

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

The Crop Journal 2020年第5期8卷 713-722页

作者： Paulino Pérez-Rodríguez Osval A.Montesinos-López Abelardo Montesinos-López JoséCross Colegio de Postgraduados MontecillosEdo.de México 56230Mexico Facultad de Telemática Universidad de ColimaColima 28040Mexico Departamento de Matemáticas Centro Universitario de Ciencias Exactas e Ingenierías(CUCEI)Universidad de GuadalajaraGuadalajaraJalisco 44430Mexico International Maize and Wheat Improvement Center(CIMMYT) Km 45 Carretera México-VeracruzTexcocoEdo.de México 56237Mexico

Genomic prediction(GP)has become a valuable tool for predicting the performance of selection candidates for the next breeding cycle.A vast majority of statistical linear models on which GP is based rely on the assumption of normality of the residuals and therefore on the response variable *** this study,we propose to use Bayesian regularized quantile regression(BRQR)in the context of GP;the model has been successfully used in other research *** evaluated the prediction ability of the proposed model and compared it with the Bayesian ridge regression(BRR;equivalent to genomic best linear unbiased predictor,GBLUP).In addition,BLUP can be used with pedigree information obtained from the coefficient of coancestry(ABLUP).We have found that the prediction ability of BRQR is comparable to that of BRR and,in some cases,better;it also has the potential to efficiently deal with outliers.A program written in the R statistical package is available as Supplementary material.

关键词： Laplace distribution robust regression Bayesian quantile regression Genomic-enabled prediction

A robust sparse representation algorithm based on adaptive joint dictionary

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

CAAI Transactions on Intelligence Technology 2023年第2期8卷 430-439页

作者： Ying Tong Rui Chen Minghu Wu Yang Jiao College of Information and Communication Engineering Nanjing Institute of TechnologyNanjingChina College of Electrical and Electronic Engineering Hubei University of TechnologyWuhanChina Department of Statistics University of TorontoTorontoOntarioCanada

Sparse representation based on dictionary construction and learning methods have aroused interests in the field of face *** at the shortcomings of face feature dictionary not‘clean’and noise interference dictionary not‘representative’in sparse representation classification model,a new method named as robust sparse representation is proposed based on adaptive joint dictionary(RSR-AJD).First,a fast lowrank subspace recovery algorithm based on LogDet function(Fast LRSR-LogDet)is proposed for accurate low-rank facial intrinsic dictionary representing the similar structure of human face and low computational ***,the Iteratively Reweighted robust Principal Component Analysis(IRRPCA)algorithm is used to get a more precise occlusion dictionary for depicting the possible discontinuous interference information attached to human face such as glasses occlusion or scarf occlusion ***,the above Fast LRSR-LogDet algorithm and IRRPCA algorithm are adopted to construct the adaptive joint dictionary,which includes the low-rank facial intrinsic dictionary,the occlusion dictionary and the remaining intra-class variant dictionary for robust sparse *** conducted on four popular databases(AR,Extended Yale B,LFW,and Pubfig)verify the robustness and effectiveness of the authors’method.

关键词： facial recognition feature extraction noise dictionary robust regression

Linear regression under model uncertainty

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Probability, Uncertainty and Quantitative Risk 2023年第4期8卷 523-546页

作者： Shuzhen Yang Jianfeng Yao Zhongtai Securities Institute for Financial Studies Shandong UniversityJinan 250100China School of Data Science The Chinese University of Hong KongShenzhen 518172China

We reexamine the classical linear regression model when it is subject to two types of uncertainty:(i)some covariates are either missing or completely inaccessible,and(ii)the variance of the measurement error is undetermined and changing according to a mechanism unknown to the *** following the recent theory of sublinear expectation,we propose to characterize such mean and variance uncertainty in the response variable by two specific nonlinear random variables,which encompass an infinite family of probability distributions for the response variable in the sense of(linear)classical probability *** approach enables a family of estimators under various loss functions for the regression parameter and the parameters related to model *** consistency of the estimators is established under mild conditions in the data generation *** applications are introduced to assess the quality of the approach including a forecasting model for the S&P Index.

关键词： robust regression G-normal distribution Distribution uncertainty Heteroscedastic error S&P index

Industrial Food Quality Analysis Using New k-Nearest-Neighbour methods

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2021年第5期67卷 2681-2694页

作者： Omar Fetitah Ibrahim M.Almanjahie Mohammed Kadi Attouch Salah Khardani Laboratory of Statistics and Stochastic Processes University of Djillali LiabesSidi Bel Abbes22000Algeria Department of Mathematics College of ScienceKing Khalid UniversityAbha62529Saudi Arabia Statistical Research and Studies Support Unit King Khalid UniversityAbha62529Saudi Arabia Facultédes sciences de Tunis Laboratoire des Réseaux Intelligents et NanotechnologieTunisTunisia

The problem of predicting continuous scalar outcomes from functional predictors has received high levels of interest in recent years in many fields,especially in the food *** k-nearest neighbor(k-NN)method of Near-Infrared Reflectance(NIR)analysis is practical,relatively easy to implement,and becoming one of the most popular methods for conducting food quality based on NIR *** k-NN is often named k nearest neighbor classifier when it is used for classifying categorical variables,while it is called k-nearest neighbor regression when it is applied for predicting noncategorical *** objective of this paper is to use the functional Near-Infrared Reflectance(NIR)spectroscopy approach to predict some chemical components with some modern statistical models based on the kernel and k-Nearest Neighbour *** this paper,three NIR spectroscopy datasets are used as examples,namely Cookie dough,sugar,and tecator ***,we propose three models for this kind of data which are Functional Nonparametric regression,Functional robust regression,and Functional Relative Error regression,with both kernel and k-NN approaches to compare between *** experimental result shows the higher efficiency of k-NN predictor over the kernel *** predictive power of the k-NN method was compared with that of the kernel method,and several real data sets were used to determine the predictive power of both methods.

关键词： Functional data analysis classical regression robust regression relative error regression kernel method k-NN method near-infrared spectroscopy

Mixed-Species Allometric Equations to Quantify Stem Volume and Tree Biomass in Dry Afromontane Forest of Ethiopia

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Open Journal of Forestry 2022年第3期12卷 263-296页

作者： Mindaye Teshome Carlos Moreira Miquelino Eleto Torres Gudeta Weldesemayat Sileshi Patricia Povoa de Mattos Evaldo Muñoz Braz Hailemariam Temesgen Samuel José Silva Soares da Rocha Mehari Alebachew Departamento de Engenharia Florestal Universidade Federal de Vi&#231 osa (UFV) Vi&#231 osa MG Brazil Central Ethiopia Environment and Forest Research Centre Addis Ababa Ethiopia University of Kwazulu-Natal Pietermaritzburg South Africa Embrapa Florestas Colombo PR Brazil Department of Forest Engineering Resources and Management College of Forestry Oregon State University Corvallis USA Departamento de Ciências Florestais Universidade Federal de Lavras (UFLA) Lavras MG Brazil

Volume and biomass equations are essential tools to determine forest productivity and enable forest managers to make informed decisions. However, volume and biomass estimation equations are scarce for Afromontane forests in Africa in general and Ethiopia in particular. This limits our knowledge of the standing volume of wood, biomass, and carbon stock of the forests therein. In this study, we developed a new mixed-species volume and biomass equations for Afromontane forests and compared them with generic pantropical and local models. A total of 193 sampled trees from seven dominant tree species were used to develop the equations. Various volume and biomass equations were fitted using robust linear and nonlinear regression. Model comparison indicated that the best model to estimate stem volume was ln(v)=-9.909+ 0.954*ln(d2h), whereas the best model to estimate biomass was ln(b)=-2.983+ 0.949*ln(ρd2h) . These equations explained over 85% of the variations in the stem volume and biomass measurements. The mean density and basal area of trees in the forest with d ≥ 2 cm was 631.5 stems·ha-1 and 24.4 m2·ha-1. Based on the newly developed equations, the forest has on average 303.0 m3·ha-1 standing volume of wood and 283.8 Mg·ha-1 biomass stock. The newly developed allometric equations derived from this study can be used to accurately determine the stem volume, biomass, and carbon storage in the Afromontane forests in Ethiopia and elsewhere with similar stand characteristics and ecological conditions. By contrast, the generic pan-tropical and other local models appear to provide biased estimates and are not suitable for dry Afromontane forests in Ethiopia. The estimated stem biomass and carbon stock in the Chilimo forest are comparable with the estimates from various tropical forests and woodlands elsewhere in Africa, indicating the importance of dry Afromontane fores

关键词： Carbon Stock Site-Specific Model robust regression Natural Forest