[图书][B] Robust statistics: theory and methods (with R)
A new edition of this popular text on robust statistics, thoroughly updated to include new and
improved methods and focus on implementation of methodology using the increasingly …
improved methods and focus on implementation of methodology using the increasingly …
[HTML][HTML] Robust modified jackknife ridge estimator for the Poisson regression model with multicollinearity and outliers
The parameters in the Poisson regression model are usually estimated using the maximum
likelihood estimator (MLE). MLE suffers a breakdown when there is either multicollinearity or …
likelihood estimator (MLE). MLE suffers a breakdown when there is either multicollinearity or …
Data-driven robust M-LS-SVR-based NARX modeling for estimation and control of molten iron quality indices in blast furnace ironmaking
Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on
a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the …
a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the …
Data-driven robust RVFLNs modeling of a blast furnace iron-making process using Cauchy distribution weighted M-estimation
Optimal operation of a practical blast furnace (BF) iron-making process depends largely on a
good measurement of molten iron quality (MIQ) indices. However, measuring the MIQ online …
good measurement of molten iron quality (MIQ) indices. However, measuring the MIQ online …
Robust biased estimators for Poisson regression model: simulation and applications
The method of maximum likelihood flops when there is linear dependency (multicollinearity)
and outlier in the generalized linear models. In this study, we combined the ridge estimator …
and outlier in the generalized linear models. In this study, we combined the ridge estimator …
A case study in credit fraud detection with SMOTE and XGboost
C Meng, L Zhou, B Liu - Journal of Physics: Conference Series, 2020 - iopscience.iop.org
Credit fraud observations are minority in the sample set, variables tend to be seriously
unbalanced, and the prediction results tend to be biased towards more observed classes …
unbalanced, and the prediction results tend to be biased towards more observed classes …
Robust online sequential RVFLNs for data modeling of dynamic time-varying systems with application of an ironmaking blast furnace
By dealing with robust modeling and online learning together in a unified random vector
functional-link networks (RVFLNs) framework, this paper presents a novel robust online …
functional-link networks (RVFLNs) framework, this paper presents a novel robust online …
robROSE: A robust approach for dealing with imbalanced data in fraud detection
B Baesens, S Höppner, I Ortner, T Verdonck - Statistical Methods & …, 2021 - Springer
A major challenge when trying to detect fraud is that the fraudulent activities form a minority
class which make up a very small proportion of the data set. In most data sets, fraud occurs …
class which make up a very small proportion of the data set. In most data sets, fraud occurs …
Fault Detection in Distribution Network with the Cauchy-M Estimate—RVFLN Method
C Haydaroğlu, B Gümüş - Energies, 2022 - mdpi.com
Fault detection is an important issue in today's distribution networks, the structure of which is
becoming more complex. In this article, a data-based Cauchy distribution weighting M …
becoming more complex. In this article, a data-based Cauchy distribution weighting M …
Robust statistical inference in generalized linear models based on minimum Renyi's pseudodistance estimators
Minimum Renyi's pseudodistance estimators (MRPEs) enjoy good robustness properties
without a significant loss of efficiency in general statistical models, and, in particular, for …
without a significant loss of efficiency in general statistical models, and, in particular, for …