Elliptical Models Subject to Hidden Truncation or Selective Sampling

2004 ◽  
Author(s):  
Barry Arnold ◽  
Robert Beaver
Keyword(s):  
Selective Sampling ◽  
Hidden Truncation ◽  
Elliptical Models

Selective Sampling and Orientation of Non-Homogeneous Tissues

1968 ◽  
Vol 26 ◽  
pp. 98-99
Author(s):  
C. C. Clawson ◽  
L. W. Anderson ◽  
R. A. Good
Keyword(s):  
Electron Microscopy ◽  
Electron Microscope ◽  
Light Microscopy ◽  
Random Sampling ◽  
New Method ◽  
Microscope Examination ◽  
Small Areas ◽  
Selective Sampling ◽  
Large Numbers ◽  
Specific Orientation

Investigations which require electron microscope examination of a few specific areas of non-homogeneous tissues make random sampling of small blocks an inefficient and unrewarding procedure. Therefore, several investigators have devised methods which allow obtaining sample blocks for electron microscopy from region of tissue previously identified by light microscopy of present here techniques which make possible: 1) sampling tissue for electron microscopy from selected areas previously identified by light microscopy of relatively large pieces of tissue; 2) dehydration and embedding large numbers of individually identified blocks while keeping each one separate; 3) a new method of maintaining specific orientation of blocks during embedding; 4) special light microscopic staining or fluorescent procedures and electron microscopy on immediately adjacent small areas of tissue.

scholarly journals Probing the location of the unpaired electron in spin–orbit changing collisions of NO with Ar

2020 ◽  
Vol 22 (39) ◽  
pp. 22289-22301
Author(s):  
Cornelia G. Heid ◽  
Imogen P. Bentham ◽  
Victoria Walpole ◽  
Razvan Gheorghe ◽  
Pablo G. Jambrina ◽  
...  
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Unpaired Electron ◽  
Energy Surface ◽  
Spin Orbit ◽  
Selective Sampling

The ability to orient NO molecules prior to collision with Ar atoms allows selective sampling of different potential energy surface regions and elucidation of the associated collision pathways.

A novel multi-stage ensemble model with multiple K-means-based selective undersampling: An application in credit scoring

2021 ◽  
Vol 40 (5) ◽  
pp. 9471-9484
Author(s):  
Yilun Jin ◽  
Yanan Liu ◽  
Wenyu Zhang ◽  
Shuai Zhang ◽  
Yu Lou
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Credit Scoring ◽  
Imbalanced Data ◽  
Ensemble Model ◽  
Selective Sampling ◽  
Machine Learning Methods ◽  
Multi Stage ◽  
Proposed Model ◽  
New Feature

With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a new feature-enhanced stacking method is proposed to construct an effective ensemble model by composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models.

scholarly journals Multivariate Tail Moments for Log-Elliptical Dependence Structures as Measures of Risks

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 559
Author(s):  
Zinoviy Landsman ◽  
Tomer Shushi
Keyword(s):  
Risk Measures ◽  
Short Paper ◽  
Dependence Structure ◽  
Elliptical Distributions ◽  
Tail Conditional Expectation ◽  
Special Cases ◽  
Optimal Portfolio Selection ◽  
Multivariate Risk Measures ◽  
Elliptical Models ◽  
Risk Managers

The class of log-elliptical distributions is well used and studied in risk measurement and actuarial science. The reason is that risks are often skewed and positive when they describe pure risks, i.e., risks in which there is no possibility of profit. In practice, risk managers confront a system of mutually dependent risks, not only one risk. Thus, it is important to measure risks while capturing their dependence structure. In this short paper, we compute the multivariate risk measures, multivariate tail conditional expectation, and multivariate tail covariance measure for the family of log-elliptical distributions, which captures the dependence structure of the risks while focusing on the tail of their distributions, i.e., on extreme loss events. We then study our result and examine special cases, as well as the optimal portfolio selection using such measures. Finally, we show how the given multivariate tail moments can also be computed for log-skew elliptical models based on similar approaches given for the log-elliptical case.

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