A note on statistical analysis of factor models of high dimension

2021 ◽  
Author(s):  
Zhigen Gao ◽  
Jianhua Guo ◽  
Yanyuan Ma
Keyword(s):  
Statistical Analysis ◽  
High Dimension ◽  
Factor Models

scholarly journals Statistical analysis of factor models of high dimension

2012 ◽  
Vol 40 (1) ◽  
pp. 436-465 ◽  
Author(s):  
Jushan Bai ◽  
Kunpeng Li
Keyword(s):  
Statistical Analysis ◽  
High Dimension ◽  
Factor Models

Statistical Analysis of the Pre- and Post-Surgery in the Healthcare Sector Using High Dimension Segmentation

2020 ◽  
pp. 159-174
Author(s):  
Soobia Saeed ◽  
Afnizanfaizal Abdullah ◽  
N. Z. Jhanjhi ◽  
Mehmood Naqvi ◽  
Mamoona Humayun
Keyword(s):  
Statistical Analysis ◽  
High Dimension ◽  
Healthcare Sector ◽  
Post Surgery

scholarly journals Statistical analysis of sparse approximate factor models

2020 ◽  
Vol 14 (2) ◽  
pp. 3315-3365
Author(s):  
Benjamin Poignard ◽  
Yoshikazu Terada
Keyword(s):  
Statistical Analysis ◽  
Factor Models

Statistical analysis of classification

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Classification Scheme ◽  
Analytical Procedure ◽  
Narrow Band ◽  
Scientific Research ◽  
Maximum Amount ◽  
Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

Quantitative parallel EELS spectrum imaging developed as a new tool in AEM

1992 ◽  
Vol 50 (2) ◽  
pp. 1202-1203
Author(s):  
Gianluigi Botton ◽  
Gilles L'espérance
Keyword(s):  
Statistical Analysis ◽  
Spatial Resolution ◽  
Personal Computer ◽  
Systematic Study ◽  
Present Author ◽  
Chemical Elements ◽  
Detection Limits ◽  
Research Groups ◽  
Quantitative Performance ◽  
Spectrum Imaging

As interest for parallel EELS spectrum imaging grows in laboratories equipped with commercial spectrometers, different approaches were used in recent years by a few research groups in the development of the technique of spectrum imaging as reported in the literature. Either by controlling, with a personal computer both the microsope and the spectrometer or using more powerful workstations interfaced to conventional multichannel analysers with commercially available programs to control the microscope and the spectrometer, spectrum images can now be obtained. Work on the limits of the technique, in terms of the quantitative performance was reported, however, by the present author where a systematic study of artifacts detection limits, statistical errors as a function of desired spatial resolution and range of chemical elements to be studied in a map was carried out The aim of the present paper is to show an application of quantitative parallel EELS spectrum imaging where statistical analysis is performed at each pixel and interpretation is carried out using criteria established from the statistical analysis and variations in composition are analyzed with the help of information retreived from t/γ maps so that artifacts are avoided.

Statistical analysis related to fetal heart rate patterns

1979 ◽  
Vol 135 (1) ◽  
pp. 168
Author(s):  
H. William Perlis ◽  
John F. Huddleston
Keyword(s):  
Heart Rate ◽  
Statistical Analysis ◽  
Fetal Heart Rate ◽  
Fetal Heart
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