Statistical Analysis of Simulated Radar Target's Movement for the Needs of Multiple Model Tracking Filter

2017 ◽  
pp. 167-172
Keyword(s):  
Statistical Analysis ◽  
Multiple Model ◽  
Tracking Filter

MTHFR, TYMS and SLCO1B1 polymorphisms and adverse liver effects of methotrexate in rheumatoid arthritis

2020 ◽  
Vol 21 (5) ◽  
pp. 337-346
Author(s):  
Johanna Karlsson Sundbaum ◽  
Eva Baecklund ◽  
Niclas Eriksson ◽  
Pär Hallberg ◽  
Hugo Kohnke ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Logistic Regression ◽  
Statistical Analysis ◽  
Multiple Testing ◽  
Laboratory Data ◽  
Multiple Model ◽  
Multiple Logistic Regression ◽  
Clinical Factors ◽  
Mthfr A1298c ◽  
C Statistic

Aims: To investigate whether variants of MTHFR, TYMS and SLCO1B1 are associated with ALT elevation in rheumatoid arthritis patients starting methotrexate (MTX). Patients & methods: Clinical and laboratory data were collected from the start of MTX treatment. Genotyping of MTHFR, TYMS and SLCO1B1 was performed. Univariate and multiple logistic regression were used for statistical analysis. Results: 34 out of 369 patients experienced ALT >1.5 × ULN less than 6 months from start. MTHFR A1298C (rs1801131) was nominally associated with an ALT >1.5 × ULN within 6 months after the start of MTX (OR = 1.7 [95% CI: 1.04–2.9]; p = 0.03), but did not pass correction for multiple testing. A multiple model containing MTHFR 1298C and clinical factors predicted the outcome (C-statistic 0.735). TYMS and SLCO1B1 were not associated with the outcome. Conclusions: A model containing MTHFR 1298C and clinical factors might predict risk of early ALT elevation.

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.

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