Parimutuel system of the Japanese race track: Voting and ranking process

We investigated a time series of betting rates in a pari-mutuel betting system of Japanese horse races. Principal component analyses and cluster analyses suggest that the level of expectation for the favorite is the most important factor when classifying races. The dataset of the odds determined before the races used in this study is exceedingly large and detailed compared to previous studies. We also conducted detailed research on whether bets placed on the favorites tend to be concentrated just before the race.

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Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation

Chaos Solitons & Fractals ◽

10.1016/j.chaos.2020.110246 ◽

2020 ◽

Vol 140 ◽

pp. 110246

Author(s):

Vimal Raj ◽

A. Renjini ◽

M.S. Swapna ◽

S. Sreejyothi ◽

S. Sankararaman

Keyword(s):

Time Series ◽

Principal Component ◽

Nonlinear Time Series ◽

Diagnostic Tools ◽

Principal Component Analyses

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Sond cruise 1965: Factor and cluster analyses of the plankton results, a general summary

Journal of the Marine Biological Association of the United Kingdom ◽

10.1017/s002531540005671x ◽

1973 ◽

Vol 53 (1) ◽

pp. 185-231 ◽

Cited By ~ 29

Author(s):

M. V Angel ◽

M. J. R Fasham

Keyword(s):

Marked Improvement ◽

Simple Structure ◽

Factor Score ◽

Principal Component ◽

Factor Analyses ◽

Cluster Analyses ◽

Principal Component Analyses ◽

Total Data ◽

The Individual ◽

Zonation Patterns

1. The data for five planktonic taxa sampled during the SOND cruise (autumn 1965) by R.R.S. ‘Discovery’ in a day and a night series of horizontally towed nets have been analysed by factor and cluster analyses.2. The factor analyses of hauls showed that there were five biologically distinct zones in the water column between the surface and 1000 m. The zones were more distinct in the day series than in the night.3. Factor analyses of the individual taxa gave sensible groupings of species and the factor score plots gave ‘averaged’ depth distributions for each group.4. Cluster analyses of the individual taxa gave results consistent with the factor analyses. Both methods gave groupings which fitted the zonation patterns of the hauls, except for the two zones between 100 and 460 m at night.5. Only cluster analyses could be carried out on the total data matrices. Although satisfactory interpretation was only possible with the aid of the analyses of the individual taxa, the zonation and species groupings were again largely retained.6. The zonation of the planktonic taxa is shown to be very similar to that described for nektonic species sampled on the same cruise.7. The usefulness of these analytical methods is compared with the conclusions of other investigators. It is concluded that the rotation of the matrices to simple structure in the factor analysis gave a marked improvement in the ease of interpretation.8. For this data, principal component analyses gave very similar results to the full factor analyses.

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THE CONVERGENCE OF HABITAT STRUCTURE IN TUNDRA COLLEMBOLAN COMMUNITIES (INSECTA: ORDER COLLEMBOLA)

The Canadian Entomologist ◽

10.4039/ent1151129-9 ◽

1983 ◽

Vol 115 (9) ◽

pp. 1129-1145 ◽

Cited By ~ 3

Author(s):

Masanori J. Toda ◽

Kouzou Tanno

Keyword(s):

Habitat Structure ◽

Principal Component ◽

The Other ◽

Statistical Techniques ◽

Mackenzie Delta ◽

Multivariate Statistical Techniques ◽

Multivariate Statistical ◽

Cluster Analyses ◽

Ecological Changes ◽

Principal Component Analyses

AbstractHabitat structure of two collembolan communities, one at Barrow, Alaska, U.S.A., the other at Tuktoyaktuk in the Mackenzie Delta, Canada, has been analyzed in relation to microtopographies characteristic of tundra regions. Multivariate statistical techniques, cluster analyses (UPGMA), and principal component analyses (PCA) reveal various ecological changes in component species. In spite of such local variations in component species, the two communities show similar patterns of habitat structure that are organized principally along a gradient of environmental moisture.

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