インドネシアにおける地域問所得格差の多変量解析による分析

(1)はじめに　(2) 地域分析の必要性　(3) 地域間格差のアプローチ　(4)クラスタ分析による地域格差分析の可能性　(5)インドネシアへの適用結果　(6)結論にかえて

Multivariate Analysis of the Morphological Traits of Female Duck, Muscovy-duck and Mule-duck

The objective of this study was to evaluate the characteristics of morphometrical measurements inthe female waterfowls. The animals used in this research were 90 ducks , 90 muscovy-ducks and 90mule-ducks in Bulukumba district of Brebes regency, Central Java, Indonesia. Parameters measuredwere maxilla length, neck length, body length, chest circumstance, wing length, chest length, femurlength and tibia length. The data were analyzed using the Statistical Analysis System ver. 9.1. Muscovyduckgenerally had the largest of size, followed by mule-duck and then duck. The most discriminantvariables were showed by chest length and chest circumstance. Muscovy-duck and mule-duck hadclosest genetic distance (3.974870) than both of the distance between duck and mule duck (14.10), andmuscovy-duck and duck (24.73). The smallest errorness level in grouping was showed in duck 1%followed by 2% in mule-duck and 3% in muscovy-duck

Multivariate analysis in vector time series

This paper reviews the applications of classical multivariate techniques for discrimination, clustering and dimension reduction for time series data. It is shown that the discrimination problem can be seen as a model selection problem. Some of the results obtained in the time domain are reviewed. Clustering time series requires the definition of an adequate metric between univariate time series and several possible metrics are analyzed. Dimension reduction has been a very active line of research in the time series literature and the dynamic principal components or canonical analysis of Box and Tiao (1977) and the factor model as developed by Peña and Box (1987) and Peña and Poncela (1998) are analyzed. The relation between the nonstationary factor model and the cointegration literature is also reviewed

Special section on modern multivariate analysis

A critically challenging problem facing statisticians is the identification of a suitable framework which consolidates data of various types, from different sources, and across different time frames or scales (many of which can be missing), and from which appropriate analysis and subsequent inference can proceed.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS529 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Multivariate Analysis Applied to Forestry Agricultural Sciences: The Model-Directed Study

This is a literature review that aimed to find articles that exemplify and describe the use of multivariate analysis in different fields of Forest Agricultural Sciences, considering effective practices using multivariate statistical techniques for the simultaneous processing of data. For data collection were selected for the meta-analysis of 70 technical articles of which 54 were employed in the study directed to the use of multivariate techniques applied in the areas of agricultural sciences. The results showed thatstudies directed to certain areas within the Forest Agricultural Sciences exhibit some regularity in the use of multivariate analysis, and most application analyzes were more usual as the Cluster Analysis (AA) and Principal Component Analysis (PCA). Thus the use of multivariate analysis studies and evaluations of experiments in Agricultural Sciences proved to great value to allow greater clarity and better interpretation of dealing with complex phenomena

Multivariate analysis of morphological variation in enset (Ensete ventricosum (Welw.) Cheesman) reveals regional and clinal variation in germplasm from south and south western Ethiopia

Enset (Ensete ventricosum (Welw.) Cheesman) is cultivated by millions of people across Ethiopia in diverse agro-ecological and cultural settings, selecting for various agronomic traits. However, as for other underutilized crops, our understanding of the diversity and utilization of enset remains limited. This work sought to redress this limitation by estimating morphological diversity among enset accessions collected from major enset growing regions, including across altitudinal gradients. In total, landraces comprising 387 accessions originating from nine regions of Ethiopia were characterized using multivariate analysis of 15 quantitative traits. Cluster analysis grouped accessions in to five distinct classes with maximum number of accessions 338 in cluster (I) and minimum 1 in cluster (V). The clustering of accessions did not show grouping on the basis of region of origin. The first four principal components accounted for ~74% of the total variance. Linear discriminant analysis indicated that around 40.8% (160 accessions) and 45.2% (175 accession) of the studied accessions were correctly classified to their respective regions of origin altitude groups, respectively. The breadth of phenotypic differences in these 15 traits suggests significant degrees of genetic variation. These traits will be exploited to identify potential donors for future enset improvement efforts

Multivariate analysis of trip-chaining behavior

Trip-chaining behavior has generally been associated with various demographic characteristics of households and individuals. This includes households with children having more complex activity patterns, or those who are employed needing to conduct activities on the way to and from work because of time constraints. No studies, as yet, have controlled for other factors that might influence trip chaining behaviour, such as levels of urbanization, public transport availability, use of other transport modes, or various other local environmental factors. This paper explores these issues using both the 1995 Nationwide Personal Transportation Survey and the 2001 National Household Travel Survey. Both surveys have data on trip chaining behavior that allows multivariate analysis of individual level behavior. Various choice models are estimated, including ordered models that account for the number of chains in a trip. Results for both the 1995 and 2001 surveys are presented to examine potential changes in behavior over time.

MULTIVARIATE ANALYSIS IN VECTOR TIME SERIES

This paper reviews the applications of classical multivariate techniques for discrimination, clustering and dimension reduction for time series data. It is shown that the discrimination problem can be seen as a model selection problem. Some of the results obtained in the time domain are reviewed. Clustering time series requires the definition of an adequate metric between univariate time series and several possible metrics are analyzed. Dimension reduction has been a very active line of research in the time series literature and the dynamic principal components or canonical analysis of Box and Tiao (1977) and the factor model as developed by Peña and Box (1987) and Peña and Poncela (1998) are analyzed. The relation between the nonstationary factor model and the cointegration literature is also reviewed.