Statistical analysis of factor models of high dimension

This paper considers the maximum likelihood estimation of factor models of high dimension, where the number of variables (N) is comparable with or even greater than the number of observations (T). An inferential theory is developed. We establish not only consistency but also the rate of convergence and the limiting distributions. Five different sets of identification conditions are considered. We show that the distributions of the MLE estimators depend on the identification restrictions. Unlike the principal components approach, the maximum likelihood estimator explicitly allows heteroskedasticities, which are jointly estimated with other parameters. Efficiency of MLE relative to the principal components method is also considered.Comment: Published in at http://dx.doi.org/10.1214/11-AOS966 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Test of Convergence in Agricultural Factor Productivity: A Semiparametric Approach

We tested for club convergence in U.S. agricultural total factory productivity using a sigma convergence test. We used the same club of states as used by McCunn and Huffman as well as different states within 10 clubs identified by the cluster analysis. Results showed convergence was evident only in a few club groups. Clusters group identified using a statistical method identified only converging clubs. Variables affecting total factor productivity among states were identified using parametric, semiparametric and nonparametric methods. Semiparametric and nonparametric methods gave a better fit than a parametric method as indicated by the specification test. Our results indicated that health care expenditure, public research and extension investment, and private expenditure are important variables impacting total factor productivity differences across states.Clubs, sigma convergence, cluster analysis, semiparametric and nonparametric methods, Productivity Analysis, Research Methods/ Statistical Methods,

Efficient analysis in planet transit surveys

With the growing number of projects dedicated to the search for extrasolar planets via transits, there is a need to develop fast, automatic, robust methods with a statistical background in order to efficiently do the analysis. We propose a modified analysis of variance (AoV) test particularly suitable for the detection of planetary transits in stellar light curves. We show how savings of labor by a factor of over 10 could be achieved by the careful organization of computations. Basing on solid analytical statistical formulation, we discuss performance of our and other methods for different signal-to-noise and number of observations.Comment: 7 pages, to be published in MNRAS, downloadable software from http://www.camk.edu.pl/~alex/#softwar

Professional self-efficacy scale for information and computer technology teachers: validity and reliability study

This study aims at developing a valid and reliable scale to measure information and communication technology (ICT) teachers' self-efficacy related to the Turkish national framework of ICT competencies. For statistical procedures, data were respectively analyzed with exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Furthermore, test-retest procedure was carried out to confirm the time invariance of the scale. EFA results revealed that the scale's seven-factor structure accounts for 65.90 percent of total variance. CFA results produced an acceptable statistical support for model-data fit between the observed item scores and the seven-dimension scale structure (X-2/df = 1.98, RMSEA = .073, CFI = .86). The standardized regression weights between the latent and observed variables ranged from .57 to .89 and Cronbach's alpha coefficient of the scale sub-dimensions ranged from .80 to .88. Besides, the item-scale correlations varied between values of .53 and .79. As a result, the developed scale is a likert questionnaire and composed of 33 five-point items with seven sub-dimensions

The application of multivariate statistical methods for understanding food consumer behaviour

Understanding consumer behaviour is a necessary precondition for a targeted communication strategy. The behaviour is a complex phenomenon and research needs to undertake a rigorously apply sophisticated methods. This article entails the combined utilisation of categorical principal component analysis and cluster analysis to determine the major, relatively homogenous consumer groups and this is coupled with confirmatory factor analysis and structural model building to understand consumer behaviour, based on Fishbein and Ajzent’s theoretic model.Categorical principal component analysis, cluster analysis, confirmatory factor analysis, consumers’ segmentation, structural model building, Research Methods/ Statistical Methods,

SHARE: Statistical Hadronization with Resonances

SHARE is a collection of programs designed for the statistical analysis of particle production in relativistic heavy-ion collisions. With the physical input of intensive statistical parameters, it generates the ratios of particle abundances. The program includes cascade decays of all confirmed resonances from the Particle Data Tables. The complete treatment of these resonances has been known to be a crucial factor behind the success of the statistical approach. An optional feature implemented is a Breit--Wigner type distribution for strong resonances. An interface for fitting the parameters of the model to the experimental data is provided.Comment: Extended version submitted to Computer Physics Communications. Program available on the web at http://www.physics.arizona.edu/~torrieri/SHARE/share.htm

TFBSTools: an R/bioconductor package for transcription factor binding site analysis.

Summary: The ability to efficiently investigate transcription factor binding sites (TFBSs) genome-wide is central to computational studies of gene regulation. TFBSTools is an R/Bioconductor package for the analysis and manipulation of TFBSs and their associated transcription factor profile matrices. TFBStools provides a toolkit for handling TFBS profile matrices, scanning sequences and alignments including whole genomes, and querying the JASPAR database. The functionality of the package can be easily extended to include advanced statistical analysis, data visualization and data integration. Availability and implementation: The package is implemented in R and available under GPL-2 license from the Bioconductor website (http://bioconductor.org/packages/TFBSTools/). Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online