Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting

In this work, we propose the Seasonal Dynamic Factor Analysis (SeaDFA), an extension of Nonstationary Dynamic Factor Analysis, through which one can deal with dimensionality reduction in vectors of time series in such a way that both common and specific components are extracted. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal ones, by means of the common factors following a multiplicative seasonal VARIMA(p, d, q) × (P, D, Q)s model. Additionally, a bootstrap procedure that does not need a backward representation of the model is proposed to be able to make inference for all the parameters in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing enhanced coverage of forecasting intervals. A challenging application is provided. The new proposed model and a bootstrap scheme are applied to an innovative subject in electricity markets: the computation of long-term point forecasts and prediction intervals of electricity prices. Several appendices with technical details, an illustrative example, and an additional table are available online as Supplementary Materials.

Factor analysis for metal grade exploration at Pallancata Vein in Peru

Se investiga la distribución espacial de contenidos metálicos analizados sobre testigos de sondeos obtenidos en las campañas de exploración de la Veta Pallancata. Se aplica el análisis factorial a dicha distribución y a los cocientes de los valores metálicos, discriminando los que están correlacionados con la mineralización argentífera y que sirven como guías de exploración para hallar zonas de potenciales reservas por sus gradientes de variación.Abstract:The metal distribution in a vein may show the paths of hydrothermal fluid flow at the time of mineralization. Such information may assist for in-fill drilling. The Pallancata Vein has been intersected by 52 drill holes, whose cores were sampled and analysed, and the results plotted to examine the mineralisation trends. The spatial distribution of the ore is observed from the logAg/logPb ratio distribution. Au is in this case closely related to Ag (electrum and uytenbogaardtite, Ag3AuS2 ). The Au grade shows the same spatial distribution as the Ag grade. The logAg/logPb ratio distribution also suggests possible ore to be expected at deeper locations. Shallow supergene Ag enrichment was also observed.

A methodology to estimate uncertainty for emission projections through sensitivity analysis

Air pollution abatement policies must be based on quantitative information on current and future emissions of pollutants. As emission projections uncertainties are inevitable and traditional statistical treatments of uncertainty are highly time/resources consuming, a simplified methodology for nonstatistical uncertainty estimation based on sensitivity analysis is presented in this work. The methodology was applied to the “with measures” scenario for Spain, concretely over the 12 highest emitting sectors regarding greenhouse gas and air pollutants emissions. Examples of methodology application for two important sectors (power plants, and agriculture and livestock) are shown and explained in depth. Uncertainty bands were obtained up to 2020 by modifying the driving factors of the 12 selected sectors and the methodology was tested against a recomputed emission trend in a low economic-growth perspective and official figures for 2010, showing a very good performance. Implications: A solid understanding and quantification of uncertainties related to atmospheric emission inventories and projections provide useful information for policy negotiations. However, as many of those uncertainties are irreducible, there is an interest on how they could be managed in order to derive robust policy conclusions. Taking this into account, a method developed to use sensitivity analysis as a source of information to derive nonstatistical uncertainty bands for emission projections is presented and applied to Spain. This method simplifies uncertainty assessment and allows other countries to take advantage of their sensitivity analyses.

Statistical analysis of the goodness of classical factor analysis regression (CFAR) /

Cover title.

An investigation of the goodness of fit of the maximum likelihood estimation procedure in factor analysis /

Thesis--Illinois.

The structure of labor market segmentation of occupations : some evidence from factor analysis /

Includes bibliographies (p. 31).

An application of factor analysis to water quality data /

Cover title.

Gray's model of personality and aggregate level factor analysis

Previous research shows that correlations tend to increase in magnitude when individuals are aggregated across groups. This suggests that uncorrelated constellations of personality variables (such as the primary scales of Extraversion and Neuroticism) may display much higher correlations in aggregate factor analysis. We hypothesize and report that individual level factor analysis can be explained in terms of Giant Three (or Big Five) descriptions of personality, whereas aggregate level factor analysis can be explained in terms of Gray's physiological based model. Although alternative interpretations exist, aggregate level factor analysis may correctly identify the basis of an individual's personality as a result of better reliability of measures due to aggregation. We discuss the implications of this form of analysis in terms of construct validity, personality theory, and its applicability in general. Copyright (C) 2003 John Wiley Sons, Ltd.

Drinking refusal self-efficacy questionnaire-revised (DRSEQ-R): a new factor structure with confirmatory factor analysis

The drinking refusal self-efficacy questionnaire (DRSEQ: Young, R.M., Oei, T.P.S., 1996. Drinking expectancy profile: test manual. Behaviour Research and Therapy Centre, University of Queensland, Australia Young, R.M., Oei, T.P.S., Crook, G.M., 1991. Development of a drinking refusal self-efficacy questionnaire. J. Psychopathol. Behav. Assess., 13, 1-15) assesses a person's belief in their ability to resist alcohol. The DRSEQ is a sound psychometric instrument based on exploratory factor analyses, but has not been subjected to confirmatory factor analysis. In total 2773 participants were used to confirm the factor structure of the DRSEQ. Initial analyses revealed that the original structure was not confirmed in the current study. Subsequent analyses resulted in a revised factor structure (DRSEQ-R) being confirmed in community, student and clinical samples. The DRSEQ-R was also found to have good construct and concurrent validity. The factor structure of the DRSEQ-R is more stable than the original structure of the DRSEQ and the revised scale has considerable potential in future alcohol-related research. (c) 2004 Elsevier Ireland Ltd. All rights reserved.

Confirmatory factor analysis and psychometric properties of the Anxiety Sensitivity Index-Revised in clinical and normative populations

The present investigation aimed to critically examine the factor structure and psychometric properties of the Anxiety Sensitivity Index - Revised (ASI-R). Confirmatory factor analysis using a clinical sample of adults (N = 248) revealed that the ASI-R could be improved substantially through the removal of 15 problematic items in order to account for the most robust dimensions of anxiety sensitivity. This modified scale was renamed the 21-item Anxiety Sensitivity Index (21-item ASI) and reanalyzed with a large sample of normative adults (N = 435), revealing configural and metric invariance across groups. Further comparisons with other alternative models, using multi-sample analysis, indicated the 21-item ASI to be the best fitting model for both groups. There was also evidence of internal consistency, test-retest reliability, and construct validity for both samples suggesting that the 21-item ASI is a useful assessment device for investigating the construct of anxiety sensitivity in both clinical and normative populations.

Factor analysis of the self-report Dysexecutive (DEX-S) Questionnaire

The self-rating Dysexecutive Questionnaire (DEX-S) is a recently developed standardized self-report measure of behavioral difficulties associated with executive functioning such as impulsivity, inhibition, control, monitoring, and planning. Few studies have examined its construct validity, particularly for its potential wider use across a variety of clinical and nonclinical populations. This study examines the factor structure of the DEX-S questionnaire using a sample of nonclinical (N = 293) and clinical (N = 49) participants. A series of factor analyses were evaluated to determine the best factor solution for this scale. This was found to be a 4-factor solution with factors best described as inhibition, intention, social regulation, and abstract problem solving. The first 2 factors replicate factors from the 5-factor solutions found in previous studies that examined specific subpopulations. Although further research is needed to evaluate the factor structure within a range of subpopulations, this study supports the view that the DEX has the factor structure sufficient for its use in a wider context than only with neurological or head-injured patients. Overall, a 4-factor solution is recommended as the most stable and parsimonious solution in the wider context.