How reliable are the weather forecasts? Based on data collected from onehundred and thirteen cities in the United States over 38 months on threevariables, maximum temperature, minimum temperature and precipitation,accuracy of the weather forecasts was examined. The same day forecast hasbeen extremely accurate, especially for the maximum temperature, whilethe forecast errors and variability increase as forecasts go further out indays. Some cities have larger or smaller forecast errors than the others.For long-term weather forecasts, the maximum and minimum temperatureforecast errors has decreasing correlations overtime, respectively; However,the correlation between maximum and minimum temperature forecast errorsis positive and increasing overtime. The 7-days forecast errors of precipitationare pretty accurate.

The paper deals with the System comprising of three components in which are in parallel configuration and in series with unit .The system fails if either or both units fails. A single server takes some time to arrive the system to carry out repair activities.The repair of the system is based on first come first serve (fcfs). The failure time distribution and time to repair of all the units is taken exponential of the form. The arrival time of the server is taken as general.

Independent Component Analysis (ICA) is a comparatively new statisticaland computational technique to find hidden components from multivariate statistical data. The technique is also employed as a tool for dimension reduction for efficient data analysis. Reduction in dimensions can be done byassigning ranks to the independent components in some appropriate way and then restricting the data analysis to certain high ranking components only.The problem of determining the number of high ranked ICs that should be retained is the main objective of this paper. A method based upon adjusted coefficient of determination is proposed for the purpose. The performance of the proposed method is demonstrated through experimental evaluation on real-world financial time series data.

The ridge estimator has been consistently demonstrated to be an attractive shrinkage method to reduce the effects of multicollinearity. The Poisson regression negative binomial regression models are well-known model in application when the response variable is count data. However, it is known that multicollinearity negatively affects the variance of maximum likelihood estimator of the count regression coefficients. To address this problem, a count data ridge estimator has been proposed by numerous researchers. In this paper, an almost unbiased regression estimator is proposed and derived. Our Monte Carlo simulation results suggest that the proposed estimator can bring significant improvement relative to other existing estimators. In addition, the real application results demonstrate that the proposed estimator outperforms both negative binomial ridge regression and maximum likelihood estimators in terms of predictive performance.

In this paper, we introduce a new continuous distribution of two parameterscalled as a generalized Quasi Lindley distribution (GQLD). The GQLD is asum of two independent Quasi Lindley distributed random variables. Compre-hensive statistical properties of the GQLD are provided in closed forms includesmoments, reliability analysis, stochastic ordering, stress-strength reliability, andthe distribution of order statistics. The parameters of the new distribution areestimated by the maximum likelihood, maximum product of spacings, ordinaryleast squares, weighted least squares, Cramer-von-Mises, and Anderson-Darlingmethods are considered. A simulation study is conducted to investigate theeciency of the proposed estimators and applications to real data sets are pro-vided.

It is well-known that in the presence of multicollinearity, the ridge estimator is an alternative to the ordinary least square (OLS) estimator. Generalized ridge estimator (GRE) is an generalization of the ridge estimator. However, the efficiency of GRE depends on appropriately choosing the shrinkage parameter matrix which is involved in the GRE. In this paper, a particle swarm optimization method, which is a metaheuristic continuous algorithm, is proposed to estimate the shrinkage parameter matrix. The simulation study and real application results show the superior performance of the proposed method in terms of prediction error.

The danger of a global pandemic, such as the new Coronavirus (Covid-19),is obvious. This study aims to investigate the behavior and relationship of thenumber of daily new conrmed deaths per million and the stringency indexof twenty-seven European Union (EU) countries by utilizing functional clusteranalysis and functional canonical correlation analysis. Functional clusteranalysis was used to observe how countries cluster together according to dailydeaths during the time interval between March and July 2020. Functionalcanonical correlation analysis was also utilized to measure the correlationbetween the frequency index and daily deaths, and also to determine therelative positions of countries concerning their respective variability structure.The data is obtained from OWID. Here, it is seen that Italy, Spain,Belgium, and France are particularly aected by the pandemic during thetime interval within the EU countries, and the course of daily deaths is in adierent position compared to other EU countries. At the same time, a veryhigh relationship emerged between the stringency index and daily deaths asexpected.

The issues of symmetry (or asymmetry) arises naturally for the analysis of square contingency tables. Many existing asymmetry models do not have the constraints on the main diagonal cells. Thus, the observations on the main diagonal cells do not contribute to the likelihood ratio chi-squared test statistics. Herein we propose a model that indicates the asymmetry for the log odds.It can utilize the information in the main diagonal cells. Also, the symmetry model is separated into some models including the proposed model.

Preference data are a particular type of ranking data that arise when n individuals express their preferences over a finite set of items. Within this framework, the main issue concerns the aggregation of the preferences to identify a compromise or a “consensus”, defined as the closest ranking (i.e. with the minimum distance or maximum correlation) to the whole set of preferences. Many approaches have been proposed, but they are not sensitive to the importance of items: i.e. changing the rank of a highly-relevant element should result in a higher penalty than changing the rank of a negligible one. The goal of this paper is to investigate the consensus between rankings taking into account the importance of items (element weights). For this purpose, we present: i) an element weighted rank correlation coefficient tau_ew as an extension of the Emond and Mason’s tau, and ii) an element weighted rank distance d_ew as an extension of the Kemeny distance d. The one-to-one correspondence between the weighted distance and the rank correlation coefficient is analytically proved. Moreover, a procedure to obtain the consensus ranking among n individuals is described and its performance is studied both by simulation and by the application to real datasets.

From the empirical point of view, measures that promote work flexibility increase income inequalities and unemployment rates in the long-term, as well as promoting employment precariousness and the informality of the labor sector. The objective of the present work is to investigate the effect on wage inequality of eliminating work flexibility, which was undertaken in Ecuador in 2008. A two-way effect econometric model was applied with panel data. Data from the 21 provinces of Ecuador covering the period of 2007-2018 were obtained from the National Employment, Unemployment and Under-Employment Survey (ENEMDU) of the National Statistical and Census Institute (INEC). The results suggest that the elimination of work flexibility had a significant and negative effect on inequality; the policy was effective in reducing inequality. This result is significant for all the years subsequent to the introduction of these measures, although with variations according to regional and economic characteristics. Policies aimed at reducing inequality should focus on improving workers' bargaining power and on generating an environment that favors increasing levels of formality.