Estimating Clear Sky Solar Radiation Using Linke and Angstrom Turbidity Coefficients in Romania

The renewable systems design software and building energy simulation software for energy efficient buildings, use as a main input the solar radiation. The implementation of such systems in the urban environment requires accurate meteorological data for the interest area. The existence of a small number of weather stations that to offer data with regard to solar radiation as well as the limited access to these, makes necessary the conceiving of some more accurate estimation mathematical models for all climatological parameters. The present paper proposes a study of the Linke and AngstrOm turbidity coefficients, for Brasov urban area, with the purpose of a more accurate solar radiation simulation. Models performance is analyzed using the root mean square error （RMSE）, the mean bias error （MBE）, the mean percentage error （MPE） and the t-statistic.

Self-normalization:Taming a wild population in a heavy-tailed world

The past two decades have witnessed the active development of a rich probability theory of Studentized statistics or self-normalized processes, typified by Student’s t-statistic as introduced by W. S. Gosset more than a century ago, and their applications to statistical problems in high dimensions, including feature selection and ranking, large-scale multiple testing and sparse, high dimensional signal detection. Many of these applications rely on the robustness property of Studentization/self-normalization against heavy-tailed sampling distributions. This paper gives an overview of the salient progress of self-normalized limit theory, from Student’s t-statistic to more general Studentized nonlinear statistics. Prototypical examples include Studentized one- and two-sample U-statistics. Furthermore, we go beyond independence and glimpse some very recent advances in self-normalized moderate deviations under dependence.

Application of Multiple Linear Regression and Manova to Evaluate Health Impacts Due to Changing River Water Quality

Rivers are important systems which provide water to fulfill human needs. However, excessive human uses over the years have led to deterioration in quality of river causing, causing health problems from contaminated water. This study focuses on the application of statistical techniques, Multiple Linear Regression model and MANOVA to assess health impacts due to pollution in Cauvery river stretch in Srirangapatna. In this study, using Multiple Linear Regression, it is found that health impact level is 60.8% dependent on water quality parameters of BOD, COD, TDS, TC and FC. The t-statistics and their associated 2-tailed p-values indicate that COD and TDS produces health impacts compared to BOD, TC and FC, when their effects are put together across all the six sampling stations in Srirangapatna. Further Pearson correlation Matrix shows highly significant positive correlation amongst parameters across all stations indicating possibility of common sources of origin that might be anthropogenic. Also graphs are plotted for individual parameters across all stations and it reveals that COD and TDS values are significant across all sampling stations, though their values are higher in impact stations, causing health impacts.

Estimation of Diffuse Solar Radiation in Area between 5°N and 10°N of Cameroon

The successful design and effective utilization of solar energy systems and devices for application in various facets of human endeavors, such as power and water supply for industrial, agricultural and domestic uses, largely depend on the availability of information on solar radiation characteristic of the location in which the system and devices should be situated. This study proposed a multivariate model of monthly mean daily diffuse solar radiation on horizontal surfaces for some cities between 5°N and 10°N of Cameroon (Ngaoundéré, Garoua-Boulai, Bafoussam, Nkambé). The estimation was based on a correlation between clearance index and diffusion to global solar radiation ratio and was computed using monthly mean daily data set for global solar radiation on horizontal surfaces. The predictive efficiency of the proposed model was compared with the observed values and those believed to be universally applicable. The results suggested that the existing methods could be replaced by the developed model for a diffuse solar radiation data generation scheme.

Random Weighting T-Statistics in Linear Regression Models

In this paper, we have constructed a random weighting statistic to approximate the distribution of studentized least square estimator in a linear regression model with ideal accuracy o(n-1/2). Thus, we have provided a more practical distribution approximating method.

Self-normalized moderate deviations for independent random variables

Let X1,X2,... be a sequence of independent random variables （r.v.s） belonging to the domain of attraction of a normal or stable law. In this paper, we study moderate deviations for the self-normalized sum n X ∑^n_i=1Xi/Vm,p ,where Vn,p （∑^n_i=1｜Xi｜p）^1/p （P 〉 1）.Applications to the self-normalized law of the iteratedlogarithm, Studentized increments of partial sums, t-statistic, and weighted sum of independent and identically distributed （i.i.d.） r.v.s are considered.