Current Issue : January - March Volume : 2020 Issue Number : 1 Articles : 5 Articles
Using the fact that a multivariate random sample of n observations also generates\nn nearest neighbour distance (NND) univariate observations and\nfrom these NND observations, a set of n auxiliary observations can be obtained\nand with these auxiliary observations when combined with the original\nmultivariate observations of the random sample, a class of pseudodistance\nh D is allowed to be used and inference methods can be developed using this\nclass of pseudodistances. The h D estimators obtained from this class can\nachieve high efficiencies and have robustness properties. Model testing also\ncan be handled in a unified way by means of goodness-of-fit tests statistics\nderived from this class which have an asymptotic normal distribution. These\nproperties make the developed inference methods relatively simple to implement\nand appear to be suitable for analyzing multivariate data which are often\nencountered in applications....
Digital cameras span a large range in price and performance. Consumers often\nfocus mainly on the resolution in pixels when shopping for a camera. Of\nequal importance is the quality of the optics and the exposure response. Digital\ncameras generally have a linear exposure response, but the amount of\nnoise and the dynamic range vary. It is difficult to obtain quantitative information\non these parameters to make an informed assessment. This work explores\nand demonstrates first-principles methods to measure the exposure\nresponse to make meaningful comparisons between different camera models.\nIt also shows how to make the most of a particular camera by measuring its\nnoise level and dynamic range, to understand the limits of its useable ISO\namplification. The methods only require a computer and free software to\ndownload images and extract their RGB pixel values. The analysis, based on\nthe RGB values, uses standard spreadsheet software. The procedures are\ntherefore accessible to anyone with a digital camera and computer, and will\nhelp to reduce speculation in comparing cameras, and help consumers make\nan informed decision....
Cloud computing is a computing hypothesis, where a huge group of systems is linked together in private, public, or hybrid\nnetwork, to offer dynamically amendable infrastructure for data storage, file storage, and application. With this emerging\ntechnology, application hosting, delivery, content storage, and reduced computation cost are achieved, and it acts as an essential\nmodule for the backbone of the Internet of Things (IoT). The efficiency of cloud service providers (CSP) could be improved by\nconsidering significant factors such as availability, reliability, usability, security, responsiveness, and elasticity. Assessment of these\nfactors leads to efficiency in designing a scheduler for CSP. These metrics also improved the quality of service (QoS) in the cloud.\nMany existing models and approaches evaluate these metrics. But these existing approaches do not offer efficient outcome. In this\npaper, a prominent performance model named the â??spectral expansion method (SPM)â? evaluates cloud reliability. The spectral\nexpansion method (SPM) is a huge technique useful in reliability and performance modelling of the computing system. This\napproach solves the Markov model of cloud service providers (CSP) to predict the reliability. The SPM is better compared to\nmatrix-geometric methods....
Clays are among the most essential industrial minerals due to their unique\nphysicochemical properties and versatile usage. This paper used Statistical\nPackage for Social Sciences (SPSS) software to characterize five clay deposits\nfor their physical and chemical compositions. The package, was employed to\ncarry out the Analysis of Variance (ANOVA) by Post-Hoctambane multiple\ncomparisons and Kristal Wallis at 5% confidence level for the f- and t-tests\nrespectively. The analysis of variance of the chemical components of the samples\nby post-hoc (f8, 36 = 52.40, p < 0.05) showed that significant difference\nexist between the average concentration means. While the Kristal Wallis one\nsample t-test (T8, 37.38 and p < 0.05) showed a great degree of significant difference\nin the p-values of the means of SiO2 and Al2O3. Pearson bivariate correlation\nstatistical tool was also used to establish if significant positive interrelationships\nexist between the parameters in each site of the clay samples at\n(p < 0.01 and p < 0.05). The result of the correlation indicates a very significant,\nstrong and positive coefficient p-values above 0.900 between the chemical\nand physicalproperties. Pearson bivariate correlation coefficient between\nthe chemical and physical parameters of the clay samples indicates very significant,\nstrong and positive correlations with p-values above 0.900 at (p <\n0.01 and <0.05). The overall physicochemical results indicate that most of the\nclay samples will meet the requirements for some industrial applications with\nminimal processing....
To understand any statistical tool requires not only an understanding of the\nrelevant computational procedures but also an awareness of the assumptions\nupon which the procedures are based, and the effects of violations of these\nassumptions. In our earlier articles (Laverty, Miket, & Kelly [1]) and (Laverty\n& Kelly, [2] [3]) we used Microsoft Excel to simulate both a Hidden Markov\nmodel and heteroskedastic models showing different realizations of these\nmodels and the performance of the techniques for identifying the underlying\nhidden states using simulated data. The advantage of using Excel is that the\nsimulations are regenerated when the spreadsheet is recalculated allowing the\nuser to observe the performance of the statistical technique under different\nrealizations of the data. In this article we will show how to use Excel to generate\ndata from a one-way ANOVA (Analysis of Variance) model and how the\nstatistical methods behave both when the fundamental assumptions of the\nmodel hold and when these assumptions are violated. The purpose of this article\nis to provide tools for individuals to gain an intuitive understanding of\nthese violations using this readily available program....
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