Probability Distributions
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2.1 Introduction Civil Engineering Systems Deal with Variable Quantities, Which Are Described in Terms of Random Variables and Random Processes
2.1 Introduction Civil Engineering systems deal with variable quantities, which are described in terms of random variables and random processes. Once the system design such as a design of building is identified in terms of providing a safe and economical building, variable resistances for different members such as columns, beams and foundations which can sustain variable loads can be analyzed within the framework of probability theory. The probabilistic description of variable phenomena needs the use of appropriate measures. In this module, various measures of description of variability are presented. 2. HISTOGRAM AND FREQUENCY DIAGRAM Five graphical methods for analyzing variability are: 1. Histograms, 2. Frequency plots, 3. Frequency density plots, 4. Cumulative frequency plots and 5. Scatter plots. 2.1. Histograms A histogram is obtained by dividing the data range into bins, and then counting the number of values in each bin. The unit weight data are divided into 4- kg/m3 wide intervals from to 2082 in Table 1. For example, there are zero values between 1441.66 and 1505 Kg / m3 (Table 1), two values between 1505.74 kg/m3 and 1569.81 kg /m3 etc. A bar-chart plot of the number of occurrences in each interval is called a histogram. The histogram for unit weight is shown on fig.1. Table 1 Total Unit Weight Data from Offshore Boring Total unit Total unit 2 3 Depth weight (x-βx) (x-βx) Depth weight Number (m) (Kg / m3) (Kg / m3)2 (Kg / m3)3 (m) (Kg / m3) 1 0.15 1681.94 115.33 -310.76 52.43 1521.75 2 0.30 1906.20 2050.36 23189.92 2.29 1537.77 -
Stat 345 - Homework 3 Chapter 4 -Continuous Random Variables
STAT 345 - HOMEWORK 3 CHAPTER 4 -CONTINUOUS RANDOM VARIABLES Problem One - Trapezoidal Distribution Consider the following probability density function f(x). 8 > x+1 ; −1 ≤ x < 0 > 5 > <> 1 ; 0 ≤ x < 4 f(x) = 5 > 5−x ; 4 ≤ x ≤ 5 > 5 > :>0; otherwise a) Sketch the PDF. b) Show that the area under the curve is equal to 1 using geometry. c) Show that the area under the curve is equal to 1 by integrating. (You will have to split the integral into pieces). d) Find P (X < 3) using geometry. e) Find P (X < 3) with integration. Problem Two - Probability Density Function Consider the following function f(x), where θ > 0. 8 < c (θ − x); 0 < x < θ f(x) = θ2 :0; otherwise a) Find the value of c so that f(x) is a valid PDF. Sketch the PDF. b) Find the mean and standard deviation of X. p c) If nd , 2 and . θ = 2 P (X > 1) P (X > 3 ) P (X > 2 − 2) c) Compute θ 3θ . P ( 10 < X < 5 ) Problem Three - Cumulative Distribution Functions For each of the following probability density functions, nd the CDF. The Median (M) of a distribution is dened by the property: 1 . Use the CDF to nd the Median of each distribution. F (M) = P (X ≤ M) = 2 a) (Special case of the Beta Distribution) 8 p <1:5 x; 0 < x < 1 f(x) = :0; otherwise b) (Special case of the Pareto Distribution) 8 < 1 ; x > 1 f(x) = x2 :0; otherwise c) Take the derivative of your CDF for part b) and show that you can get back the PDF. -
STAT 345 Spring 2018 Homework 5 - Continuous Random Variables Name
STAT 345 Spring 2018 Homework 5 - Continuous Random Variables Name: Please adhere to the homework rules as given in the Syllabus. 1. Trapezoidal Distribution. Consider the following probability density function. 8 x+1 ; −1 ≤ x < 0 > 5 > 1 < 5 ; 0 ≤ x < 4 f(x) = 5−x > ; 4 ≤ x ≤ 5 > 5 :>0; otherwise a) Sketch the PDF. b) Show that the area under the curve is equal to 1 using geometry. c) Show that the area under the curve is equal to 1 by integrating. (You will have to split the integral into peices). d) Find P (X < 3) using geometry. e) Find P (X < 3) with integration. 2. Let X have the following PDF, for θ > 0. ( c (θ − x); 0 < x < θ f(x) = θ2 0; otherwise a) Find the value of c which makes f(x) a valid PDF. Sketch the PDF. b) Find the mean and standard deviation of X. θ 3θ c) Find P 10 < X < 5 . 3. PDF to CDF. For each of the following PDFs, find and sketch the CDF. The Median (M) of a continuous distribution is defined by the property F (M) = 0:5. Use the CDF to find the Median of each distribution. a) (Special case of Beta Distribution) p f(x) = 1:5 x; 0 < x < 1 b) (Special case of Pareto Distribution) 1 f(x) = ; x > 1 x2 4. CDF to PDF. For each of the following CDFs, find the PDF. a) 8 0; x < 0 <> F (x) = 1 − (1 − x2)2; 0 ≤ x < 1 :>1; x ≥ 1 b) ( 0; x < 0 F (x) = 1 − e−x2 ; x ≥ 0 5. -
Moments of the Product and Ratio of Two Correlated Chi-Square Variables
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Springer - Publisher Connector Stat Papers (2009) 50:581–592 DOI 10.1007/s00362-007-0105-0 REGULAR ARTICLE Moments of the product and ratio of two correlated chi-square variables Anwar H. Joarder Received: 2 June 2006 / Revised: 8 October 2007 / Published online: 20 November 2007 © The Author(s) 2007 Abstract The exact probability density function of a bivariate chi-square distribu- tion with two correlated components is derived. Some moments of the product and ratio of two correlated chi-square random variables have been derived. The ratio of the two correlated chi-square variables is used to compare variability. One such applica- tion is referred to. Another application is pinpointed in connection with the distribution of correlation coefficient based on a bivariate t distribution. Keywords Bivariate chi-square distribution · Moments · Product of correlated chi-square variables · Ratio of correlated chi-square variables Mathematics Subject Classification (2000) 62E15 · 60E05 · 60E10 1 Introduction Fisher (1915) derived the distribution of mean-centered sum of squares and sum of products in order to study the distribution of correlation coefficient from a bivariate nor- mal sample. Let X1, X2,...,X N (N > 2) be two-dimensional independent random vectors where X j = (X1 j , X2 j ) , j = 1, 2,...,N is distributed as a bivariate normal distribution denoted by N2(θ, ) with θ = (θ1,θ2) and a 2 × 2 covariance matrix = (σik), i = 1, 2; k = 1, 2. The sample mean-centered sums of squares and sum of products are given by a = N (X − X¯ )2 = mS2, m = N − 1,(i = 1, 2) ii j=1 ij i i = N ( − ¯ )( − ¯ ) = and a12 j=1 X1 j X1 X2 j X2 mRS1 S2, respectively. -
On the Computation of Multivariate Scenario Sets for the Skew-T and Generalized Hyperbolic Families Arxiv:1402.0686V1 [Math.ST]
On the Computation of Multivariate Scenario Sets for the Skew-t and Generalized Hyperbolic Families Emanuele Giorgi1;2, Alexander J. McNeil3;4 February 5, 2014 Abstract We examine the problem of computing multivariate scenarios sets for skewed distributions. Our interest is motivated by the potential use of such sets in the stress testing of insurance companies and banks whose solvency is dependent on changes in a set of financial risk factors. We define multivariate scenario sets based on the notion of half-space depth (HD) and also introduce the notion of expectile depth (ED) where half-spaces are defined by expectiles rather than quantiles. We then use the HD and ED functions to define convex scenario sets that generalize the concepts of quantile and expectile to higher dimensions. In the case of elliptical distributions these sets coincide with the regions encompassed by the contours of the density function. In the context of multivariate skewed distributions, the equivalence of depth contours and density contours does not hold in general. We consider two parametric families that account for skewness and heavy tails: the generalized hyperbolic and the skew- t distributions. By making use of a canonical form representation, where skewness is completely absorbed by one component, we show that the HD contours of these distributions are near-elliptical and, in the case of the skew-Cauchy distribution, we prove that the HD contours are exactly elliptical. We propose a measure of multivariate skewness as a deviation from angular symmetry and show that it can explain the quality of the elliptical approximation for the HD contours. -
A Formulation for Continuous Mixtures of Multivariate Normal Distributions
A formulation for continuous mixtures of multivariate normal distributions Reinaldo B. Arellano-Valle Adelchi Azzalini Departamento de Estadística Dipartimento di Scienze Statistiche Pontificia Universidad Católica de Chile Università di Padova Chile Italia 31st March 2020 Abstract Several formulations have long existed in the literature in the form of continuous mixtures of normal variables where a mixing variable operates on the mean or on the variance or on both the mean and the variance of a multivariate normal variable, by changing the nature of these basic constituents from constants to random quantities. More recently, other mixture-type constructions have been introduced, where the core random component, on which the mixing operation oper- ates, is not necessarily normal. The main aim of the present work is to show that many existing constructions can be encompassed by a formulation where normal variables are mixed using two univariate random variables. For this formulation, we derive various general properties. Within the proposed framework, it is also simpler to formulate new proposals of parametric families and we provide a few such instances. At the same time, the exposition provides a review of the theme of normal mixtures. Key-words: location-scale mixtures, mixtures of normal distribution. arXiv:2003.13076v1 [math.PR] 29 Mar 2020 1 1 Continuous mixtures of normal distributions In the last few decades, a number of formulations have been put forward, in the context of distribution theory, where a multivariate normal variable represents the basic constituent but with the superpos- ition of another random component, either in the sense that the normal mean value or the variance matrix or both these components are subject to the effect of another random variable of continuous type. -
A Multivariate Student's T-Distribution
Open Journal of Statistics, 2016, 6, 443-450 Published Online June 2016 in SciRes. http://www.scirp.org/journal/ojs http://dx.doi.org/10.4236/ojs.2016.63040 A Multivariate Student’s t-Distribution Daniel T. Cassidy Department of Engineering Physics, McMaster University, Hamilton, ON, Canada Received 29 March 2016; accepted 14 June 2016; published 17 June 2016 Copyright © 2016 by author and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract A multivariate Student’s t-distribution is derived by analogy to the derivation of a multivariate normal (Gaussian) probability density function. This multivariate Student’s t-distribution can have different shape parameters νi for the marginal probability density functions of the multi- variate distribution. Expressions for the probability density function, for the variances, and for the covariances of the multivariate t-distribution with arbitrary shape parameters for the marginals are given. Keywords Multivariate Student’s t, Variance, Covariance, Arbitrary Shape Parameters 1. Introduction An expression for a multivariate Student’s t-distribution is presented. This expression, which is different in form than the form that is commonly used, allows the shape parameter ν for each marginal probability density function (pdf) of the multivariate pdf to be different. The form that is typically used is [1] −+ν Γ+((ν n) 2) T ( n) 2 +Σ−1 n 2 (1.[xx] [ ]) (1) ΓΣ(νν2)(π ) This “typical” form attempts to generalize the univariate Student’s t-distribution and is valid when the n marginal distributions have the same shape parameter ν . -
A New Family of Odd Generalized Nakagami (Nak-G) Distributions
TURKISH JOURNAL OF SCIENCE http:/dergipark.gov.tr/tjos VOLUME 5, ISSUE 2, 85-101 ISSN: 2587–0971 A New Family of Odd Generalized Nakagami (Nak-G) Distributions Ibrahim Abdullahia, Obalowu Jobb aYobe State University, Department of Mathematics and Statistics bUniversity of Ilorin, Department of Statistics Abstract. In this article, we proposed a new family of generalized Nak-G distributions and study some of its statistical properties, such as moments, moment generating function, quantile function, and prob- ability Weighted Moments. The Renyi entropy, expression of distribution order statistic and parameters of the model are estimated by means of maximum likelihood technique. We prove, by providing three applications to real-life data, that Nakagami Exponential (Nak-E) distribution could give a better fit when compared to its competitors. 1. Introduction There has been recent developments focus on generalized classes of continuous distributions by adding at least one shape parameters to the baseline distribution, studying the properties of these distributions and using these distributions to model data in many applied areas which include engineering, biological studies, environmental sciences and economics. Numerous methods for generating new families of distributions have been proposed [8] many researchers. The beta-generalized family of distribution was developed , Kumaraswamy generated family of distributions [5], Beta-Nakagami distribution [19], Weibull generalized family of distributions [4], Additive weibull generated distributions [12], Kummer beta generalized family of distributions [17], the Exponentiated-G family [6], the Gamma-G (type I) [21], the Gamma-G family (type II) [18], the McDonald-G [1], the Log-Gamma-G [3], A new beta generated Kumaraswamy Marshall-Olkin- G family of distributions with applications [11], Beta Marshall-Olkin-G family [2] and Logistic-G family [20]. -
A Guide on Probability Distributions
powered project A guide on probability distributions R-forge distributions Core Team University Year 2008-2009 LATEXpowered Mac OS' TeXShop edited Contents Introduction 4 I Discrete distributions 6 1 Classic discrete distribution 7 2 Not so-common discrete distribution 27 II Continuous distributions 34 3 Finite support distribution 35 4 The Gaussian family 47 5 Exponential distribution and its extensions 56 6 Chi-squared's ditribution and related extensions 75 7 Student and related distributions 84 8 Pareto family 88 9 Logistic ditribution and related extensions 108 10 Extrem Value Theory distributions 111 3 4 CONTENTS III Multivariate and generalized distributions 116 11 Generalization of common distributions 117 12 Multivariate distributions 132 13 Misc 134 Conclusion 135 Bibliography 135 A Mathematical tools 138 Introduction This guide is intended to provide a quite exhaustive (at least as I can) view on probability distri- butions. It is constructed in chapters of distribution family with a section for each distribution. Each section focuses on the tryptic: definition - estimation - application. Ultimate bibles for probability distributions are Wimmer & Altmann (1999) which lists 750 univariate discrete distributions and Johnson et al. (1994) which details continuous distributions. In the appendix, we recall the basics of probability distributions as well as \common" mathe- matical functions, cf. section A.2. And for all distribution, we use the following notations • X a random variable following a given distribution, • x a realization of this random variable, • f the density function (if it exists), • F the (cumulative) distribution function, • P (X = k) the mass probability function in k, • M the moment generating function (if it exists), • G the probability generating function (if it exists), • φ the characteristic function (if it exists), Finally all graphics are done the open source statistical software R and its numerous packages available on the Comprehensive R Archive Network (CRAN∗). -
Dictionary-Based Stochastic Expectation– Maximization for SAR
188 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 1, JANUARY 2006 Dictionary-Based Stochastic Expectation– Maximization for SAR Amplitude Probability Density Function Estimation Gabriele Moser, Member, IEEE, Josiane Zerubia, Fellow, IEEE, and Sebastiano B. Serpico, Senior Member, IEEE Abstract—In remotely sensed data analysis, a crucial problem problem as a parameter estimation problem. Several strategies is represented by the need to develop accurate models for the have been proposed in the literature to deal with parameter statistics of the pixel intensities. This paper deals with the problem estimation, e.g., the maximum-likelihood methodology [18] of probability density function (pdf) estimation in the context of and the “method of moments” [41], [46], [53]. On the contrary, synthetic aperture radar (SAR) amplitude data analysis. Several theoretical and heuristic models for the pdfs of SAR data have nonparametric pdf estimation approaches do not assume any been proposed in the literature, which have been proved to be specific analytical model for the unknown pdf, thus providing effective for different land-cover typologies, thus making the a higher flexibility, although usually involving internal archi- choice of a single optimal parametric pdf a hard task, especially tecture parameters to be set by the user [18]. In particular, when dealing with heterogeneous SAR data. In this paper, an several nonparametric kernel-based estimation and regression innovative estimation algorithm is described, which faces such a problem by adopting a finite mixture model for the amplitude architectures have been described in the literature, that have pdf, with mixture components belonging to a given dictionary of proved to be effective estimation tools, such as standard Parzen SAR-specific pdfs. -
Estimation and Testing Procedures of the Reliability Functions of Nakagami Distribution
Austrian Journal of Statistics January 2019, Volume 48, 15{34. AJShttp://www.ajs.or.at/ doi:10.17713/ajs.v48i3.827 Estimation and Testing Procedures of the Reliability Functions of Nakagami Distribution Ajit Chaturvedi Bhagwati Devi Rahul Gupta Dept. of Statistics, Dept. of Statistics, Dept. of Statistics, University of Delhi University of Jammu University of Jammu Abstract A very important distribution called Nakagami distribution is taken into considera- tion. Reliability measures R(t) = P r(X > t) and P = P r(X > Y ) are considered. Point as well as interval procedures are obtained for estimation of parameters. Uniformly Mini- mum Variance Unbiased Estimators (U.M.V.U.Es) and Maximum Likelihood Estimators (M.L.Es) are developed for the said parameters. A new technique of obtaining these esti- mators is introduced. Moment estimators for the parameters of the distribution have been found. Asymptotic confidence intervals of the parameter based on M.L.E and log(M.L.E) are also constructed. Then, testing procedures for various hypotheses are developed. At the end, Monte Carlo simulation is performed for comparing the results obtained. A real data analysis is performed to describe the procedure clearly. Keywords: Nakagami distribution, point estimation, testing procedures, confidence interval, Markov chain Monte Carlo (MCMC) procedure.. 1. Introduction and preliminaries R(t), the reliability function is the probability that a system performs its intended function without any failure at time t under the prescribed conditions. So, if we suppose that life- time of an item or any system is denoted by the random variate X then the reliability is R(t) = P r(X > t). -
Idescat. SORT. an Extension of the Slash-Elliptical Distribution. Volume 38
Statistics & Operations Research Transactions Statistics & Operations Research c SORT 38 (2) July-December 2014, 215-230 Institut d’Estad´ısticaTransactions de Catalunya ISSN: 1696-2281 [email protected] eISSN: 2013-8830 www.idescat.cat/sort/ An extension of the slash-elliptical distribution Mario A. Rojas1, Heleno Bolfarine2 and Hector´ W. Gomez´ 3 Abstract This paper introduces an extension of the slash-elliptical distribution. This new distribution is gen- erated as the quotient between two independent random variables, one from the elliptical family (numerator) and the other (denominator) a beta distribution. The resulting slash-elliptical distribu- tion potentially has a larger kurtosis coefficient than the ordinary slash-elliptical distribution. We investigate properties of this distribution such as moments and closed expressions for the density function. Moreover, an extension is proposed for the location scale situation. Likelihood equations are derived for this more general version. Results of a real data application reveal that the pro- posed model performs well, so that it is a viable alternative to replace models with lesser kurtosis flexibility. We also propose a multivariate extension. MSC: 60E05. Keywords: Slash distribution, elliptical distribution, kurtosis. 1. Introduction A distribution closely related to the normal distribution is the slash distribution. This distribution can be represented as the quotient between two independent random vari- ables, a normal one (numerator) and the power of a uniform distribution (denominator). To be more specific, we say that a random variable S follows a slash distribution if it can be written as 1 S = Z/U q , (1) 1 Departamento de Matematicas,´ Facultad de Ciencias Basicas,´ Universidad de Antofagasta, Antofagasta, Chile.