(AF) Cascaded Fading Channels, 233

Total Page:16

File Type:pdf, Size:1020Kb

(AF) Cascaded Fading Channels, 233 Index A p/4-QPSK, 133–136 American Digital Cellular and Japanese time domain waveforms, 131, 132 Digital Cellular systems, 138 waveforms, 130 Amount of fading (AF) signal space, 129 cascaded fading channels, 233, 235 symbol error Gaussian pdf, 366 coherent MPSK, 137, 138 lognormal shadowing channel, 364, 366 equivalent bit error rate, 137 MRC diversity, 362 generic MPSK constellation, 136, 137 Nakagami fading, 211 k bits encoding/mapping, 136 Nakagami parameter, 364, 366 spectral efficiencies and SNR, 138, 139 Rayleigh fading, 200–201 Binomial distribution, 13–15 Rician fading, 203, 204 Bit error rate (BER), 255, 256, 307 shadowing models, 216 BPSK, 256–259 short term faded Nakagami channel, 362 CDF, 259 SSC, 363 fading channel, 259 two channel selection combining moderately shadowed channel, 260, 262 diversity, 363 MRC, 448–450 Amplitude shift keying (ASK), 121–122 Nakagami channels Arnold and Strauss’s bivariate gamma average error probability, 442, 444, 445 distribution, 76 hypergeometric function, 443 MeijerG function, 443 pdf, 449 B Rayleigh channels, 441–442 Bayes theorem, 86–87 severely shadowed channel, 260, 261 BER. See Bit error rate shadowed fading channels Bessel function, 221, 303 average error rates, 446, 447 Beta distribution, 11–13 CCI effect, 446 Binary phase shift keying (BPSK) cochannels, 448 digital signal spectra, 127 N Nakagami interferers, 445 gray encoding, 129, 130 BPSK. See Binary phase shift keying M-ary signals, 128–129 MQAM, 139–141 null-to-null criterion, 127 C QPSK Cascaded channels, 306 modulator, 131, 132 Cascaded fading channels offset-QPSK scheme, 133, 134 AF, 233, 235 phase constellation, 131 average SNR, 233 P.M. Shankar, Fading and Shadowing in Wireless Systems, 455 DOI 10.1007/978-1-4614-0367-8, # Springer Science+Business Media, LLC 2012 456 Index Cascaded fading channels (cont.) probability of miss, 91, 92 CDF, 235, 236 Q function, 92 density function, 233, 234 Rayleigh densities, 90 gamma-distribution, 232 Rician densities, 90 MeijerG function, 232–233 Differential phase shift keying (DPSK), 154 Nakagami-gamma model, 234 Digital frequency modulation (DFM) Cascaded Nakagami channels block diagram, 142, 143 AF, 395, 400, 401 CPFSK, 144 average error probabilities, 405, 406 frequency deviation, 143 BER, 401, 402 pulse shape, 143 CDFs, 396–401, 404, 405 Diversity techniques, 1 density function, SNR, 395 AF Matlab, 396 Gaussian pdf, 366 M-fold convolution, 403, 404 lognormal shadowing channel, 364, 365 MGF, 404, 405 MRC diversity, 363 MRC diversity, AF, 403–404 Nakagami parameter, 364, 366 multihop relayed communication short term faded Nakagami channel, 362 system, 395 SSC, 364 order statistics, 400 two channel selection combining outage probability, 401, 403, diversity, 363 405, 407 average error probability pdfs, SNR, 396, 398–400, 402 average error rate, 366–367 received signal power, 395 CBPSK, 367 threshold SNR, 404 CDF, 369 Cauchy distribution, 15–16 density functions, 373–377 CDFs. See Cumulative distribution functions dual branch correlated MRC, 372 Central limit theorem (CLT), 46–47 dual branch MRC, 375, 377–379 Characteristic function (CHF), 10–11, 57 dual branch SC, 371, 372, 378, 379 Chebyshev inequality, 94–95, 323 four-branch diversity, 371 Chi-squared distribution, 16–18 four channel MRC, 375, 378 Cochannel interference, 5 GSC algorithm, 371, 373 Coherent binary phase shift keying hybrid diversity approach, 375 (CBPSK), 367 Laplace transform, 367 Cumulative distribution functions (CDFs), 8–9, MGF, 367–368 207–208, 322, 330–331 MRC algorithm, 369 Nakagami channel, 368, 373 Nakagami-lognormal model, 373 D Nakagami-m channel, MRC, 370, 371 Decision theory and error rates Q function, 368 channel noise, 85–86 shadowed fading channels, 375, 377, Gaussian case 378, 379 Bayes theorem, 86–87 branch correlation effects binary channel, 86 correlated SC and SSC receiver, 337 conditional density functions, 87 exponential correlation, 334 conditional probability, 87 joint CDF, 339 error probability, 87, 89, 90 Marcum’s Q function, 337 false alarm probability, 89 modified Bessel function, 337, 339 hypothesis testing, 86, 88 MRC algorithm (see Maximal ratio likelihood ratio, 89 combining algorithm) probability of miss, 89 Nakagami parameter, 337, 338 non-Gaussian case pdf, SC algorithm, 339 false alarm probability, 91 cascaded Nakagami channels (see hypothesis testing, 91, 93 Cascaded Nakagami channels) Index 457 CDF, 325 short-term faded channel, 381 diversity receivers, 321 pdf, 314 EGC algorithm, 324 polarization diversity, 318–319 frequency diversity, 318 Rayleigh faded channel, 315 gamma shadowing, 362 received signal power, 315 generalized gamma and Weibull channels SC algorithm AF, 386 CDFs, 322 average error probability, 391, 392 SNR expression, 321–322 BER, dual diversity, 393, 394 SSC algorithm (see Switched and stay CDFs, 387–389, 391 combining algorithm) coherent BPSK modem, 389 signal processing methods, 313 complementary incomplete SNR, 316 gamma function, 390 space diversity, 317–318 detection scheme, 390 time diversity, 319 dual MRC, 392 types, 316 dual SC, BER, 394 “keyhole” scattering, 395 MGF, 392 E modulation type, 389 Equal gain combining (EGC) algorithm, 324 Nakagami-m distribution, 385 Ergodic channel capacity Nakagami pdf, 385 additive white Gaussian noise, 284, 285 SNR, pdfs, 385, 387, 388, 391 cascaded short-term fading channel, 287 GSC algorithm, 361 channel bandwidth, 285 average error probabilities, 408, 409 density function, generalized K average SNR, 357 distribution, 289–290 CDFs, 357–359, 408, 409 double Nakagami cascaded channels, 288 CDMA system, 353 MeijerG function, 285 densities and distributions, 359–360 Nakagami faded channel, 285, 286 gamma random variables, 359 Nakagami-Hoyt channels, 287, 288 joint pdf, 354–356 normalized average channel capacity, 285 Mc signals, 354 quadruple cascaded channels, 287, 289 Nakagami channels, 358, 360 Rayleigh fading, 286 Nakagami-m faded channels, 360, 361 Rician faded channel, 286, 287 normalized peak values, 360 shadowed fading channels, 290, 291 outage probabilities, 407, 410 triple cascaded channels, 287, 289 output SNR, pdfs, 408 Erlang distribution, 18 RAKE reception, 353 Exponential distribution, 18–20 Rayleigh channel, 354–356 three-branch diversity receiver, pdfs, 358, 359 F macrodiversity (see Macrodiversity F (Fisher-Snedecor) distribution, 20–21 techniques) Frequency diversity, 318 MRC algorithm (see Maximal ratio Frequency shift keying (FSK), 124–126, combining algorithm) 141–142 multipath diversity, 320 Nakagami-m distribution (see Nakagami-m distribution) G noise power, 315 Gamma distribution, 21–23 outage probability Gaussian distribution, 33–35 CDF, 381 Gaussian function, 301 MRC algorithm, 382, 384 Gaussian minimum shift keying (GMSK), Nakagami fading channels, 382 146–149 SC algorithm, 382 Generalized Bessel K (GBK) distribution, 65 sensitivity, 381 Generalized gamma distribution, 25 shadowed fading channel, 383–385 definition, 22 458 Index Generalized gamma distribution (cont.) gamma pdf, 344 generalized gamma pdf, 24 GK distribution, 344, 345 normalization factor, 25 joint pdf, 345–346 random variable, 24 MeijerG functions, 351 scaling factor, 25 MRC diversity, 348, 349 Stacy distribution, 24 MRC–SC, 345–347, 350–351 two-sided generalized gamma pdf, 26 pdfs, 352, 353 Generalized K (GK) distribution, 344, 345 shadowed fading channel, 349 Generalized selection combining (GSC) short-term fading effects, 343 algorithm, 361 SNR, SC, 349–350 average error probabilities, 408, 409 shadowing mitigation average SNR, 357 CDF, SC algorithm, 341 CDFs, 357–359, 408, 409 dual correlated Nakagami channels, CDMA system, 353 342 densities and distributions, 359–360 multiple base stations, 340 gamma random variables, 359 normalized Gaussian variable, 341 joint pdf, 354–356 SNR, 340, 342, 343 Mc signals, 354 three-base station arrangement, Nakagami channels, 358, 360 340, 341 Nakagami-m faded channels, 360, 361 Marcum’s Q functions, 3, 179–182 normalized peak values, 360 M-ary phase shift keying (MPSK). See Binary outage probabilities, 407, 410 phase shift keying output SNR, pdfs, 408 M-ary quadrature amplitude modulation RAKE reception, 353 (MQAM), 139–141 Rayleigh channel, 354–356 Matlab and Maple, 5 three-branch diversity receiver, pdfs, 358, 359 Maximal ratio combining (MRC) algorithm, GK distribution. See Generalized K 448–450 distribution Chebyshev inequality, 323 GMSK. See Gaussian minimum shift keying correlation coefficient correlated branch expression, 334 density functions, 335, 336 K SNR, fractional decline, 337 Kibble’s bivariate gamma distribution, 76 noise power, 323 Kurtosis coefficient, 10 processing algorithm, 322 signal power, 323 McKay’s bivariate gamma distribution, 76 L Meijer G function, 67, 68, 299–301, 304 Laplace distribution, 27–28 MGF. See Moment generating function Laplace transforms, 11, 307 Minimum shift keying (MSK) 8-Level phase shift keying (8PSK) modulator, 144, 145 demodulator, 158 power spectrum, 144 modulator, 158 waveform, 144–146 phase constellation, 156, 157 Modems phase encoding, 156, 157 bandwidth requirement, 174 waveform, 159 bit energy, 163 Lognormal distribution, 28–29 carrier regeneration and synchronization, 166–168 channel capacity, 175 M complementary error function, 176–179 Macrodiversity techniques correlator, 114 microdiversity systems cos() and sine() functions, 119 CDFs, 352 differentially encoded signals density functions, 348, 349 coherent vs. noncoherent modems, 156 Index 459 DPSK, 154 inter channel interference, 160, 162 error probability, 152 inverse fast
Recommended publications
  • Power Comparisons of the Rician and Gaussian Random Fields Tests for Detecting Signal from Functional Magnetic Resonance Images Hasni Idayu Binti Saidi
    University of Northern Colorado Scholarship & Creative Works @ Digital UNC Dissertations Student Research 8-2018 Power Comparisons of the Rician and Gaussian Random Fields Tests for Detecting Signal from Functional Magnetic Resonance Images Hasni Idayu Binti Saidi Follow this and additional works at: https://digscholarship.unco.edu/dissertations Recommended Citation Saidi, Hasni Idayu Binti, "Power Comparisons of the Rician and Gaussian Random Fields Tests for Detecting Signal from Functional Magnetic Resonance Images" (2018). Dissertations. 507. https://digscholarship.unco.edu/dissertations/507 This Text is brought to you for free and open access by the Student Research at Scholarship & Creative Works @ Digital UNC. It has been accepted for inclusion in Dissertations by an authorized administrator of Scholarship & Creative Works @ Digital UNC. For more information, please contact [email protected]. ©2018 HASNI IDAYU BINTI SAIDI ALL RIGHTS RESERVED UNIVERSITY OF NORTHERN COLORADO Greeley, Colorado The Graduate School POWER COMPARISONS OF THE RICIAN AND GAUSSIAN RANDOM FIELDS TESTS FOR DETECTING SIGNAL FROM FUNCTIONAL MAGNETIC RESONANCE IMAGES A Dissertation Submitted in Partial Fulfillment of the Requirement for the Degree of Doctor of Philosophy Hasni Idayu Binti Saidi College of Education and Behavioral Sciences Department of Applied Statistics and Research Methods August 2018 This dissertation by: Hasni Idayu Binti Saidi Entitled: Power Comparisons of the Rician and Gaussian Random Fields Tests for Detecting Signal from Functional Magnetic Resonance Images has been approved as meeting the requirement for the Degree of Doctor of Philosophy in College of Education and Behavioral Sciences in Department of Applied Statistics and Research Methods Accepted by the Doctoral Committee Khalil Shafie Holighi, Ph.D., Research Advisor Trent Lalonde, Ph.D., Committee Member Jay Schaffer, Ph.D., Committee Member Heng-Yu Ku, Ph.D., Faculty Representative Date of Dissertation Defense Accepted by the Graduate School Linda L.
    [Show full text]
  • 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].
    [Show full text]
  • Intensity Distribution of Interplanetary Scintillation at 408 Mhz
    Aust. J. Phys., 1975,28,621-32 Intensity Distribution of Interplanetary Scintillation at 408 MHz R. G. Milne Chatterton Astrophysics Department, School of Physics, University of Sydney, Sydney, N.S.W. 2006. Abstract It is shown that interplanetary scintillation of small-diameter radio sources at 408 MHz produces intensity fluctuations which are well fitted by a Rice-squared. distribution, better so than is usually claimed. The observed distribution can be used to estimate the proportion of flux density in the core of 'core-halo' sources without the need for calibration against known point sources. 1. Introduction The observed intensity of a radio source of angular diameter ;s 1" arc shows rapid fluctuations when the line of sight passes close to the Sun. This interplanetary scintillation (IPS) is due to diffraction by electron density variations which move outwards from the Sun at high velocities (-350 kms-1) •. In a typical IPS observation the fluctuating intensity Set) from a radio source is reCorded for a few minutes. This procedure is repeated on several occasions during the couple of months that the line of sight is within - 20° (for observatio~s at 408 MHz) of the direction of the Sun. For each observation we derive a mean intensity 8; a scintillation index m defined by m2 = «(S-8)2)/82, where ( ) denote the expectation value; intensity moments of order q QI! = «(S-8)4); and the skewness parameter Y1 = Q3 Q;3/2, hereafter referred to as y. A histogram, or probability distribution, of the normalized intensity S/8 is also constructed. The present paper is concerned with estimating the form of this distribution.
    [Show full text]
  • 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.
    [Show full text]
  • 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).
    [Show full text]
  • Newdistns: an R Package for New Families of Distributions
    JSS Journal of Statistical Software March 2016, Volume 69, Issue 10. doi: 10.18637/jss.v069.i10 Newdistns: An R Package for New Families of Distributions Saralees Nadarajah Ricardo Rocha University of Manchester Universidade Federal de São Carlos Abstract The contributed R package Newdistns written by the authors is introduced. This pack- age computes the probability density function, cumulative distribution function, quantile function, random numbers and some measures of inference for nineteen families of distri- butions. Each family is flexible enough to encompass a large number of structures. The use of the package is illustrated using a real data set. Also robustness of random number generation is checked by simulation. Keywords: cumulative distribution function, probability density function, quantile function, random numbers. 1. Introduction Let G be any valid cumulative distribution function defined on the real line. The last decade or so has seen many approaches proposed for generating new distributions based on G. All of these approaches can be put in the form F (x) = B (G(x)) , (1) where B : [0, 1] → [0, 1] and F is a valid cumulative distribution function. So, for every G one can use (1) to generate a new distribution. The first approach of the kind of (1) proposed in recent years was that due to Marshall and Olkin(1997). In Marshall and Olkin(1997), B was taken to be B(p) = βp/ {1 − (1 − β)p} for β > 0. The distributions generated in this way using (1) will be referred to as Marshall Olkin G distributions. Since Marshall and Olkin(1997), many other approaches have been proposed.
    [Show full text]
  • University of Nevada, Reno Generalized Univariate
    University of Nevada, Reno Generalized Univariate Distributions and a New Asymmetric Laplace Model A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Mathematics By Palash Sharma Dr. Tomasz Kozubowski/Thesis Advisor August, 2017 c 2017 Palash Sharma ALL RIGHTS RESERVED THE GRADUATE SCHOOL We recommend that the thesis prepared under our supervision by Palash Sharma entitled Generalized Univariate Distributions and a New Asymmetric Laplace Model be accepted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Tomasz J. Kozubowski, Ph.D., Advisor Anna Panorska, Ph.D., Committee Member Minggen Lu, Ph.D., Graduate School Representative David Zeh, Ph.D., Dean, Graduate School August, 2017 i ABSTRACT Generalized Univariate Distributions and a New Asymmetric Laplace Model By Palash Sharma This work provides a survey of general class of distributions generated from a mixture of beta random variables. We provide an extensive review of the literature, concern- ing generating new distributions via the inverse CDF transformation. In particular, we account for beta generated and Kumaraswamy generated families of distributions. We provide a brief summary of each of their families of distributions. We also propose a new asymmetric mixture distribution, which is an alternative to beta generated dis- tributions. We provide basic properties of this new class of distributions generated from the Laplace model. We also address the issue of parameter estimation of this new skew generalized Laplace model. ii ACKNOWLEDGMENTS At first, I would like to thank my honorable thesis advisor, Professor Tomasz J. Kozubowski, who showed me a great interest in the field of statistics and probability theory.
    [Show full text]
  • Idealized Models of the Joint Probability Distribution of Wind Speeds
    Nonlin. Processes Geophys., 25, 335–353, 2018 https://doi.org/10.5194/npg-25-335-2018 © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. Idealized models of the joint probability distribution of wind speeds Adam H. Monahan School of Earth and Ocean Sciences, University of Victoria, P.O. Box 3065 STN CSC, Victoria, BC, Canada, V8W 3V6 Correspondence: Adam H. Monahan ([email protected]) Received: 24 October 2017 – Discussion started: 2 November 2017 Revised: 6 February 2018 – Accepted: 26 March 2018 – Published: 2 May 2018 Abstract. The joint probability distribution of wind speeds position in space, or point in time. The simplest measure of at two separate locations in space or points in time com- statistical dependence, the correlation coefficient, is a natu- pletely characterizes the statistical dependence of these two ral measure for Gaussian-distributed quantities but does not quantities, providing more information than linear measures fully characterize dependence for non-Gaussian variables. such as correlation. In this study, we consider two models The most general representation of dependence between two of the joint distribution of wind speeds obtained from ide- or more quantities is their joint probability distribution. The alized models of the dependence structure of the horizon- joint probability distribution for a multivariate Gaussian is tal wind velocity components. The bivariate Rice distribu- well known, and expressed in terms of the mean and co- tion follows from assuming that the wind components have variance matrix (e.g. Wilks, 2005; von Storch and Zwiers, Gaussian and isotropic fluctuations. The bivariate Weibull 1999).
    [Show full text]
  • Field Guide to Continuous Probability Distributions
    Field Guide to Continuous Probability Distributions Gavin E. Crooks v 1.0.0 2019 G. E. Crooks – Field Guide to Probability Distributions v 1.0.0 Copyright © 2010-2019 Gavin E. Crooks ISBN: 978-1-7339381-0-5 http://threeplusone.com/fieldguide Berkeley Institute for Theoretical Sciences (BITS) typeset on 2019-04-10 with XeTeX version 0.99999 fonts: Trump Mediaeval (text), Euler (math) 271828182845904 2 G. E. Crooks – Field Guide to Probability Distributions Preface: The search for GUD A common problem is that of describing the probability distribution of a single, continuous variable. A few distributions, such as the normal and exponential, were discovered in the 1800’s or earlier. But about a century ago the great statistician, Karl Pearson, realized that the known probabil- ity distributions were not sufficient to handle all of the phenomena then under investigation, and set out to create new distributions with useful properties. During the 20th century this process continued with abandon and a vast menagerie of distinct mathematical forms were discovered and invented, investigated, analyzed, rediscovered and renamed, all for the purpose of de- scribing the probability of some interesting variable. There are hundreds of named distributions and synonyms in current usage. The apparent diver- sity is unending and disorienting. Fortunately, the situation is less confused than it might at first appear. Most common, continuous, univariate, unimodal distributions can be orga- nized into a small number of distinct families, which are all special cases of a single Grand Unified Distribution. This compendium details these hun- dred or so simple distributions, their properties and their interrelations.
    [Show full text]
  • Probability Models and Statistical Tests for Extreme Precipitation Based on Generalized Negative Binomial Distributions
    mathematics Article Probability Models and Statistical Tests for Extreme Precipitation Based on Generalized Negative Binomial Distributions Victor Korolev 1,2,3,4 and Andrey Gorshenin 1,2,3,* 1 Moscow Center for Fundamental and Applied Mathematics, Lomonosov Moscow State University, 119991 Moscow, Russia; [email protected] 2 Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, 119991 Moscow, Russia 3 Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 119333 Moscow, Russia 4 Department of Mathematics, School of Science, Hangzhou Dianzi University, Hangzhou 310018, China * Correspondence: [email protected] Received: 4 April 2020; Accepted: 14 April 2020; Published: 16 April 2020 Abstract: Mathematical models are proposed for statistical regularities of maximum daily precipitation within a wet period and total precipitation volume per wet period. The proposed models are based on the generalized negative binomial (GNB) distribution of the duration of a wet period. The GNB distribution is a mixed Poisson distribution, the mixing distribution being generalized gamma (GG). The GNB distribution demonstrates excellent fit with real data of durations of wet periods measured in days. By means of limit theorems for statistics constructed from samples with random sizes having the GNB distribution, asymptotic approximations are proposed for the distributions of maximum daily precipitation volume within a wet period and total precipitation volume for a wet period. It is shown that the exponent power parameter in the mixing GG distribution matches slow global climate trends. The bounds for the accuracy of the proposed approximations are presented. Several tests for daily precipitation, total precipitation volume and precipitation intensities to be abnormally extremal are proposed and compared to the traditional PoT-method.
    [Show full text]
  • Concentration Inequalities from Likelihood Ratio Method
    Concentration Inequalities from Likelihood Ratio Method ∗ Xinjia Chen September 2014 Abstract We explore the applications of our previously established likelihood-ratio method for deriving con- centration inequalities for a wide variety of univariate and multivariate distributions. New concentration inequalities for various distributions are developed without the idea of minimizing moment generating functions. Contents 1 Introduction 3 2 Likelihood Ratio Method 4 2.1 GeneralPrinciple.................................... ...... 4 2.2 Construction of Parameterized Distributions . ............. 5 2.2.1 WeightFunction .................................... .. 5 2.2.2 ParameterRestriction .............................. ..... 6 3 Concentration Inequalities for Univariate Distributions 7 3.1 BetaDistribution.................................... ...... 7 3.2 Beta Negative Binomial Distribution . ........ 7 3.3 Beta-Prime Distribution . ....... 8 arXiv:1409.6276v1 [math.ST] 1 Sep 2014 3.4 BorelDistribution ................................... ...... 8 3.5 ConsulDistribution .................................. ...... 8 3.6 GeetaDistribution ................................... ...... 9 3.7 GumbelDistribution.................................. ...... 9 3.8 InverseGammaDistribution. ........ 9 3.9 Inverse Gaussian Distribution . ......... 10 3.10 Lagrangian Logarithmic Distribution . .......... 10 3.11 Lagrangian Negative Binomial Distribution . .......... 10 3.12 Laplace Distribution . ....... 11 ∗The author is afflicted with the Department of Electrical
    [Show full text]
  • MPS: an R Package for Modelling New Families of Distributions
    MPS: An R package for modelling new families of distributions Mahdi Teimouri Department of Statistics Gonbad Kavous University Gonbad Kavous, IRAN Abstract: We introduce an R package, called MPS, for computing the probability density func- tion, computing the cumulative distribution function, computing the quantile function, simulating random variables, and estimating the parameters of 24 new shifted families of distributions. By considering an extra shift (location) parameter for each family more flexibility yields. Under some situations, since the maximum likelihood estimators may fail to exist, we adopt the well-known maximum product spacings approach to estimate the parameters of shifted 24 new families of distributions. The performance of the MPS package for computing the cdf, pdf, and simulating random samples will be checked by examples. The performance of the maximum product spacings approach is demonstrated by executing MPS package for three sets of real data. As it will be shown, for the first set, the maximum likelihood estimators break down but MPS package find them. For the second set, adding the location parameter leads to acceptance the model while absence of the location parameter makes the model quite inappropriate. For the third set, presence of the location parameter yields a better fit. Keywords: Cumulative distribution function; Maximum likelihood estimation; Method of maxi- mum product spacings; Probability density function; Quantile function; R package; Simulation; 1 Introduction Over the last two decades, generalization of the statistical distributions has attracted much at- tention in the literature. Most of these extensions have been spawned by applications found in arXiv:1809.02959v1 [stat.CO] 9 Sep 2018 analyzing lifetime data.
    [Show full text]