A Note on Inference in a Bivariate Normal Distribution Model Jaya
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4.2 Variance and Covariance
4.2 Variance and Covariance The most important measure of variability of a random variable X is obtained by letting g(X) = (X − µ)2 then E[g(X)] gives a measure of the variability of the distribution of X. 1 Definition 4.3 Let X be a random variable with probability distribution f(x) and mean µ. The variance of X is σ 2 = E[(X − µ)2 ] = ∑(x − µ)2 f (x) x if X is discrete, and ∞ σ 2 = E[(X − µ)2 ] = ∫ (x − µ)2 f (x)dx if X is continuous. −∞ ¾ The positive square root of the variance, σ, is called the standard deviation of X. ¾ The quantity x - µ is called the deviation of an observation, x, from its mean. 2 Note σ2≥0 When the standard deviation of a random variable is small, we expect most of the values of X to be grouped around mean. We often use standard deviations to compare two or more distributions that have the same unit measurements. 3 Example 4.8 Page97 Let the random variable X represent the number of automobiles that are used for official business purposes on any given workday. The probability distributions of X for two companies are given below: x 12 3 Company A: f(x) 0.3 0.4 0.3 x 01234 Company B: f(x) 0.2 0.1 0.3 0.3 0.1 Find the variances of X for the two companies. Solution: µ = 2 µ A = (1)(0.3) + (2)(0.4) + (3)(0.3) = 2 B 2 2 2 2 σ A = (1− 2) (0.3) + (2 − 2) (0.4) + (3− 2) (0.3) = 0.6 2 2 4 σ B > σ A 2 2 4 σ B = ∑(x − 2) f (x) =1.6 x=0 Theorem 4.2 The variance of a random variable X is σ 2 = E(X 2 ) − µ 2. -
UC Riverside UC Riverside Previously Published Works
UC Riverside UC Riverside Previously Published Works Title Fisher information matrix: A tool for dimension reduction, projection pursuit, independent component analysis, and more Permalink https://escholarship.org/uc/item/9351z60j Authors Lindsay, Bruce G Yao, Weixin Publication Date 2012 Peer reviewed eScholarship.org Powered by the California Digital Library University of California 712 The Canadian Journal of Statistics Vol. 40, No. 4, 2012, Pages 712–730 La revue canadienne de statistique Fisher information matrix: A tool for dimension reduction, projection pursuit, independent component analysis, and more Bruce G. LINDSAY1 and Weixin YAO2* 1Department of Statistics, The Pennsylvania State University, University Park, PA 16802, USA 2Department of Statistics, Kansas State University, Manhattan, KS 66502, USA Key words and phrases: Dimension reduction; Fisher information matrix; independent component analysis; projection pursuit; white noise matrix. MSC 2010: Primary 62-07; secondary 62H99. Abstract: Hui & Lindsay (2010) proposed a new dimension reduction method for multivariate data. It was based on the so-called white noise matrices derived from the Fisher information matrix. Their theory and empirical studies demonstrated that this method can detect interesting features from high-dimensional data even with a moderate sample size. The theoretical emphasis in that paper was the detection of non-normal projections. In this paper we show how to decompose the information matrix into non-negative definite information terms in a manner akin to a matrix analysis of variance. Appropriate information matrices will be identified for diagnostics for such important modern modelling techniques as independent component models, Markov dependence models, and spherical symmetry. The Canadian Journal of Statistics 40: 712– 730; 2012 © 2012 Statistical Society of Canada Resum´ e:´ Hui et Lindsay (2010) ont propose´ une nouvelle methode´ de reduction´ de la dimension pour les donnees´ multidimensionnelles. -
Statistical Estimation in Multivariate Normal Distribution
STATISTICAL ESTIMATION IN MULTIVARIATE NORMAL DISTRIBUTION WITH A BLOCK OF MISSING OBSERVATIONS by YI LIU Presented to the Faculty of the Graduate School of The University of Texas at Arlington in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY THE UNIVERSITY OF TEXAS AT ARLINGTON DECEMBER 2017 Copyright © by YI LIU 2017 All Rights Reserved ii To Alex and My Parents iii Acknowledgements I would like to thank my supervisor, Dr. Chien-Pai Han, for his instructing, guiding and supporting me over the years. You have set an example of excellence as a researcher, mentor, instructor and role model. I would like to thank Dr. Shan Sun-Mitchell for her continuously encouraging and instructing. You are both a good teacher and helpful friend. I would like to thank my thesis committee members Dr. Suvra Pal and Dr. Jonghyun Yun for their discussion, ideas and feedback which are invaluable. I would like to thank the graduate advisor, Dr. Hristo Kojouharov, for his instructing, help and patience. I would like to thank the chairman Dr. Jianzhong Su, Dr. Minerva Cordero-Epperson, Lona Donnelly, Libby Carroll and other staffs for their help. I would like to thank my manager, Robert Schermerhorn, for his understanding, encouraging and supporting which make this happen. I would like to thank my employer Sabre and my coworkers for their support in the past two years. I would like to thank my husband Alex for his encouraging and supporting over all these years. In particularly, I would like to thank my parents -- without the inspiration, drive and support that you have given me, I might not be the person I am today. -
On the Efficiency and Consistency of Likelihood Estimation in Multivariate
On the efficiency and consistency of likelihood estimation in multivariate conditionally heteroskedastic dynamic regression models∗ Gabriele Fiorentini Università di Firenze and RCEA, Viale Morgagni 59, I-50134 Firenze, Italy <fi[email protected]fi.it> Enrique Sentana CEMFI, Casado del Alisal 5, E-28014 Madrid, Spain <sentana@cemfi.es> Revised: October 2010 Abstract We rank the efficiency of several likelihood-based parametric and semiparametric estima- tors of conditional mean and variance parameters in multivariate dynamic models with po- tentially asymmetric and leptokurtic strong white noise innovations. We detailedly study the elliptical case, and show that Gaussian pseudo maximum likelihood estimators are inefficient except under normality. We provide consistency conditions for distributionally misspecified maximum likelihood estimators, and show that they coincide with the partial adaptivity con- ditions of semiparametric procedures. We propose Hausman tests that compare Gaussian pseudo maximum likelihood estimators with more efficient but less robust competitors. Fi- nally, we provide finite sample results through Monte Carlo simulations. Keywords: Adaptivity, Elliptical Distributions, Hausman tests, Semiparametric Esti- mators. JEL: C13, C14, C12, C51, C52 ∗We would like to thank Dante Amengual, Manuel Arellano, Nour Meddahi, Javier Mencía, Olivier Scaillet, Paolo Zaffaroni, participants at the European Meeting of the Econometric Society (Stockholm, 2003), the Sympo- sium on Economic Analysis (Seville, 2003), the CAF Conference on Multivariate Modelling in Finance and Risk Management (Sandbjerg, 2006), the Second Italian Congress on Econometrics and Empirical Economics (Rimini, 2007), as well as audiences at AUEB, Bocconi, Cass Business School, CEMFI, CREST, EUI, Florence, NYU, RCEA, Roma La Sapienza and Queen Mary for useful comments and suggestions. Of course, the usual caveat applies. -
A Sufficiency Paradox: an Insufficient Statistic Preserving the Fisher
A Sufficiency Paradox: An Insufficient Statistic Preserving the Fisher Information Abram KAGAN and Lawrence A. SHEPP this it is possible to find an insufficient statistic T such that (1) holds. An example of a regular statistical experiment is constructed The following example illustrates this. Let g(x) be the density where an insufficient statistic preserves the Fisher information function of a gamma distribution Gamma(3, 1), that is, contained in the data. The data are a pair (∆,X) where ∆ is a 2 −x binary random variable and, given ∆, X has a density f(x−θ|∆) g(x)=(1/2)x e ,x≥ 0; = 0,x<0. depending on a location parameter θ. The phenomenon is based on the fact that f(x|∆) smoothly vanishes at one point; it can be Take a binary variable ∆ with eliminated by adding to the regularity of a statistical experiment P (∆=1)= w , positivity of the density function. 1 P (∆=2)= w2,w1 + w2 =1,w1 =/ w2 (2) KEY WORDS: Convexity; Regularity; Statistical experi- ment. and let Y be a continuous random variable with the conditional density f(y|∆) given by f1(y)=f(y|∆=1)=.7g(y),y≥ 0; = .3g(−y),y≤ 0, 1. INTRODUCTION (3) Let P = {p(x; θ),θ∈ Θ} be a parametric family of densities f2(y)=f(y|∆=2)=.3g(y),y≥ 0; = .7g(−y),y≤ 0. (with respect to a measure µ) of a random element X taking values in a measurable space (X , A), the parameter space Θ (4) being an interval. Following Ibragimov and Has’minskii (1981, There is nothing special in the pair (.7,.3); any pair of positive chap. -
Sampling Student's T Distribution – Use of the Inverse Cumulative
Sampling Student’s T distribution – use of the inverse cumulative distribution function William T. Shaw Department of Mathematics, King’s College, The Strand, London WC2R 2LS, UK With the current interest in copula methods, and fat-tailed or other non-normal distributions, it is appropriate to investigate technologies for managing marginal distributions of interest. We explore “Student’s” T distribution, survey its simulation, and present some new techniques for simulation. In particular, for a given real (not necessarily integer) value n of the number of degrees of freedom, −1 we give a pair of power series approximations for the inverse, Fn ,ofthe cumulative distribution function (CDF), Fn.Wealsogivesomesimpleandvery fast exact and iterative techniques for defining this function when n is an even −1 integer, based on the observation that for such cases the calculation of Fn amounts to the solution of a reduced-form polynomial equation of degree n − 1. We also explain the use of Cornish–Fisher expansions to define the inverse CDF as the composition of the inverse CDF for the normal case with a simple polynomial map. The methods presented are well adapted for use with copula and quasi-Monte-Carlo techniques. 1 Introduction There is much interest in many areas of financial modeling on the use of copulas to glue together marginal univariate distributions where there is no easy canonical multivariate distribution, or one wishes to have flexibility in the mechanism for combination. One of the more interesting marginal distributions is the “Student’s” T distribution. This statistical distribution was published by W. Gosset in 1908. -
The Smoothed Median and the Bootstrap
Biometrika (2001), 88, 2, pp. 519–534 © 2001 Biometrika Trust Printed in Great Britain The smoothed median and the bootstrap B B. M. BROWN School of Mathematics, University of South Australia, Adelaide, South Australia 5005, Australia [email protected] PETER HALL Centre for Mathematics & its Applications, Australian National University, Canberra, A.C.T . 0200, Australia [email protected] G. A. YOUNG Statistical L aboratory, University of Cambridge, Cambridge CB3 0WB, U.K. [email protected] S Even in one dimension the sample median exhibits very poor performance when used in conjunction with the bootstrap. For example, both the percentile-t bootstrap and the calibrated percentile method fail to give second-order accuracy when applied to the median. The situation is generally similar for other rank-based methods, particularly in more than one dimension. Some of these problems can be overcome by smoothing, but that usually requires explicit choice of the smoothing parameter. In the present paper we suggest a new, implicitly smoothed version of the k-variate sample median, based on a particularly smooth objective function. Our procedure preserves many features of the conventional median, such as robustness and high efficiency, in fact higher than for the conventional median, in the case of normal data. It is however substantially more amenable to application of the bootstrap. Focusing on the univariate case, we demonstrate these properties both theoretically and numerically. Some key words: Bootstrap; Calibrated percentile method; Median; Percentile-t; Rank methods; Smoothed median. 1. I Rank methods are based on simple combinatorial ideas of permutations and sign changes, which are attractive in applications far removed from the assumptions of normal linear model theory. -
Chapter 23 Flexible Budgets and Standard Cost Systems
Chapter 23 Flexible Budgets and Standard Cost Systems Review Questions 1. What is a variance? A variance is the difference between an actual amount and the budgeted amount. 2. Explain the difference between a favorable and an unfavorable variance. A variance is favorable if it increases operating income. For example, if actual revenue is greater than budgeted revenue or if actual expense is less than budgeted expense, then the variance is favorable. If the variance decreases operating income, the variance is unfavorable. For example, if actual revenue is less than budgeted revenue or if actual expense is greater than budgeted expense, the variance is unfavorable. 3. What is a static budget performance report? A static budget is a budget prepared for only one level of sales volume. A static budget performance report compares actual results to the expected results in the static budget and reports the differences (static budget variances). 4. How do flexible budgets differ from static budgets? A flexible budget is a budget prepared for various levels of sales volume within a relevant range. A static budget is prepared for only one level of sales volume—the expected number of units sold— and it doesn’t change after it is developed. 5. How is a flexible budget used? Because a flexible budget is prepared for various levels of sales volume within a relevant range, it provides the basis for preparing the flexible budget performance report and understanding the two components of the overall static budget variance (a static budget is prepared for only one level of sales volume, and a static budget performance report shows only the overall static budget variance). -
Random Variables Generation
Random Variables Generation Revised version of the slides based on the book Discrete-Event Simulation: a first course L.L. Leemis & S.K. Park Section(s) 6.1, 6.2, 7.1, 7.2 c 2006 Pearson Ed., Inc. 0-13-142917-5 Discrete-Event Simulation Random Variables Generation 1/80 Introduction Monte Carlo Simulators differ from Trace Driven simulators because of the use of Random Number Generators to represent the variability that affects the behaviors of real systems. Uniformly distributed random variables are the most elementary representations that we can use in Monte Carlo simulation, but are not enough to capture the complexity of real systems. We must thus devise methods for generating instances (variates) of arbitrary random variables Properly using uniform random numbers, it is possible to obtain this result. In the sequel we will first recall some basic properties of Discrete and Continuous random variables and then we will discuss several methods to obtain their variates Discrete-Event Simulation Random Variables Generation 2/80 Basic Probability Concepts Empirical Probability, derives from performing an experiment many times n and counting the number of occurrences na of an event A The relative frequency of occurrence of event is na/n The frequency theory of probability asserts thatA the relative frequency converges as n → ∞ n Pr( )= lim a A n→∞ n Axiomatic Probability is a formal, set-theoretic approach Mathematically construct the sample space and calculate the number of events A The two are complementary! Discrete-Event Simulation Random -
Comparison of the Estimation Efficiency of Regression
J. Stat. Appl. Pro. Lett. 6, No. 1, 11-20 (2019) 11 Journal of Statistics Applications & Probability Letters An International Journal http://dx.doi.org/10.18576/jsapl/060102 Comparison of the Estimation Efficiency of Regression Parameters Using the Bayesian Method and the Quantile Function Ismail Hassan Abdullatif Al-Sabri1,2 1 Department of Mathematics, College of Science and Arts (Muhayil Asir), King Khalid University, KSA 2 Department of Mathematics and Computer, Faculty of Science, Ibb University, Yemen Received: 3 Jul. 2018, Revised: 2 Sep. 2018, Accepted: 12 Sep. 2018 Published online: 1 Jan. 2019 Abstract: There are several classical as well as modern methods to predict model parameters. The modern methods include the Bayesian method and the quantile function method, which are both used in this study to estimate Multiple Linear Regression (MLR) parameters. Unlike the classical methods, the Bayesian method is based on the assumption that the model parameters are variable, rather than fixed, estimating the model parameters by the integration of prior information (prior distribution) with the sample information (posterior distribution) of the phenomenon under study. By contrast, the quantile function method aims to construct the error probability function using least squares and the inverse distribution function. The study investigated the efficiency of each of them, and found that the quantile function is more efficient than the Bayesian method, as the values of Theil’s Coefficient U and least squares of both methods 2 2 came to be U = 0.052 and ∑ei = 0.011, compared to U = 0.556 and ∑ei = 1.162, respectively. Keywords: Bayesian Method, Prior Distribution, Posterior Distribution, Quantile Function, Prediction Model 1 Introduction Multiple linear regression (MLR) analysis is an important statistical method for predicting the values of a dependent variable using the values of a set of independent variables. -
Risk, Scores, Fisher Information, and Glrts (Supplementary Material for Math 494) Stanley Sawyer — Washington University Vs
Risk, Scores, Fisher Information, and GLRTs (Supplementary Material for Math 494) Stanley Sawyer | Washington University Vs. April 24, 2010 Table of Contents 1. Statistics and Esimatiors 2. Unbiased Estimators, Risk, and Relative Risk 3. Scores and Fisher Information 4. Proof of the Cram¶erRao Inequality 5. Maximum Likelihood Estimators are Asymptotically E±cient 6. The Most Powerful Hypothesis Tests are Likelihood Ratio Tests 7. Generalized Likelihood Ratio Tests 8. Fisher's Meta-Analysis Theorem 9. A Tale of Two Contingency-Table Tests 1. Statistics and Estimators. Let X1;X2;:::;Xn be an independent sample of observations from a probability density f(x; θ). Here f(x; θ) can be either discrete (like the Poisson or Bernoulli distributions) or continuous (like normal and exponential distributions). In general, a statistic is an arbitrary function T (X1;:::;Xn) of the data values X1;:::;Xn. Thus T (X) for X = (X1;X2;:::;Xn) can depend on X1;:::;Xn, but cannot depend on θ. Some typical examples of statistics are X + X + ::: + X T (X ;:::;X ) = X = 1 2 n (1:1) 1 n n = Xmax = maxf Xk : 1 · k · n g = Xmed = medianf Xk : 1 · k · n g These examples have the property that the statistic T (X) is a symmetric function of X = (X1;:::;Xn). That is, any permutation of the sample X1;:::;Xn preserves the value of the statistic. This is not true in general: For example, for n = 4 and X4 > 0, T (X1;X2;X3;X4) = X1X2 + (1=2)X3=X4 is also a statistic. A statistic T (X) is called an estimator of a parameter θ if it is a statistic that we think might give a reasonable guess for the true value of θ. -
Evaluating Fisher Information in Order Statistics Mary F
Southern Illinois University Carbondale OpenSIUC Research Papers Graduate School 2011 Evaluating Fisher Information in Order Statistics Mary F. Dorn [email protected] Follow this and additional works at: http://opensiuc.lib.siu.edu/gs_rp Recommended Citation Dorn, Mary F., "Evaluating Fisher Information in Order Statistics" (2011). Research Papers. Paper 166. http://opensiuc.lib.siu.edu/gs_rp/166 This Article is brought to you for free and open access by the Graduate School at OpenSIUC. It has been accepted for inclusion in Research Papers by an authorized administrator of OpenSIUC. For more information, please contact [email protected]. EVALUATING FISHER INFORMATION IN ORDER STATISTICS by Mary Frances Dorn B.A., University of Chicago, 2009 A Research Paper Submitted in Partial Fulfillment of the Requirements for the Master of Science Degree Department of Mathematics in the Graduate School Southern Illinois University Carbondale July 2011 RESEARCH PAPER APPROVAL EVALUATING FISHER INFORMATION IN ORDER STATISTICS By Mary Frances Dorn A Research Paper Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in the field of Mathematics Approved by: Dr. Sakthivel Jeyaratnam, Chair Dr. David Olive Dr. Kathleen Pericak-Spector Graduate School Southern Illinois University Carbondale July 14, 2011 ACKNOWLEDGMENTS I would like to express my deepest appreciation to my advisor, Dr. Sakthivel Jeyaratnam, for his help and guidance throughout the development of this paper. Special thanks go to Dr. Olive and Dr. Pericak-Spector for serving on my committee. I would also like to thank the Department of Mathematics at SIUC for providing many wonderful learning experiences. Finally, none of this would have been possible without the continuous support and encouragement from my parents.