The Effect of Kurtosis on the Cross-Section of Stock Returns
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The Locally Gaussian Partial Correlation Håkon Otneim∗ Dag Tjøstheim†
The Locally Gaussian Partial Correlation Håkon Otneim∗ Dag Tjøstheim† Abstract It is well known that the dependence structure for jointly Gaussian variables can be fully captured using correlations, and that the conditional dependence structure in the same way can be described using partial correlations. The partial correlation does not, however, characterize conditional dependence in many non-Gaussian populations. This paper introduces the local Gaussian partial correlation (LGPC), a new measure of conditional dependence. It is a local version of the partial correlation coefficient that characterizes conditional dependence in a large class of populations. It has some useful and novel properties besides: The LGPC reduces to the ordinary partial correlation for jointly normal variables, and it distinguishes between positive and negative conditional dependence. Furthermore, the LGPC can be used to study departures from conditional independence in specific parts of the distribution. We provide several examples of this, both simulated and real, and derive estimation theory under a local likelihood framework. Finally, we indicate how the LGPC can be used to construct a powerful test for conditional independence, which, again, can be used to detect Granger causality in time series. 1 Introduction Estimation of conditional dependence and testing for conditional independence are extremely important topics in classical as well as modern statistics. In the last two decades, for instance, there has been a very intense development using conditional dependence in probabilistic network theory. This comes in addition to conditional multivariate time series analysis and copula analysis. For jointly Gaussian variables, conditional dependence is measured by the partial correlation coefficient. Given three jointly Gaussian stochastic variables X1, X2 and X3, X1 and X2 are conditionally independent given X3 if and only if the partial correlation between X1 and X2 given X3 is equal to zero. -
1 One Parameter Exponential Families
1 One parameter exponential families The world of exponential families bridges the gap between the Gaussian family and general dis- tributions. Many properties of Gaussians carry through to exponential families in a fairly precise sense. • In the Gaussian world, there exact small sample distributional results (i.e. t, F , χ2). • In the exponential family world, there are approximate distributional results (i.e. deviance tests). • In the general setting, we can only appeal to asymptotics. A one-parameter exponential family, F is a one-parameter family of distributions of the form Pη(dx) = exp (η · t(x) − Λ(η)) P0(dx) for some probability measure P0. The parameter η is called the natural or canonical parameter and the function Λ is called the cumulant generating function, and is simply the normalization needed to make dPη fη(x) = (x) = exp (η · t(x) − Λ(η)) dP0 a proper probability density. The random variable t(X) is the sufficient statistic of the exponential family. Note that P0 does not have to be a distribution on R, but these are of course the simplest examples. 1.0.1 A first example: Gaussian with linear sufficient statistic Consider the standard normal distribution Z e−z2=2 P0(A) = p dz A 2π and let t(x) = x. Then, the exponential family is eη·x−x2=2 Pη(dx) / p 2π and we see that Λ(η) = η2=2: eta= np.linspace(-2,2,101) CGF= eta**2/2. plt.plot(eta, CGF) A= plt.gca() A.set_xlabel(r'$\eta$', size=20) A.set_ylabel(r'$\Lambda(\eta)$', size=20) f= plt.gcf() 1 Thus, the exponential family in this setting is the collection F = fN(η; 1) : η 2 Rg : d 1.0.2 Normal with quadratic sufficient statistic on R d As a second example, take P0 = N(0;Id×d), i.e. -
On the Meaning and Use of Kurtosis
Psychological Methods Copyright 1997 by the American Psychological Association, Inc. 1997, Vol. 2, No. 3,292-307 1082-989X/97/$3.00 On the Meaning and Use of Kurtosis Lawrence T. DeCarlo Fordham University For symmetric unimodal distributions, positive kurtosis indicates heavy tails and peakedness relative to the normal distribution, whereas negative kurtosis indicates light tails and flatness. Many textbooks, however, describe or illustrate kurtosis incompletely or incorrectly. In this article, kurtosis is illustrated with well-known distributions, and aspects of its interpretation and misinterpretation are discussed. The role of kurtosis in testing univariate and multivariate normality; as a measure of departures from normality; in issues of robustness, outliers, and bimodality; in generalized tests and estimators, as well as limitations of and alternatives to the kurtosis measure [32, are discussed. It is typically noted in introductory statistics standard deviation. The normal distribution has a kur- courses that distributions can be characterized in tosis of 3, and 132 - 3 is often used so that the refer- terms of central tendency, variability, and shape. With ence normal distribution has a kurtosis of zero (132 - respect to shape, virtually every textbook defines and 3 is sometimes denoted as Y2)- A sample counterpart illustrates skewness. On the other hand, another as- to 132 can be obtained by replacing the population pect of shape, which is kurtosis, is either not discussed moments with the sample moments, which gives or, worse yet, is often described or illustrated incor- rectly. Kurtosis is also frequently not reported in re- ~(X i -- S)4/n search articles, in spite of the fact that virtually every b2 (•(X i - ~')2/n)2' statistical package provides a measure of kurtosis. -
Estimation of Correlation Coefficient in Data with Repeated Measures
Paper 2424-2018 Estimation of correlation coefficient in data with repeated measures Katherine Irimata, Arizona State University; Paul Wakim, National Institutes of Health; Xiaobai Li, National Institutes of Health ABSTRACT Repeated measurements are commonly collected in research settings. While the correlation coefficient is often used to characterize the relationship between two continuous variables, it can produce unreliable estimates in the repeated measure setting. Alternative correlation measures have been proposed, but a comprehensive evaluation of the estimators and confidence intervals is not available. We provide a comparison of correlation estimators for two continuous variables in repeated measures data. We consider five methods using SAS/STAT® software procedures, including a naïve Pearson correlation coefficient (PROC CORR), correlation of subject means (PROC CORR), partial correlation adjusting for patient ID (PROC GLM), partial correlation coefficient (PROC MIXED), and a mixed model (PROC MIXED) approach. Confidence intervals were calculated using the normal approximation, cluster bootstrap, and multistage bootstrap. The performance of the five correlation methods and confidence intervals were compared through the analysis of pharmacokinetics data collected on 18 subjects, measured over a total of 76 visits. Although the naïve estimate does not account for subject-level variability, the method produced a point estimate similar to the mixed model approach under the conditions of this example (complete data). The mixed model approach and corresponding confidence interval was the most appropriate measure of correlation as the method fully specifies the correlation structure. INTRODUCTION The correlation coefficient ρ is often used to characterize the linear relationship between two continuous variables. Estimates of the correlation (r) that are close to 0 indicate little to no association between the two variables, whereas values close to 1 or -1 indicate a strong association. -
Univariate and Multivariate Skewness and Kurtosis 1
Running head: UNIVARIATE AND MULTIVARIATE SKEWNESS AND KURTOSIS 1 Univariate and Multivariate Skewness and Kurtosis for Measuring Nonnormality: Prevalence, Influence and Estimation Meghan K. Cain, Zhiyong Zhang, and Ke-Hai Yuan University of Notre Dame Author Note This research is supported by a grant from the U.S. Department of Education (R305D140037). However, the contents of the paper do not necessarily represent the policy of the Department of Education, and you should not assume endorsement by the Federal Government. Correspondence concerning this article can be addressed to Meghan Cain ([email protected]), Ke-Hai Yuan ([email protected]), or Zhiyong Zhang ([email protected]), Department of Psychology, University of Notre Dame, 118 Haggar Hall, Notre Dame, IN 46556. UNIVARIATE AND MULTIVARIATE SKEWNESS AND KURTOSIS 2 Abstract Nonnormality of univariate data has been extensively examined previously (Blanca et al., 2013; Micceri, 1989). However, less is known of the potential nonnormality of multivariate data although multivariate analysis is commonly used in psychological and educational research. Using univariate and multivariate skewness and kurtosis as measures of nonnormality, this study examined 1,567 univariate distriubtions and 254 multivariate distributions collected from authors of articles published in Psychological Science and the American Education Research Journal. We found that 74% of univariate distributions and 68% multivariate distributions deviated from normal distributions. In a simulation study using typical values of skewness and kurtosis that we collected, we found that the resulting type I error rates were 17% in a t-test and 30% in a factor analysis under some conditions. Hence, we argue that it is time to routinely report skewness and kurtosis along with other summary statistics such as means and variances. -
A Family of Skew-Normal Distributions for Modeling Proportions and Rates with Zeros/Ones Excess
S S symmetry Article A Family of Skew-Normal Distributions for Modeling Proportions and Rates with Zeros/Ones Excess Guillermo Martínez-Flórez 1, Víctor Leiva 2,* , Emilio Gómez-Déniz 3 and Carolina Marchant 4 1 Departamento de Matemáticas y Estadística, Facultad de Ciencias Básicas, Universidad de Córdoba, Montería 14014, Colombia; [email protected] 2 Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile 3 Facultad de Economía, Empresa y Turismo, Universidad de Las Palmas de Gran Canaria and TIDES Institute, 35001 Canarias, Spain; [email protected] 4 Facultad de Ciencias Básicas, Universidad Católica del Maule, 3466706 Talca, Chile; [email protected] * Correspondence: [email protected] or [email protected] Received: 30 June 2020; Accepted: 19 August 2020; Published: 1 September 2020 Abstract: In this paper, we consider skew-normal distributions for constructing new a distribution which allows us to model proportions and rates with zero/one inflation as an alternative to the inflated beta distributions. The new distribution is a mixture between a Bernoulli distribution for explaining the zero/one excess and a censored skew-normal distribution for the continuous variable. The maximum likelihood method is used for parameter estimation. Observed and expected Fisher information matrices are derived to conduct likelihood-based inference in this new type skew-normal distribution. Given the flexibility of the new distributions, we are able to show, in real data scenarios, the good performance of our proposal. Keywords: beta distribution; centered skew-normal distribution; maximum-likelihood methods; Monte Carlo simulations; proportions; R software; rates; zero/one inflated data 1. -
Partial Correlation Lecture
Analytical Paleobiology Workshop 2018 Partial Correlation Instructor Michał Kowalewski Florida Museum of Natural History University of Florida [email protected] Partial Correlation Partial correlation attempts to estimate relationship between two variables after accounting for other variables. Synthetic Example Z <- rnorm(100, 20, 3) # let Z (‘human impact’) be independent X <- rnorm(100, 20, 1) + Z # ley X (‘vegetation density’) be strongly dependent on Z Y <- rnorm(100, 20, 1) +Z # let Y (‘animal diversity’) be also strongly dependent on Z Note that in this example X (vegetation density) and Y (animal diversity) are not interrelated directly However, they appear strongly correlated because they are both strongly influenced by Z One way to re-evaluate X and Y while minimizing effects of Z is partial correlation Computing Partial Correlation For a simplest case of three linearly related variables (x, y, z) partial correlation of x and y given z can be computed from residuals of OLS linear regression models z ~ x and z ~ y 1. Compute residuals for x (dependent) ~ z (independent) model 2. Compute residuals for y (dependent) ~ z (independent) model 3. Compute correlation between the residuals rxy_z = cor(lm(X ~ Z)$residuals), lm(Y ~ Z)$residuals)) What are residuals? OLS residuals for y = vegetation density Partial correlation XY_Z is -0.081 (recall that r for XY was 0.901) partial correlations human.impact versus vegetation.density 0.72651264 human.impact versus animal.diversity 0.71126584 vegetation.density versus animal.diversity -0.08147584 Partial and Semi-Partial Correlation “The partial correlation can be explained as the association between two random variables after eliminating the effect of all other random variables, while the semi-partial correlation eliminates the effect of a fraction of other random variables, for instance, removing the effect of all other random variables from just one of two interesting random variables. -
Tests Based on Skewness and Kurtosis for Multivariate Normality
Communications for Statistical Applications and Methods DOI: http://dx.doi.org/10.5351/CSAM.2015.22.4.361 2015, Vol. 22, No. 4, 361–375 Print ISSN 2287-7843 / Online ISSN 2383-4757 Tests Based on Skewness and Kurtosis for Multivariate Normality Namhyun Kim1;a aDepartment of Science, Hongik University, Korea Abstract A measure of skewness and kurtosis is proposed to test multivariate normality. It is based on an empirical standardization using the scaled residuals of the observations. First, we consider the statistics that take the skewness or the kurtosis for each coordinate of the scaled residuals. The null distributions of the statistics converge very slowly to the asymptotic distributions; therefore, we apply a transformation of the skewness or the kurtosis to univariate normality for each coordinate. Size and power are investigated through simulation; consequently, the null distributions of the statistics from the transformed ones are quite well approximated to asymptotic distributions. A simulation study also shows that the combined statistics of skewness and kurtosis have moderate sensitivity of all alternatives under study, and they might be candidates for an omnibus test. Keywords: Goodness of fit tests, multivariate normality, skewness, kurtosis, scaled residuals, empirical standardization, power comparison 1. Introduction Classical multivariate analysis techniques require the assumption of multivariate normality; conse- quently, there are numerous test procedures to assess the assumption in the literature. For a gen- eral review, some references are Henze (2002), Henze and Zirkler (1990), Srivastava and Mudholkar (2003), and Thode (2002, Ch.9). For comparative studies in power, we refer to Farrell et al. (2007), Horswell and Looney (1992), Mecklin and Mundfrom (2005), and Romeu and Ozturk (1993). -
Approximating the Distribution of the Product of Two Normally Distributed Random Variables
S S symmetry Article Approximating the Distribution of the Product of Two Normally Distributed Random Variables Antonio Seijas-Macías 1,2 , Amílcar Oliveira 2,3 , Teresa A. Oliveira 2,3 and Víctor Leiva 4,* 1 Departamento de Economía, Universidade da Coruña, 15071 A Coruña, Spain; [email protected] 2 CEAUL, Faculdade de Ciências, Universidade de Lisboa, 1649-014 Lisboa, Portugal; [email protected] (A.O.); [email protected] (T.A.O.) 3 Departamento de Ciências e Tecnologia, Universidade Aberta, 1269-001 Lisboa, Portugal 4 Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile * Correspondence: [email protected] or [email protected] Received: 21 June 2020; Accepted: 18 July 2020; Published: 22 July 2020 Abstract: The distribution of the product of two normally distributed random variables has been an open problem from the early years in the XXth century. First approaches tried to determinate the mathematical and statistical properties of the distribution of such a product using different types of functions. Recently, an improvement in computational techniques has performed new approaches for calculating related integrals by using numerical integration. Another approach is to adopt any other distribution to approximate the probability density function of this product. The skew-normal distribution is a generalization of the normal distribution which considers skewness making it flexible. In this work, we approximate the distribution of the product of two normally distributed random variables using a type of skew-normal distribution. The influence of the parameters of the two normal distributions on the approximation is explored. When one of the normally distributed variables has an inverse coefficient of variation greater than one, our approximation performs better than when both normally distributed variables have inverse coefficients of variation less than one. -
Linear Mixed Model Selection by Partial Correlation
LINEAR MIXED MODEL SELECTION BY PARTIAL CORRELATION Audry Alabiso A Dissertation Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY May 2020 Committee: Junfeng Shang, Advisor Andy Garcia, Graduate Faculty Representative Hanfeng Chen Craig Zirbel ii ABSTRACT Junfeng Shang, Advisor Linear mixed models (LMM) are commonly used when observations are no longer independent of each other, and instead, clustered into two or more groups. In the LMM, the mean response for each subject is modeled by a combination of fixed effects and random effects. The fixed effects are characteristics shared by all individuals in the study; they are analogous to the coefficients of the linear model. The random effects are specific to each group or cluster and help describe the correlation structure of the observations. Because of this, linear mixed models are popular when multiple measurements are made on the same subject or when there is a natural clustering or grouping of observations. Our goal in this dissertation is to perform fixed effect selection in the high-dimensional linear mixed model. We generally define high-dimensional data to be when the number of potential predictors is large relative to the sample size. High-dimensional data is common in genomic and other biological datasets. In the high-dimensional setting, selecting the fixed effect coefficients can be difficult due to the number of potential models to choose from. However, it is important to be able to do so in order to build models that are easy to interpret. -
Nonparametric Multivariate Kurtosis and Tailweight Measures
Nonparametric Multivariate Kurtosis and Tailweight Measures Jin Wang1 Northern Arizona University and Robert Serfling2 University of Texas at Dallas November 2004 – final preprint version, to appear in Journal of Nonparametric Statistics, 2005 1Department of Mathematics and Statistics, Northern Arizona University, Flagstaff, Arizona 86011-5717, USA. Email: [email protected]. 2Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas 75083- 0688, USA. Email: [email protected]. Website: www.utdallas.edu/∼serfling. Support by NSF Grant DMS-0103698 is gratefully acknowledged. Abstract For nonparametric exploration or description of a distribution, the treatment of location, spread, symmetry and skewness is followed by characterization of kurtosis. Classical moment- based kurtosis measures the dispersion of a distribution about its “shoulders”. Here we con- sider quantile-based kurtosis measures. These are robust, are defined more widely, and dis- criminate better among shapes. A univariate quantile-based kurtosis measure of Groeneveld and Meeden (1984) is extended to the multivariate case by representing it as a transform of a dispersion functional. A family of such kurtosis measures defined for a given distribution and taken together comprises a real-valued “kurtosis functional”, which has intuitive appeal as a convenient two-dimensional curve for description of the kurtosis of the distribution. Several multivariate distributions in any dimension may thus be compared with respect to their kurtosis in a single two-dimensional plot. Important properties of the new multivariate kurtosis measures are established. For example, for elliptically symmetric distributions, this measure determines the distribution within affine equivalence. Related tailweight measures, influence curves, and asymptotic behavior of sample versions are also discussed. -
Regression Analysis and Statistical Control
CHAPTER 4 REGRESSION ANALYSIS AND STATISTICAL CONTROL 4.1 INTRODUCTION Bivariate regression involves one predictor and one quantitative outcome variable. Adding a second predictor shows how statistical control works in regression analysis. The previous chap- ter described two ways to understand statistical control. In the previous chapter, the outcome variable was denoted , the predictor of interest was denoted , and the control variable was Y X1 distribute called X2. 1. We can control for an X variable by dividing data into groups on the basis of X 2 or 2 scores and then analyzing the X1, Y relationship separately within these groups. Results are rarely reported this way in journal articles; however, examining data this way makes it clear that the nature of an X1, Y relationship can change in many ways when you control for an X2 variable. 2. Another way to control for an X2 variable is obtaining a partial correlation between X1 and Y, controlling for X2. This partial correlation is denoted r1Y.2. Partial correlations are not often reported in journalpost, articles either. However, thinking about them as correlations between residuals helps you understand the mechanics of statistical control. A partial correlation between X1 and Y, controlling for X2, can be understood as a correlation between the parts of the X1 scores that are not related to X2, and the parts of the Y scores that are not related to X2. This chapter introduces the method of statistical control that is most widely used and reported. This method involves using both X1 and X2 as predictors of Y in a multiple linear regression.