A Generalization on Weighted Means and Convex Functions with Respect to the Non-Newtonian Calculus

Total Page:16

File Type:pdf, Size:1020Kb

A Generalization on Weighted Means and Convex Functions with Respect to the Non-Newtonian Calculus Hindawi Publishing Corporation International Journal of Analysis Volume 2016, Article ID 5416751, 9 pages http://dx.doi.org/10.1155/2016/5416751 Research Article A Generalization on Weighted Means and Convex Functions with respect to the Non-Newtonian Calculus ULur Kadak1 and Yusuf Gürefe2 1 Department of Mathematics, Bozok University, 66100 Yozgat, Turkey 2Department of Econometrics, Us¸ak University, 64300 Us¸ak, Turkey Correspondence should be addressed to Ugur˘ Kadak; [email protected] Received 22 July 2016; Accepted 19 September 2016 Academic Editor: Julien Salomon Copyright © 2016 U. Kadak and Y. Gurefe.¨ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper is devoted to investigating some characteristic features of weighted means and convex functions in terms of the non- Newtonian calculus which is a self-contained system independent of any other system of calculus. It is shown that there are infinitely many such useful types of weighted means and convex functions depending on the choice of generating functions. Moreover, some relations between classical weighted mean and its non-Newtonian version are compared and discussed in a table. Also, some geometric interpretations of convex functions are presented with respect to the non-Newtonian slope. Finally, using multiplicative continuous convex functions we give an application. 1. Introduction some cases, for example, for wage-rate (in dollars, euro, etc.) related problems, the use of bigeometric calculus which is It is well known that the theory of convex functions and a kind of non-Newtonian calculus is advocated instead of a weighted means plays a very important role in mathematics traditional Newtonian one. and other fields. There is wide literature covering this topic Many authors have extensively developed the notion of (see, e.g., [1–8]). Nowadays the study of convex functions multiplicative calculus; see [12–14] for details. Also some has evolved into a larger theory about functions which are authorshavealsoworkedontheclassicalsequencespaces adapted to other geometries of the domain and/or obey and related topics by using non-Newtonian calculus [15–17]. otherlawsofcomparisonofmeans.Alsothestudyofconvex Furthermore, Kadak et al. [18, 19] characterized the classes of functions begins in the context of real-valued functions of a matrix transformations between certain sequence spaces over real variable. More important, they will serve as a model for the non-Newtonian complex field and generalized Runge- deep generalizations into the setting of several variables. Kutta method with respect to the non-Newtonian calculus. As an alternative to the classical calculus, Grossman and For more details, see [20–22]. Katz [9–11] introduced the non-Newtonian calculus consist- The main focus of this work is to extend weighted ing of the branches of geometric, quadratic and harmonic means and convex functions based on various generator + calculus, and so forth. All these calculi can be described functions, that is, exp and ( ∈ R ) generators. simultaneously within the framework of a general theory. The rest of this paper is organized as follows: in Sec- They decided to use the adjective non-Newtonian to indicate tion 2, we give some required definitions and consequences any calculi other than the classical calculus. Every property in related with the -arithmetic and -arithmetic. Based on classical calculus has an analogue in non-Newtonian calculus two arbitrarily selected generators and ,wegivesome whichisamethodologythatallowsonetohaveadifferent basic definitions with respect to the ∗-arithmetic. We also look at problems which can be investigated via calculus. In report the most relevant and recent literature in this section. 2 International Journal of Analysis In Section 3, first the definitions of non-Newtonian means arithmetic are special cases for =2and =−1,respectively. −1 are given which will be used for non-Newtonian convexity. In The function : R → R ⊆ R and its inverse are defined this section, the forms of weighted means are presented and as follows ( ∈ R \{0}): an illustrative table is given. In Section 4, the generalized non- Newtonian convex function is defined on the interval and 1/ { ,>0 some types of convex function are obtained by using different { () = 0, = 0 generators. In the final section of the paper, we assert the { notion of multiplicative Lipschitz condition on the closed { − (−)1/ ,<0, interval [, ] ⊂ (0,. ∞) { (4) {, >0 { 2. Preliminary, Background, and Notation −1 { () = {0, =0 { Arithmetic is any system that satisfies the whole of the { ordered field axioms whose domain is a subset of R.There {− (−) , <0. are infinitely many types of arithmetic, all of which are isomorphic, that is, structurally equivalent. It is to be noted that -calculus is reduced to the classical A generator is a one-to-one function whose domain is calculus for =1. Additionally it is concluded that the - R andwhoserangeisasubsetR of R where R = {() : summation can be given as follows: ∈R}. Each generator generates exactly one arithmetic, and conversely each arithmetic is generated by exactly one ∑ ={∑−1 ( )} generator. If (),forall = ∈R,theidentity function’s =1 inverse is itself. In the special cases =and =exp, =1 (5) generates the classical and geometric arithmetic, respectively. −1 −1 + ={ (1)+⋅⋅⋅+ ()} ∀ ∈ R . By -arithmetic, we mean the arithmetic whose domain is R and whose operations are defined as follows: for , ∈ R Definition 1 (see [15]). Let =(,) be an -metric space. and any generator , Thenthebasicnotionscanbedefinedasfollows: −1 −1 -addition +={̇ () + ()} , (a) A sequence =() is a function from the set N into −1 −1 the set R.The-real number denotes the value of -subtraction −={̇ () − ()} , the function at ∈N and is called the th term of the −1 −1 sequence. -multiplication ×={̇ () × ()} , (1) (b) A sequence () in =(,) is said to be - ̇ −1 −1 ̇ ̇ -division /={ () ÷ ()} , convergent if, for every given > 0 (∈R), there exist an 0 =0() ∈ N and ∈such that ̇ −1 −1 ̇ ̇ -order <⇐⇒ () < () . (,)= | −| <for all >0 and is denoted by lim→∞ =or →,as→∞. As a generator, we choose exp function acting from R into the set Rexp =(0,∞)as follows: (c) A sequence () in =(,) is said to be -Cauchy ̇ ̇ if for every > 0 there is an 0 =0() ∈ N such that :R → R ̇ exp (,) <for all , 0 > . (2) → = () = . Throughout this paper, we define the th -exponent (1/) + and th -root of ∈R by It is obvious that -arithmetic reduces to the geometric arith- metic as follows: 2 −1 −1 =×={̇ () × ()} {ln +ln } geometric addition +=̇ =⋅, −1 2 {ln −ln } ={[ ()] }, geometric subtraction −=̇ =÷, 3 2 {ln ln } ln = ×̇ geometric multiplication ×=̇ = (3) ={−1 { [−1 () ×−1 ()]} × −1 ()} =ln , (6) −1 3 ̇ {ln / ln } 1/ ln ={[ ()] }, geometric division /= = , <⇐⇒̇ () < () . geometric order ln ln . Following Grossman and Katz [10] we give the infinitely (−1) −1 = ×={[̇ ()] }, many -arithmetics, of which the quadratic and harmonic International Journal of Analysis 3 (1/2) and √= =provided there exists an ∈R such -arithmetic can readily be transformed into a statement in 2 that =. -arithmetic. Definition 3 (see [10]). The following statements are valid: 2.1. ∗-Arithmetic. Suppose that and are two arbitrarily selected generators and (“star-”) also is the ordered pair of arithmetics ( -arithmetic and -arithmetic). The sets ∗ ∗ ̈ ̈ ̇ ̇ (i) The -points 1, 2,and 3 are -collinear provided (R, +,̈ −,̈ ×,̈ /, <) and (R, +,̇ −,̇ ×,̇ /, <) are complete ordered that at least one of the following holds: fields and (ℎ)-generator generates (ℎ)- arithmetic, respectively. Definitions given for -arithmetic are also valid for -arithmetic. Also -arithmetic is used for ∗ ( ,) +̈ ∗ ( ,)=∗ ( ,), arguments and -arithmeticisusedforvalues;inparticu- 2 1 1 3 2 3 lar, changes in arguments and values are measured by - ∗ ( ,) +̈ ∗ ( ,)=∗ ( ,), differences and -differences, respectively. 1 2 2 3 1 3 (10) ∈(R , +,̇ −,̇ ×,̇ /,̇ <)̇ ∈(R , +,̈ −,̈ ×,̈ /,̈ <)̈ ∗ ∗ ∗ Let and be (1,3) +̈ (3,2)= (1,2). arbitrarily chosen elements from corresponding arithmetic. Then the ordered pair (, ) is called a ∗-point and the set of all ∗-points is called the set of ∗-complex numbers and is ∗ (ii) A ∗-line is a set of at least two distinct points such denoted by C ;thatis, that, for any distinct points 1 and 2 in ,apoint3 is in if and only if 1, 2,and3 are ∗-collinear. When C∗ fl {∗ =(,)|∈R ,∈R }. (7) ==,the∗-lines are the straight lines in two- dimensional Euclidean space. Definition 2 (see [17]). (a) The ∗-limit of a function at an element in R is, if it exists, the unique number in R such that (iii) The ∗-slope of a ∗-line through the points (1,1) and (2,2) is given by ∗ () =⇐⇒ →lim ̈ ̇ ∀ >̈ 0, ∃ >̇ 0∋ () −̈ <̈ (8) ∗ ̈ =(2 −̈ 1) /(2 −̇ 1) ̇ ̇ −1 −1 ∀, ∈ R, − <, ( )− ( ) (11) ={ 2 1 }, ( ≠ ), −1 −1 1 2 ∗ (2)− (1) for ∈R,andiswrittenas lim→(). = Afunction is ∗-continuous at a point in R if and only ∗ if is an argument of and lim→() = ().When , ∈ R , ∈ R and are the identity function ,theconceptsof∗-limit for 1 2 and 1 2 . and ∗-continuity are reduced to those of classical limit and If the following ∗-limit in (12) exists, we denote it by classical continuity. ∗ (),callitthe∗-derivative of at ,andsaythat is ∗- (b) The isomorphism from -arithmetic to -arithmetic is the unique function (iota) which has the following three differentiable at (see [19]): properties: (i) is one to one. ∗ ( () −̈ ()) /(̈ −)̇ lim→ (ii) is from R to R. −1 { ()}−−1 { ()} , V ∈ R = lim{ } (iii) For any numbers , → −1 () −−1 () −1 { ()}−−1 { ()} (12) (+̇ V)=() +̈ (V) ; = lim{ → − (−̇ V)=() −̈ (V) ; −1 (9) − {( ∘) ()} (×̇ V)=() ×̈ (V) ; ⋅ }={ } . −1 () −−1 () (−1) () { } (/̇ V)=() /̈ (V) . −1 3. Non-Newtonian (Weighted) Means It turns out that () = { ()} for every in R and that ()̇ = ̈ for every -integer ̇. Since, for example, Definition 4 (-arithmetic mean).
Recommended publications
  • Data Analysis in Astronomy and Physics
    Data Analysis in Astronomy and Physics Lecture 3: Statistics 2 Lecture_Note_3_Statistics_new.nb M. Röllig - SS19 Lecture_Note_3_Statistics_new.nb 3 Statistics Statistics are designed to summarize, reduce or describe data. A statistic is a function of the data alone! Example statistics for a set of data X1,X 2, … are: average, the maximum value, average of the squares,... Statistics are combinations of finite amounts of data. Example summarizing examples of statistics: location and scatter. 4 Lecture_Note_3_Statistics_new.nb Location Average (Arithmetic Mean) Out[ ]//TraditionalForm= N ∑Xi i=1 X N Example: Mean[{1, 2, 3, 4, 5, 6, 7}] Mean[{1, 2, 3, 4, 5, 6, 7, 8}] Mean[{1, 2, 3, 4, 5, 6, 7, 100}] 4 9 2 16 Lecture_Note_3_Statistics_new.nb 5 Location Weighted Average (Weighted Arithmetic Mean) 1 "N" In[36]:= equationXw ⩵ wi Xi "N" = ∑ wi i 1 i=1 Out[36]//TraditionalForm= N ∑wi Xi i=1 Xw N ∑wi i=1 1 1 1 1 In case of equal weights wi = w : Xw = ∑wi Xi = ∑wXi = w ∑Xi = ∑Xi = X ∑wi ∑w w N 1 N Example: data: xi, weights :w i = xi In[46]:= x1={1., 2, 3, 4, 5, 6, 7}; w1=1/ x1; x2={1., 2, 3, 4, 5, 6, 7, 8}; w2=1 x2; x3={1., 2, 3, 4, 5, 6, 7, 100}; w3=1 x3; In[49]:= x1.w1 Total[w1] x2.w2 Total[w2] x3.w3 Total[w3] Out[49]= 2.69972 Out[50]= 2.9435 Out[51]= 3.07355 6 Lecture_Note_3_Statistics_new.nb Location Median Arrange Xi according to size and renumber.
    [Show full text]
  • The Generalized Likelihood Uncertainty Estimation Methodology
    CHAPTER 4 The Generalized Likelihood Uncertainty Estimation methodology Calibration and uncertainty estimation based upon a statistical framework is aimed at finding an optimal set of models, parameters and variables capable of simulating a given system. There are many possible sources of mismatch between observed and simulated state variables (see section 3.2). Some of the sources of uncertainty originate from physical randomness, and others from uncertain knowledge put into the system. The uncertainties originating from physical randomness may be treated within a statistical framework, whereas alternative methods may be needed to account for uncertainties originating from the interpretation of incomplete and perhaps ambiguous data sets. The GLUE methodology (Beven and Binley 1992) rejects the idea of one single optimal solution and adopts the concept of equifinality of models, parameters and variables (Beven and Binley 1992; Beven 1993). Equifinality originates from the imperfect knowledge of the system under consideration, and many sets of models, parameters and variables may therefore be considered equal or almost equal simulators of the system. Using the GLUE analysis, the prior set of mod- els, parameters and variables is divided into a set of non-acceptable solutions and a set of acceptable solutions. The GLUE methodology deals with the variable degree of membership of the sets. The degree of membership is determined by assessing the extent to which solutions fit the model, which in turn is determined 53 CHAPTER 4. THE GENERALIZED LIKELIHOOD UNCERTAINTY ESTIMATION METHODOLOGY by subjective likelihood functions. By abandoning the statistical framework we also abandon the traditional definition of uncertainty and in general will have to accept that to some extent uncertainty is a matter of subjective and individual interpretation by the hydrologist.
    [Show full text]
  • Lecture 3: Measure of Central Tendency
    Lecture 3: Measure of Central Tendency Donglei Du ([email protected]) Faculty of Business Administration, University of New Brunswick, NB Canada Fredericton E3B 9Y2 Donglei Du (UNB) ADM 2623: Business Statistics 1 / 53 Table of contents 1 Measure of central tendency: location parameter Introduction Arithmetic Mean Weighted Mean (WM) Median Mode Geometric Mean Mean for grouped data The Median for Grouped Data The Mode for Grouped Data 2 Dicussion: How to lie with averges? Or how to defend yourselves from those lying with averages? Donglei Du (UNB) ADM 2623: Business Statistics 2 / 53 Section 1 Measure of central tendency: location parameter Donglei Du (UNB) ADM 2623: Business Statistics 3 / 53 Subsection 1 Introduction Donglei Du (UNB) ADM 2623: Business Statistics 4 / 53 Introduction Characterize the average or typical behavior of the data. There are many types of central tendency measures: Arithmetic mean Weighted arithmetic mean Geometric mean Median Mode Donglei Du (UNB) ADM 2623: Business Statistics 5 / 53 Subsection 2 Arithmetic Mean Donglei Du (UNB) ADM 2623: Business Statistics 6 / 53 Arithmetic Mean The Arithmetic Mean of a set of n numbers x + ::: + x AM = 1 n n Arithmetic Mean for population and sample N P xi µ = i=1 N n P xi x¯ = i=1 n Donglei Du (UNB) ADM 2623: Business Statistics 7 / 53 Example Example: A sample of five executives received the following bonuses last year ($000): 14.0 15.0 17.0 16.0 15.0 Problem: Determine the average bonus given last year. Solution: 14 + 15 + 17 + 16 + 15 77 x¯ = = = 15:4: 5 5 Donglei Du (UNB) ADM 2623: Business Statistics 8 / 53 Example Example: the weight example (weight.csv) The R code: weight <- read.csv("weight.csv") sec_01A<-weight$Weight.01A.2013Fall # Mean mean(sec_01A) ## [1] 155.8548 Donglei Du (UNB) ADM 2623: Business Statistics 9 / 53 Will Rogers phenomenon Consider two sets of IQ scores of famous people.
    [Show full text]
  • Experimental and Simulation Studies of the Shape and Motion of an Air Bubble Contained in a Highly Viscous Liquid Flowing Through an Orifice Constriction
    Accepted Manuscript Experimental and simulation studies of the shape and motion of an air bubble contained in a highly viscous liquid flowing through an orifice constriction B. Hallmark, C.-H. Chen, J.F. Davidson PII: S0009-2509(19)30414-2 DOI: https://doi.org/10.1016/j.ces.2019.04.043 Reference: CES 14954 To appear in: Chemical Engineering Science Received Date: 13 February 2019 Revised Date: 25 April 2019 Accepted Date: 27 April 2019 Please cite this article as: B. Hallmark, C.-H. Chen, J.F. Davidson, Experimental and simulation studies of the shape and motion of an air bubble contained in a highly viscous liquid flowing through an orifice constriction, Chemical Engineering Science (2019), doi: https://doi.org/10.1016/j.ces.2019.04.043 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Experimental and simulation studies of the shape and motion of an air bubble contained in a highly viscous liquid flowing through an orifice constriction. B. Hallmark, C.-H. Chen, J.F. Davidson Department of Chemical Engineering and Biotechnology, Philippa Fawcett Drive, Cambridge. CB3 0AS. UK Abstract This paper reports an experimental and computational study on the shape and motion of an air bubble, contained in a highly viscous Newtonian liquid, as it passes through a rectangular channel having a constriction orifice.
    [Show full text]
  • Prevalence of Elevated Blood Pressure Levels In
    PREVALENCE OF ELEVATED BLOOD PRESSURE LEVELS IN OVERWEIGHT AND OBESE YOUNG POPULATION (2 – 19 YEARS) IN THE UNITED STATES BETWEEN 2011 AND 2012 By: Lawrence O. Agyekum A Dissertation Submitted to Faculty of the School of Health Related Professions, Rutgers, The State University of New Jersey in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Biomedical Informatics Department of Health Informatics Spring 2015 © 2015 Lawrence Agyekum All Rights Reserved Final Dissertation Approval Form PREVALENCE OF ELEVATED BLOOD PRESSURE LEVELS IN OVERWEIGHT AND OBESE YOUNG POPULATION (2 – 19 YEARS) IN THE UNITED STATES BETWEEN 2011 AND 2012 BY Lawrence O. Agyekum Dissertation Committee: Syed Haque, Ph.D., Committee Chair Frederick Coffman Ph.D., Committee Member Shankar Srinivasan, Ph.D., Member Approved by the Dissertation Committee: _____________________________________ Date: _______________ _____________________________________ Date: _______________ ____________________________________ Date: _______________ ii ABSTRACT Prevalence of Elevated Blood Pressure Levels in Overweight and Obese Young Population (2 -19 Years) in the United States between 2011-2012 By Lawrence Ofori Agyekum Several studies have reported hypertension prevalence in children and adolescents in the United States (US) using regional or local population-based samples but few have reported national prevalence. The present study estimates national hypertension prevalence in US children and adolescents for 2011-2012. A convenient sample size of 4,196 (population aged ≤ 19) representing 43% of 9,756 (total survey respondents) was selected and stratified by age groups; “Children” and “Adolescents” using the 2007 Joint National Committee recommended definitions. Next, hypertension distribution was explained by gender, race, age, body weight, standing height and blood serum total cholesterol.
    [Show full text]
  • Arxiv:1111.2491V1 [Physics.Data-An] 10 Nov 2011 .Simulation 2
    Optimized differential energy loss estimation for tracker detectors Ferenc Sikl´er KFKI Research Institute for Particle and Nuclear Physics, Budapest, Hungary CERN, Geneva, Switzerland S´andor Szeles E¨otv¨os University, Budapest, Hungary Abstract The estimation of differential energy loss for charged particles in tracker detectors is studied. The robust truncated mean method can be generalized to the linear combination of the energy deposit measurements. The optimized weights in case of arithmetic and geometric means are obtained using a detailed simulation. The results show better particle separation power for both semiconductor and gaseous detectors. Key words: Energy loss, Silicon, TPC PACS: 29.40.Gx, 29.85.-c, 34.50.Bw 1. Introduction The identification of charged particles is crucial in several fields of particle and nuclear physics: particle spectra, correlations, selection of daughters of resonance decays and for reducing the background of rare physics processes [1, 2]. Tracker detectors, both semiconductor and gaseous, can be employed for particle identification, or yield extraction in the statistical sense, by proper use of energy deposit measurements along the trajectory of the particle. While for gaseous detectors a wide momentum range is available, in semiconductors there is practically no logarithmic rise of differential energy loss (dE/dx) at high momentum, thus only momenta below the the minimum ionization region are accessible. In this work two representative materials, silicon and neon are studied. Energy loss of charged particles inside matter is a complicated process. For detailed theoretical model and several comparisons to measured data see Refs. [3, 4]. While the energy lost and deposited differ, they will be used interchangeably in the discussion.
    [Show full text]
  • Estimation of a Proportion in Survey Sampling Using the Logratio Approach
    Noname manuscript No. (will be inserted by the editor) Estimation of a proportion in survey sampling using the logratio approach Karel Hron · Matthias Templ · Peter Filzmoser Received: date / Accepted: date Abstract The estimation of a mean of a proportion is a frequent task in statistical survey analysis, and often such ratios are estimated from compositions such as income components, wage components, tax components, etc. In practice, the weighted arithmetic mean is reg- ularly used to estimate the center of the data. However, this estimator is not appropriate if the ratios are estimated from compositions, because the sample space of compositional data is the simplex and not the usual Euclidean space. We demonstrate that the weighted geometric mean is useful for this purpose. Even for different sampling designs, the weighted geometric mean shows excellent behavior. Keywords Survey sampling, Proportions, Compositional data, Logratio analysis K. Hron Department of Mathematical Analysis and Applications of Mathematics, Faculty of Science, Palack´yUni- versity, 17. listopadu 12, 771 46 Olomouc, Czech Republic Department of Geoinformatics, Faculty of Science, Palack´yUniversity, tˇr.Svobody 26, 771 46 Olomouc, Czech Republic Tel.: +420585634605 Fax: +420585634002 E-mail: [email protected] M. Templ Department of Statistics and Probability Theory, Vienna University of Technology, Wiedner Hauptstr. 7, A-1040 Vienna, Austria Tel.: +4315880110715 E-mail: [email protected] P. Filzmoser Department of Statistics and Probability Theory, Vienna University of Technology, Wiedner Hauptstr. 8-10, A-1040 Vienna, Austria Tel.: +4315880110733 E-mail: p.fi[email protected] 2 Karel Hron et al. 1 Introduction Many surveys are concerned with the problem of estimating a mean of proportions.
    [Show full text]
  • Simultaneous Penalized M-Estimation of Covariance Matrices Using
    Simultaneous penalized M-estimation of covariance matrices using geodesically convex optimization Esa Ollilaa,∗, Ilya Soloveychikb, David E. Tylerc,1, Ami Wieselb,2 aAalto University, Finland bThe Hebrew University of Jerusalem, Israel cRutgers – The State University of New Jersey, USA Abstract A common assumption when sampling p-dimensional observations from K dis- tinct group is the equality of the covariance matrices. In this paper, we propose two penalized M-estimation approaches for the estimation of the covariance or scatter matrices under the broader assumption that they may simply be close to each other, and hence roughly deviate from some positive definite “center”. The first approach begins by generating a pooled M-estimator of scatter based on all the data, followed by a penalised M-estimator of scatter for each group, with the penalty term chosen so that the individual scatter matrices are shrunk towards the pooled scatter matrix. In the second approach, we minimize the sum of the individual group M-estimation cost functions together with an additive joint penalty term which enforces some similarity between the individual scatter es- timators, i.e. shrinkage towards a mutual center. In both approaches, we utilize the concept of geodesic convexity to prove the existence and uniqueness of the penalized solution under general conditions. We consider three specific penalty functions based on the Euclidean, the Riemannian, and the Kullback-Leibler distances. In the second approach, the distance based penalties are shown to lead to estimators of the mutual center that are related to the arithmetic, the Riemannian and the harmonic means of positive definite matrices, respectively.
    [Show full text]
  • Weighted Power Mean Copulas: Theory and Application
    A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Klein, Ingo; Fischer, Matthias J.; Pleier, Thomas Working Paper Weighted power mean copulas: Theory and application IWQW Discussion Papers, No. 01/2011 Provided in Cooperation with: Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics Suggested Citation: Klein, Ingo; Fischer, Matthias J.; Pleier, Thomas (2011) : Weighted power mean copulas: Theory and application, IWQW Discussion Papers, No. 01/2011, Friedrich- Alexander-Universität Erlangen-Nürnberg, Institut für Wirtschaftspolitik und Quantitative Wirtschaftsforschung (IWQW), Nürnberg This Version is available at: http://hdl.handle.net/10419/50916 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have
    [Show full text]
  • Weighted Mean Mathematics
    underground Weighted mean mathematics A weighted mean or weighted average of a set of data (or numbers) is a mean in which the different pieces of data are given different weights. “Weight” here has the sense of “importance”, but can also be thought of as literal “weight”. The simplest case is where each piece of data 푥푖 occurs with a corresponding frequency 푓푖, then we can think of the data as 푥1, … , 푥1, 푥2, … , 푥2, … , 푥푛, … , 푥푛 with 푓1 copies of 푥1, and so on. Thus the arithmetic mean of these would be 푥 + ⋯ + 푥 + 푥 + ⋯ + 푥 + ⋯ + 푥 + ⋯ + 푥 ∑ 푓 푥 ̄푥= 1 1 2 2 푛 푛 = 푖 푖 . 푓1 + 푓2 + ⋯ + 푓푛 ∑ 푓푖 More generally, if we weight the data 푥푖 with weight 푤푖, then the weighted arithmetic mean ∑ 푤푖푥푖 is . ∑ 푤푖 This is the same formula as that for the centre of mass of a set of objects (where weight 푤푖 ∑ 푚푖푥푖 is replaced by mass 푚푖): ̄푥= , that is, the 푥-coordinate of the centre of mass is the ∑ 푚푖 mean of the 푥-coordinates of the objects, where the objects are weighted by their mass. It is also the same formula as that for the expectation of a discrete random variable 푋. If 푋 can take the values 푥1, …, 푥푛 with probabilities 푝1, …, 푝푛 respectively, where 푝1 + 푝2 + ⋯ + 푝푛 = 1, then the expectation (mean) of 푋 is given by 퐸(푋) = 푝1푥1 + 푝2푥2 + ⋯ + 푝푛푥푛 = ∑ 푝푖푥푖. (We don’t need to explicitly divide by 푝1 + 푝2 + ⋯ + 푝푛 as this equals 1.) Similarly, the weighted geometric mean of a set of data would be 푤1 푤2 푤푛 1/(푤1+푤2+⋯+푤푛) (푥1 푥2 … 푥푛 ) .
    [Show full text]
  • 1 Social Studies 201 September 27, 2004 Mean for Grouped Data
    1 Social Studies 201 September 27, 2004 Mean for grouped data { text, section 5.4.2, pp. 188-200. For grouped data, where data are grouped into categories or intervals and presented as diagrams or tables, the de¯nition of the mean is unchanged, but the method of obtaining it di®ers from that used for ungrouped data. The mean is the sum of values of the variable divided by the number of cases, but it is necessary to make sure that the sum is properly obtained. In order to obtain the sum or total value, each individual value must be multiplied by the number, or percentage, of cases that take on that value, and these products are added together. The mean is then obtained by dividing this sum by the number, or percentage, of cases. A formal de¯nition and examples follow { again, if you are unfamiliar with the notation below, please read the text, pp. 97-100 and pp. 190-92 on notation. De¯nition of the mean for grouped data. For a variable X, taking on k di®erent values X1;X2;X3; :::Xk, with respective frequencies f1; f2; f3; :::fk, the mean of X is f X + f X + f X + ::: + f X X¹ = 1 1 2 2 3 3 k k n where n = f1 + f2 + f3 + ::: + fk: Using summation notation, §(fX) X¹ = n where n = §f. Notes on calculating the mean with grouped data 1. Products. The ¯rst step in calculating the mean is to multiply each value of the variable X by the frequency of occurrence of that value.
    [Show full text]
  • On a Variational Definition for the Jensen-Shannon Symmetrization Of
    entropy Article On a Variational Definition for the Jensen-Shannon Symmetrization of Distances Based on the Information Radius Frank Nielsen Sony Computer Science Laboratories, Tokyo 141-0022, Japan; [email protected] Abstract: We generalize the Jensen-Shannon divergence and the Jensen-Shannon diversity index by considering a variational definition with respect to a generic mean, thereby extending the notion of Sibson’s information radius. The variational definition applies to any arbitrary distance and yields a new way to define a Jensen-Shannon symmetrization of distances. When the variational optimization is further constrained to belong to prescribed families of probability measures, we get relative Jensen-Shannon divergences and their equivalent Jensen-Shannon symmetrizations of distances that generalize the concept of information projections. Finally, we touch upon applications of these variational Jensen-Shannon divergences and diversity indices to clustering and quantization tasks of probability measures, including statistical mixtures. Keywords: Jensen-Shannon divergence; diversity index; Rényi entropy; information radius; information projection; exponential family; Bregman divergence; Fenchel–Young divergence; Bregman information; q-exponential family; q-divergence; Bhattacharyya distance; centroid; clustering 1. Introduction: Background and Motivations Citation: Nielsen, F. On a Variational Definition for the Jensen-Shannon The goal of the author is to methodologically contribute to an extension of the Sib- Symmetrization of Distances Based son’s information radius [1] and also concentrate on analysis of the specified families of on the Information Radius. Entropy distributions called exponential families [2]. 2021, 23, 464. https://doi.org/ Let (X , F) denote a measurable space [3] with sample space X and s-algebra F on 10.3390/e23040464 the set X .
    [Show full text]