Greek Means and the Arithmetic-Geometric Mean
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Gpindex: Generalized Price and Quantity Indexes
Package ‘gpindex’ August 4, 2021 Title Generalized Price and Quantity Indexes Version 0.3.4 Description A small package for calculating lots of different price indexes, and by extension quan- tity indexes. Provides tools to build and work with any type of bilateral generalized-mean in- dex (of which most price indexes are), along with a few important indexes that don't be- long to the generalized-mean family. Implements and extends many of the meth- ods in Balk (2008, ISBN:978-1-107-40496-0) and ILO, IMF, OECD, Euro- stat, UN, and World Bank (2020, ISBN:978-1-51354-298-0) for bilateral price indexes. Depends R (>= 3.5) Imports stats, utils License MIT + file LICENSE Encoding UTF-8 URL https://github.com/marberts/gpindex LazyData true Collate 'helper_functions.R' 'means.R' 'weights.R' 'price_indexes.R' 'operators.R' 'utilities.R' NeedsCompilation no Author Steve Martin [aut, cre, cph] Maintainer Steve Martin <[email protected]> Repository CRAN Date/Publication 2021-08-04 06:10:06 UTC R topics documented: gpindex-package . .2 contributions . .3 generalized_mean . .8 lehmer_mean . 12 logarithmic_means . 15 nested_mean . 18 offset_prices . 20 1 2 gpindex-package operators . 22 outliers . 23 price_data . 25 price_index . 26 transform_weights . 33 Index 37 gpindex-package Generalized Price and Quantity Indexes Description A small package for calculating lots of different price indexes, and by extension quantity indexes. Provides tools to build and work with any type of bilateral generalized-mean index (of which most price indexes are), along with a few important indexes that don’t belong to the generalized-mean family. Implements and extends many of the methods in Balk (2008, ISBN:978-1-107-40496-0) and ILO, IMF, OECD, Eurostat, UN, and World Bank (2020, ISBN:978-1-51354-298-0) for bilateral price indexes. -
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. -
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. -
Inequalities Among Complementary Means of Heron Mean and Classical Means
Advances and Applications in Mathematical Sciences Volume 20, Issue 7, May 2021, Pages 1249-1258 © 2021 Mili Publications INEQUALITIES AMONG COMPLEMENTARY MEANS OF HERON MEAN AND CLASSICAL MEANS AMBIKA M. HEJIB, K. M. NAGARAJA, R. SAMPATHKUMAR and B. S. VENKATARAMANA Department of Mathematics R N S Institute of Technology Uttarahalli-Kengeri Main Road R Rnagar post, Bengaluru-98, Karnataka, India E-mail: [email protected] [email protected] Department of Mathematics J.S.S. Academy of Technical Education Uttarahalli-Kengeri Main Road Bengaluru-60, Karnataka, India E-mail: [email protected] Department of Mathematics K S Institute of Technology Kannakapura Main Road Bengaluru - 560 109, Karnataka, India E-mail: [email protected] Abstract In this paper, the complementary means of arithmetic, geometric, harmonic and contra harmonic with respect to Heron mean are defined and verified them as means. Further, inequalities among them and classical means are established. I. Introduction In Pythagorean school ten Greek means are defined based on proportion, the following are the familiar means in literature and are given as follows: 2010 Mathematics Subject Classification: Primary 26D10, secondary 26D15. Keywords: Complementary mean, Heron mean, Classical means. Received September 7, 2020; Accepted February 18, 2021 1250 HEJIB, NAGARAJA, SAMPATHKUMAR and VENKATARAMANA u v For two real numbers u and v which are positive, Au , v 1 1 ; 1 1 1 1 2 2 2 2u1v1 u1 v1 Gu1, v1 u1v1 ; Hu1, v1 and Cu1, v1 . These are u1 v1 u1 v1 called Arithmetic, Geometric, Harmonic and Contra harmonic mean respectively. The Hand book of Means and their Inequalities, by Bullen [1], gave the tremendous work on Mathematical means and the corresponding inequalities involving huge number of means. -
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. -
Digital Image Processing Chapter 5: Image Restoration Concept of Image Restoration
Digital Image Processing Chapter 5: Image Restoration Concept of Image Restoration Image restoration is to restore a degraded image back to the original image while image enhancement is to manipulate the image so that it is suitable for a specific application. Degradation model: g(x, y) = f (x, y) ∗h(x, y) +η(x, y) where h(x,y) is a system that causes image distortion and η(x,y) is noise. (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition. Concept of Image Restoration g(x, y) = f (x, y) ∗h(x, y) +η(x, y) We can perform the same operations in the frequency domain, where convolution is replaced by multiplication, and addition remains as addition, because of the linearity of the Fourier transform. G(u,v) = F(u,v)H(u,v) + N(u,v) If we knew the values of H and N we could recover F by writing the above equation as N(u,v) F(u,v) = G(u,v) − H(u,v) However, as we shall see, this approach may not be practical. Even though we may have some statistical information about the noise, we will not know the value of n(x,y) or N(u,v) for all, or even any, values. A s we ll, di vidi ng b y H(i, j) will cause diff ulti es if there are values which are close to, or equal to, zero. Noise WdfiibddiihiilWe may define noise to be any degradation in the image signal, caused by external disturbance. -
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. -
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. -
EEM 463 Introduction to Image Processing Week 5: Filtering in the Frequency Domain
EEM 463 Introduction to Image Processing Week 5: Image Restoration and Reconstruction Fall 2013 Instructor: Hatice Çınar Akakın, Ph.D. [email protected] Anadolu University 12.11.2013 Image Restoration • Image restoration: recover an image that has been degraded by using a prior knowledge of the degradation phenomenon. • Model the degradation and applying the inverse process in order to recover the original image. • The principal goal of restoration techniques is to improve an image in some predefined sense. • Although there are areas of overlap, image enhancement is largely a subjective process, while restoration is for the most part an objective process. 12.11.2013 A Model of the Image Degradation/Restoration Process • The degraded image in the spatial domain: 푔 푥, 푦 = ℎ 푥, 푦 ∗ 푓 푥, 푦 + 휂 푥, 푦 • Frequency domain representation 퐺 푢, 푣 = 퐻 푢, 푣 퐹 푢, 푣 + 푁(푢, 푣) 12.11.2013 Noise Models • The principal sources of noise in digital images arise during image acquisition and/or transmission • Light levels and sensor temperature during acquisition • Lightning or other atmospheric disturbance in wireless network during transmission • White noise: Fourier spectrum this noise is constant • carryover from the physical proporties of White light, which contains nearly all frequencies in the visible spectrum in equal proportions. • With the exception of spatially periodic noise, we assume • Noise is independent of spatial coordinates • Noise is uncorrelated with respect to the image itself 12.11.2013 Gaussian Noise • The pdf of a Gaussian random variable, z, is given by 1 2 푝 푧 = 푒−(푧−푧) /2휎2 2휋휎 where z represents intensity, 푧 is the mean (average) value of z , and σ is its standard deviation. -
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. -
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. -
Computer Vision & Digital Image Processing Outline
Computer Vision & Digital Image Processing Image Restoration and Reconstruction II Electrical & Computer Engineering Dr. D. J. Jackson Lecture 12-1 Outline • Periodic noise • Estimation of noise parameters • Restoration in the presence of noise only –spatial filtering Electrical & Computer Engineering Dr. D. J. Jackson Lecture 12-2 Periodic noise • Periodic noise typically arises from interference during image acquisition • Spatially dependent noise type • Can be effectively reduced via frequency domain filtering Electrical & Computer Engineering Dr. D. J. Jackson Lecture 12-3 Sample periodic images and their spectra 50 50 100 100 150 150 200 200 250 250 50 100 150 200 250 50 100 150 200 250 50 50 100 100 150 150 200 200 250 250 50 100 150 200 250 50 100 150 200 250 Electrical & Computer Engineering Dr. D. J. Jackson Lecture 12-4 Sample periodic images and their spectra 50 50 100 100 150 150 200 200 250 250 50 100 150 200 250 50 100 150 200 250 122 124 126 128 130 132 134 136 105 110 115 120 125 130 135 140 145 150 Electrical & Computer Engineering Dr. D. J. Jackson Lecture 12-5 Estimation of noise parameters • Noise parameters can often be estimated by observing the Fourier spectrum of the image – Periodic noise tends to produce frequency spikes • Parameters of noise PDFs may be known (partially) from sensor specification – Can still estimate them for a particular imaging setup – One method • Capture a set of “flat” images from a known setup (i.e. a uniform gray surface under uniform illumination) • Study characteristics of resulting image(s) to develop an indicator of system noise Electrical & Computer Engineering Dr.