Revenue from the Saints, the Showoffs, and the Predators: Comparisons of Auctions with Price-Preference Values Supplemental Information
|
|
|
- Bartholomew Tate
- 5 years ago
- Views:
Transcription
1 Revenue from the Saints, the Showoffs, and the Predators: Comparisons of Auctions with Price-Preference Values Supplemental Information Timothy C. Salmon Florida State University R. Mark Isaac Florida State University March, 2004 The tables below contain all of the numerical results from the main paper that are either summarized in the figures or described in the text. We have also included results of significance tests between the revenue and efficiencies achieved for every set of parameters run. The tests on the revenue numbers are general t-tests and Wilcoxon rank sum tests with the numbers included representing the p-values of the test. For the efficiency comparisons, the tests are paired t-tests and signed rank sum tests, a paired version of the Wilcoxon test. The numbers also are the p-values of the test. Standard Charity Figure : Revenue comparison assuming uniformly distributed values between second price and first price auctions and between the standard and then baseline symmetric charity cases. Expected Revenue Second Price First Price α,β=0 α,β=.5 α,β=.3 α,β=0 α,β=.5 α,β=.3 α,β=0 α,β=.5 α,β=.3 N=2 N=4 N=6 Support for Figure :
2 Revenue Efficiency α i = (.20) β i = (.77) (.044) α i = (.280) β i = (.254) (.058) α i = (.380) β i = (.209) (.03).3477 (.2395).6028 (.202).755 (.590).4097 (.969).6588 (.770).7562 (.355).485 (.808).7040 (.5).7733 (.263) NA NA NA NA NA NA NA NA NA 2
3 Figure 2: Revenue comparison for the standard and baseline symmetric charity cases assuming normally distributed values Second Price First Price : 0 First Price : Expected Revenue α,β=0 α,β=.5 α,β=.3 α,β=0 α,β=.5 α,β=.3 α,β=0 α,β=.5 α,β=.3 N=2 N=4 N=6 Normal distribution, µ =.5, σ =.5 Support for figure 2: ᾱ = β = α i = β i Revenue Efficiency α i = (.0539) β i = (.0460) (.048) α i = (.0596) β i = (.058) (.0473) α i = (.0849) β i = (.0677) (.0594).483 (.2).5434 (.0898).600 (.079).460 (.0828).5600 (.0850).6030 (.0795).498 (.0729).5780 (.0746).6205 (.0779) (.0055) NA NA.374 NA NA NA NA NA NA 3
4 ᾱ = β =0 Revenue Efficiency α i = (.0546) β i = (.0450) (.047) α i = (.050) β i = (.0443) (.048).462 (.0982).5760 (.0863).69 (.0752).4857 (.0880).5975 (.0796).6342 (.0772) (.0095) (.0095) NA NA NA NA 4
5 2 See and Be Seen Figure 3: Revene comparisons for SBS model assuming uniformly distributed values. Expected Revenue Second Price First Price β={0,.5} β={0,.5} β={0,.5} β={0,.5} β={0,.5} β={0,.5} N=2 N=4 N=6 Support for figure 3: β =0 Revenue Efficiency β i = (.334) α i = (.432) (.275) β i = (.2468) α i = (.3054) (.2886).3525 (.2462).644 (.237).7734 (.763).423 (.279).864 (.3322).084 (.3450) (.0206) (.037) (.0330).94 (.264).970 (.376).93 (.307).9953 (.0206).9889 (.037).9866 (.0330).94 (.264).970 (.376).93 (.307) β =.5β i Revenue Efficiency β i = (.334) α i = (.398) (.224) β i = (.2390) α i = (.32) (.2996).3609 (.2467).6434 (.2096).7835 (.733).429 (.285).8598 (.3463).0897 (.3528) (.0243) (.0297) (.0353).9403 (.256).950 (.400).982 (.290).9937 (.0243).990 (.0297).985 (.0353).9403 (.256).950 (.400).982 (.290) 5
6 β = β i Revenue Efficiency β i = (.35) α i = (.408) (.368) β i = (.2434) α i = (.325) (.2987).3484 (.2533).6557 (.2233).772 (.767).4297 (.282).8504 (.3487).087 (.3593) (.0226) (.0292) (.0346).938 (.366).920 (.35).950 (.339).9945 (.0226).9900 (.0292).9864 (.0346).938 (.366).920 (.35).950 (.339) 6
7 Figure 4: Revenue comparison for the SBS model for normally distributed values. Expected Revenue Second Price First Price : 0 First Price :.5 First Price : β={0,.5} β={0,.5} β={0,.5} β={0,.5} β={0,.5} β={0,.5} N=2 N=4 N=6 Support for figure 4: β =0 Revenue Efficiency β i = (.0403) α i = (.0403) 6.66 (.0389) β i = (.0322) α i = (.0385) (.037).4469 (.323).5908 (.036).6555 (.086).4890 (.439).8542 (.2049).0066 (.897) (.0372).9835 (.038).9862 (.0327) (.30).899 (.246).976 (.009).9872 (.0340).9850 (.0352).9833 (.0368).9050 (.442).9022 (.362).900 (.22) β =.5β i Revenue Efficiency β i = (.0625) α i = (.0505) (.0477) β i = (.07) α i = (.0698) (.0595).4487 (.32).5968 (.0985).6536 (.0892).503 (.484).8580 (.965).9947 (.937) (.0302) (.036) (.0323).8963 (.439).9048 (.248).907 (.43).9896 (.030).983 (.0377).9825 (.0377).8859 (.529).9060 (.305).904 (.28)
8 β = β i Revenue Efficiency β i = (.0765) α i = (.063) (.0564) β i = (.67) α i = (.400) (.265).45 (.363).5854 (.0996).6580 (.0898).4879 (.492).858 (.2087).0000 (.857) (.0322).9859 (.0347) (.0363).8922 (.494).96 (.284).97 (.32).9887 (.036).9865 (.0338).987 (.0392).8953 (.462).952 (.237).950 (.6)
9 3 Raising Rivals Cost Figure 5: Revenue comparisons for RRC model assuming uniformly distributed values. Expected Revenue Standard Second Price : 0 Second Price :.5 Second Price : First Price α={0,.5} α={0,.5} α={0,.5} α={0,.5} α={0,.5} α={0,.5} N=2 N=4 N=6 ᾱ =0 Revenue Efficiency β i = (.44) α i = (.83) (.03) β i = (.04) α i = (.347) (.264).362 (.202).5854 (.725).6903 (.407).359 (.782).567 (.375).6508 (.20) (.0089) (.069) (.0246).990 (.0347) (.0659) (.0776).9894 (.078).9950 (.0287).9924 (.023).9707 (.59).98 (.0559).9757 (.0540) ᾱ =.5α i Revenue Efficiency β i = (.26) α i = (.90) (.074) β i = (.44) α i = (.369) (.255).3477 (.249).5804 (.707).6779 (.409).33 (.674).5443 (.429).6302 (.330) (.0070) (.057) (.0222) (.0306) (.0673).9627 (.0733).9926 (.0573).9959 (.079).9928 (.022).975 (.24).9728 (.0625).9692 (.0646)
10 ᾱ = α i Revenue Efficiency β i = (.63) α i = (.59) (.047) β i = (.) α i = (.287) (.33).3385 (.2036).5879 (.735).6786 (.408).390 (.580).589 (.470).6059 (.427) (.0063) (.053) (.022) (.0379) (.0646).9575 (.0786).9934 (.0553).9939 (.0239).997 (.0236).9763 (.0886).9665 (.0735).9574 (.0798)
11 Figure 6: Revenue comparisons for RRC model assuming normally distributed values. Expected Revenue Standard Second Price First Price : 0 First Price :.5 First Price : α={0,.5} α={0,.5} α={0,.5} α={0,.5} α={0,.5} α={0,.5} N=2 N=4 N=6 ᾱ =0 Revenue Efficiency β i = (.0586) α i = (.05) 6.57 (.0484) β i = (.0683) α i = (.063) (.0586).4055 (.05).5230 (.085).5682 (.0744).4004 (.0760).4963 (.0665).5352 (.0683) (.090) (.0253) (.0249) (.0743) (.0966).9374 (.090).9949 (.03).9947 (.082).995 (.0228).9793 (.0580).9656 (.0657).9564 (.076) ᾱ =.5α i Revenue Efficiency β i = (.053) α i = (.0446) (.0409) β i = (.0464) α i = (.049) (.036).4094 (.0879).4976 (.0652).5398 (.0700).3674 (.0724).47 (.0830).594 (.0832).9948 (.084) (.0246).9904 (.0244) (.0690) (.0830).9478 (.0807).9855 (.0435).9745 (.0566).9573 (.0720).9642 (.0738).949 (.0840).944 (.088)
12 ᾱ = α i Revenue Efficiency β i = (.0470) α i = (.0372) (.0356) β i = (.0326) α i = (.0273) (.0248).4095 (.0996).56 (.0862).566 (.0763).352 (.0754).4672 (.0872).5229 (.0853).9936 (.027).9900 (.0268).996 (.0226).967 (.0682).9425 (.0879).9445 (.087).993 (.0242).9920 (.0230).990 (.0240).9586 (.0796).9476 (.0853).9376 (.0922)
Two Populations Hypothesis Testing
Two Populations Hypothesis Testing Two Proportions (Large Independent Samples) Two samples are said to be independent if the data from the first sample is not connected to the data from the second sample.
σ e, which will be large when prediction errors are Linear regression model
Linear regression model we assume that two quantitative variables, x and y, are linearly related; that is, the population of (x, y) pairs are related by an ideal population regression line y = α + βx +
The Normal Distribution
The Normal Distribution The normal distribution plays a central role in probability theory and in statistics. It is often used as a model for the distribution of continuous random variables. Like all models,
Statistics and Their Distributions
Statistics and Their Distributions Deriving Sampling Distributions Example A certain system consists of two identical components. The life time of each component is supposed to have an expentional distribution
Tests for One Variance
Chapter 65 Introduction Occasionally, researchers are interested in the estimation of the variance (or standard deviation) rather than the mean. This module calculates the sample size and performs power
Determining Sample Size. Slide 1 ˆ ˆ. p q n E = z α / 2. (solve for n by algebra) n = E 2
Determining Sample Size Slide 1 E = z α / 2 ˆ ˆ p q n (solve for n by algebra) n = ( zα α / 2) 2 p ˆ qˆ E 2 Sample Size for Estimating Proportion p When an estimate of ˆp is known: Slide 2 n = ˆ ˆ ( )
Index Models and APT
Index Models and APT (Text reference: Chapter 8) Index models Parameter estimation Multifactor models Arbitrage Single factor APT Multifactor APT Index models predate CAPM, originally proposed as a simplification
What was in the last lecture?
What was in the last lecture? Normal distribution A continuous rv with bell-shaped density curve The pdf is given by f(x) = 1 2πσ e (x µ)2 2σ 2, < x < If X N(µ, σ 2 ), E(X) = µ and V (X) = σ 2 Standard
EC476 Contracts and Organizations, Part III: Lecture 3
EC476 Contracts and Organizations, Part III: Lecture 3 Leonardo Felli 32L.G.06 26 January 2015 Failure of the Coase Theorem Recall that the Coase Theorem implies that two parties, when faced with a potential
On Sensitivity Value of Pair-Matched Observational Studies
On Sensitivity Value of Pair-Matched Observational Studies Qingyuan Zhao Department of Statistics, University of Pennsylvania August 2nd, JSM 2017 Manuscript and slides are available at http://www-stat.wharton.upenn.edu/~qyzhao/.
Consumers may be incompletely informed about states. Difference between imperfect information and asymmetric information
Chapter 10 Asymmetric information and agency Complete information versus incomplete information Consumers may be incompletely informed about states Difference between imperfect information and asymmetric
Financial Econometrics
Financial Econometrics Value at Risk Gerald P. Dwyer Trinity College, Dublin January 2016 Outline 1 Value at Risk Introduction VaR RiskMetrics TM Summary Risk What do we mean by risk? Dictionary: possibility
TWO μs OR MEDIANS: COMPARISONS. Business Statistics
TWO μs OR MEDIANS: COMPARISONS Business Statistics CONTENTS Comparing two samples Comparing two unrelated samples Comparing the means of two unrelated samples Comparing the medians of two unrelated samples
All Equilibrium Revenues in Buy Price Auctions
All Equilibrium Revenues in Buy Price Auctions Yusuke Inami Graduate School of Economics, Kyoto University This version: January 009 Abstract This note considers second-price, sealed-bid auctions with
Order Making Fiscal Year 2018 Annual Adjustments to Transaction Fee Rates
This document is scheduled to be published in the Federal Register on 04/20/2018 and available online at https://federalregister.gov/d/2018-08339, and on FDsys.gov 8011-01p SECURITIES AND EXCHANGE COMMISSION
Assessing Model Stability Using Recursive Estimation and Recursive Residuals
Assessing Model Stability Using Recursive Estimation and Recursive Residuals Our forecasting procedure cannot be expected to produce good forecasts if the forecasting model that we constructed was stable
Notes for Section: Week 4
Economics 160 Professor Steven Tadelis Stanford University Spring Quarter, 2004 Notes for Section: Week 4 Notes prepared by Paul Riskind (pnr@stanford.edu). spot errors or have questions about these notes.
Unit 2 Statistics of One Variable
Unit 2 Statistics of One Variable Day 6 Summarizing Quantitative Data Summarizing Quantitative Data We have discussed how to display quantitative data in a histogram It is useful to be able to describe
Program Studi Akuntansi, Fakultas Ekonomi, Universitas Atma Jaya. Yogyakarta. Jalan Babarsari 43-44, Yogyakarta
THE ADOPTION OF IFRS AND EARNINGS QUALITY OF INDONESIA REAL ESTATE, PROPERTY AND BUILDING CONSTRUCTION COMPANIES Written by: A Vendix Christo Dewa S Jenjang Sri Lestari Program Studi Akuntansi, Fakultas
In a binomial experiment of n trials, where p = probability of success and q = probability of failure. mean variance standard deviation
Name In a binomial experiment of n trials, where p = probability of success and q = probability of failure mean variance standard deviation µ = n p σ = n p q σ = n p q Notation X ~ B(n, p) The probability
Two hours. To be supplied by the Examinations Office: Mathematical Formula Tables and Statistical Tables THE UNIVERSITY OF MANCHESTER
Two hours MATH20802 To be supplied by the Examinations Office: Mathematical Formula Tables and Statistical Tables THE UNIVERSITY OF MANCHESTER STATISTICAL METHODS Answer any FOUR of the SIX questions.
Power in Mixed Effects
Power in Mixed Effects Gary W. Oehlert School of Statistics University of Minnesota December 1, 2014 Power is an important aspect of designing an experiment; we now return to power in mixed effects. We
Previously, when making inferences about the population mean, μ, we were assuming the following simple conditions:
Chapter 17 Inference about a Population Mean Conditions for inference Previously, when making inferences about the population mean, μ, we were assuming the following simple conditions: (1) Our data (observations)
Statistics Class 15 3/21/2012
Statistics Class 15 3/21/2012 Quiz 1. Cans of regular Pepsi are labeled to indicate that they contain 12 oz. Data Set 17 in Appendix B lists measured amounts for a sample of Pepsi cans. The same statistics
CHAPTER 8. Confidence Interval Estimation Point and Interval Estimates
CHAPTER 8. Confidence Interval Estimation Point and Interval Estimates A point estimate is a single number, a confidence interval provides additional information about the variability of the estimate Lower
5.3 Interval Estimation
5.3 Interval Estimation Ulrich Hoensch Wednesday, March 13, 2013 Confidence Intervals Definition Let θ be an (unknown) population parameter. A confidence interval with confidence level C is an interval
Continuous random variables
Continuous random variables probability density function (f(x)) the probability distribution function of a continuous random variable (analogous to the probability mass function for a discrete random variable),
Stochastic Loss Reserving with Bayesian MCMC Models Revised March 31
w w w. I C A 2 0 1 4. o r g Stochastic Loss Reserving with Bayesian MCMC Models Revised March 31 Glenn Meyers FCAS, MAAA, CERA, Ph.D. April 2, 2014 The CAS Loss Reserve Database Created by Meyers and Shi
CSC Advanced Scientific Programming, Spring Descriptive Statistics
CSC 223 - Advanced Scientific Programming, Spring 2018 Descriptive Statistics Overview Statistics is the science of collecting, organizing, analyzing, and interpreting data in order to make decisions.
Increasing Returns and Economic Geography
Increasing Returns and Economic Geography Department of Economics HKUST April 25, 2018 Increasing Returns and Economic Geography 1 / 31 Introduction: From Krugman (1979) to Krugman (1991) The award of
Estimation and Confidence Intervals
Estimation and Confidence Intervals Chapter 9-1/2 McGraw-Hill/Irwin Copyright 2011 by the McGraw-Hill Companies, Inc. All rights reserved. LEARNING OBJECTIVES LO1. Define a point estimate. LO2. Define
Distribution. Lecture 34 Section Fri, Oct 31, Hampden-Sydney College. Student s t Distribution. Robb T. Koether.
Lecture 34 Section 10.2 Hampden-Sydney College Fri, Oct 31, 2008 Outline 1 2 3 4 5 6 7 8 Exercise 10.4, page 633. A psychologist is studying the distribution of IQ scores of girls at an alternative high
continuous rv Note for a legitimate pdf, we have f (x) 0 and f (x)dx = 1. For a continuous rv, P(X = c) = c f (x)dx = 0, hence
continuous rv Let X be a continuous rv. Then a probability distribution or probability density function (pdf) of X is a function f(x) such that for any two numbers a and b with a b, P(a X b) = b a f (x)dx.
Analysis of 2x2 Cross-Over Designs using T-Tests for Non-Inferiority
Chapter 235 Analysis of 2x2 Cross-Over Designs using -ests for Non-Inferiority Introduction his procedure analyzes data from a two-treatment, two-period (2x2) cross-over design where the goal is to demonstrate
Lecture 18 Section Mon, Feb 16, 2009
The s the Lecture 18 Section 5.3.4 Hampden-Sydney College Mon, Feb 16, 2009 Outline The s the 1 2 3 The 4 s 5 the 6 The s the Exercise 5.12, page 333. The five-number summary for the distribution of income
STA 4504/5503 Sample questions for exam True-False questions.
STA 4504/5503 Sample questions for exam 2 1. True-False questions. (a) For General Social Survey data on Y = political ideology (categories liberal, moderate, conservative), X 1 = gender (1 = female, 0
. (i) What is the probability that X is at most 8.75? =.875
Worksheet 1 Prep-Work (Distributions) 1)Let X be the random variable whose c.d.f. is given below. F X 0 0.3 ( x) 0.5 0.8 1.0 if if if if if x 5 5 x 10 10 x 15 15 x 0 0 x Compute the mean, X. (Hint: First
Hints on Some of the Exercises
Hints on Some of the Exercises of the book R. Seydel: Tools for Computational Finance. Springer, 00/004/006/009/01. Preparatory Remarks: Some of the hints suggest ideas that may simplify solving the exercises
SEQUENTIAL DECISION PROBLEM WITH PARTIAL MAINTENANCE ON A PARTIALLY OBSERVABLE MARKOV PROCESS. Toru Nakai. Received February 22, 2010
Scientiae Mathematicae Japonicae Online, e-21, 283 292 283 SEQUENTIAL DECISION PROBLEM WITH PARTIAL MAINTENANCE ON A PARTIALLY OBSERVABLE MARKOV PROCESS Toru Nakai Received February 22, 21 Abstract. In
Lecture 18 Section Mon, Sep 29, 2008
The s the Lecture 18 Section 5.3.4 Hampden-Sydney College Mon, Sep 29, 2008 Outline The s the 1 2 3 The 4 s 5 the 6 The s the Exercise 5.12, page 333. The five-number summary for the distribution of income
Earnings Dynamics, Mobility Costs and Transmission of Firm and Market Level Shocks
Earnings Dynamics, Mobility Costs and Transmission of Firm and Market Level Shocks Preliminary and Incomplete Thibaut Lamadon Magne Mogstad Bradley Setzler U Chicago U Chicago U Chicago Statistics Norway
2. The sum of all the probabilities in the sample space must add up to 1
Continuous Random Variables and Continuous Probability Distributions Continuous Random Variable: A variable X that can take values on an interval; key feature remember is that the values of the variable
Lecture 6: Chapter 6
Lecture 6: Chapter 6 C C Moxley UAB Mathematics 3 October 16 6.1 Continuous Probability Distributions Last week, we discussed the binomial probability distribution, which was discrete. 6.1 Continuous Probability
7.1 Comparing Two Population Means: Independent Sampling
University of California, Davis Department of Statistics Summer Session II Statistics 13 September 4, 01 Lecture 7: Comparing Population Means Date of latest update: August 9 7.1 Comparing Two Population
Internet Appendix for Asymmetry in Stock Comovements: An Entropy Approach
Internet Appendix for Asymmetry in Stock Comovements: An Entropy Approach Lei Jiang Tsinghua University Ke Wu Renmin University of China Guofu Zhou Washington University in St. Louis August 2017 Jiang,
Internet Appendix for. A new method to estimate risk and return of. non-traded assets from cash flows: The case of. private equity funds
Internet Appendix for A new method to estimate risk and return of non-traded assets from cash flows: The case of private equity funds Joost Driessen Tse-Chun Lin Ludovic Phalippou February 2011 1 Internet
Probabilistic Analysis of the Economic Impact of Earthquake Prediction Systems
The Minnesota Journal of Undergraduate Mathematics Probabilistic Analysis of the Economic Impact of Earthquake Prediction Systems Tiffany Kolba and Ruyue Yuan Valparaiso University The Minnesota Journal
Density curves. (James Madison University) February 4, / 20
Density curves Figure 6.2 p 230. A density curve is always on or above the horizontal axis, and has area exactly 1 underneath it. A density curve describes the overall pattern of a distribution. Example
The Normal Distribution. (Ch 4.3)
5 The Normal Distribution (Ch 4.3) The Normal Distribution The normal distribution is probably the most important distribution in all of probability and statistics. Many populations have distributions
Experimental Design and Statistics - AGA47A
Experimental Design and Statistics - AGA47A Czech University of Life Sciences in Prague Department of Genetics and Breeding Fall/Winter 2014/2015 Matúš Maciak (@ A 211) Office Hours: M 14:00 15:30 W 15:30
University of California, Los Angeles Department of Statistics. Final exam 07 June 2013
University of California, Los Angeles Department of Statistics Statistics C183/C283 Instructor: Nicolas Christou Final exam 07 June 2013 Name: Problem 1 (20 points) a. Suppose the variable X follows the
Two Hours. Mathematical formula books and statistical tables are to be provided THE UNIVERSITY OF MANCHESTER. 22 January :00 16:00
Two Hours MATH38191 Mathematical formula books and statistical tables are to be provided THE UNIVERSITY OF MANCHESTER STATISTICAL MODELLING IN FINANCE 22 January 2015 14:00 16:00 Answer ALL TWO questions
Mixed models in R using the lme4 package Part 3: Inference based on profiled deviance
Mixed models in R using the lme4 package Part 3: Inference based on profiled deviance Douglas Bates Department of Statistics University of Wisconsin - Madison Madison January 11, 2011
Chapter 5. Continuous Random Variables and Probability Distributions. 5.1 Continuous Random Variables
Chapter 5 Continuous Random Variables and Probability Distributions 5.1 Continuous Random Variables 1 2CHAPTER 5. CONTINUOUS RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS Probability Distributions Probability
IE652 - Chapter 6. Stochastic Inventory Models
IE652 - Chapter 6 Stochastic Inventory Models Single Period Stochastic Model (News-boy Model) The problem relates to seasonal goods A typical example is a newsboy who buys news papers from a news paper
(5) Multi-parameter models - Summarizing the posterior
(5) Multi-parameter models - Summarizing the posterior Spring, 2017 Models with more than one parameter Thus far we have studied single-parameter models, but most analyses have several parameters For example,
Price Transmission from the Corn Market to the Hog Market in Québec
Price Transmission from the Corn Market to the Hog Market in Québec Aïcha Coulibaly, Ag Economist, M.B.A.,M.Sc. Michel Morin, Ag Economist, Market Analyst Contents Overview of the hog market in Québec
Outline. Review Continuation of exercises from last time
Bayesian Models II Outline Review Continuation of exercises from last time 2 Review of terms from last time Probability density function aka pdf or density Likelihood function aka likelihood Conditional
Confidence Intervals Introduction
Confidence Intervals Introduction A point estimate provides no information about the precision and reliability of estimation. For example, the sample mean X is a point estimate of the population mean μ
Quantitative Modelling of Market Booms and Crashes
Quantitative Modelling of Market Booms and Crashes Ilya Sheynzon (LSE) Workhop on Mathematics of Financial Risk Management Isaac Newton Institute for Mathematical Sciences March 28, 2013 October. This
Techniques for Calculating the Efficient Frontier
Techniques for Calculating the Efficient Frontier Weerachart Kilenthong RIPED, UTCC c Kilenthong 2017 Tee (Riped) Introduction 1 / 43 Two Fund Theorem The Two-Fund Theorem states that we can reach any
Lie Algebras and Representation Theory Homework 7
Lie Algebras and Representation Theory Homework 7 Debbie Matthews 2015-05-19 Problem 10.5 If σ W can be written as a product of t simple reflections, prove that t has the same parity as l(σ). Let = {α
STAT Chapter 6 The Standard Deviation (SD) as a Ruler and The Normal Model
STAT 203 - Chapter 6 The Standard Deviation (SD) as a Ruler and The Normal Model In Chapter 5, we introduced a few measures of center and spread, and discussed how the mean and standard deviation are good
Risk Reduction Potential
Risk Reduction Potential Research Paper 006 February, 015 015 Northstar Risk Corp. All rights reserved. info@northstarrisk.com Risk Reduction Potential In this paper we introduce the concept of risk reduction
Normal Probability Distributions
Normal Probability Distributions Properties of Normal Distributions The most important probability distribution in statistics is the normal distribution. Normal curve A normal distribution is a continuous
STAT Chapter 6 The Standard Deviation (SD) as a Ruler and The Normal Model
STAT 203 - Chapter 6 The Standard Deviation (SD) as a Ruler and The Normal Model In Chapter 5, we introduced a few measures of center and spread, and discussed how the mean and standard deviation are good
Lecture Notes 6. Assume F belongs to a family of distributions, (e.g. F is Normal), indexed by some parameter θ.
Sufficient Statistics Lecture Notes 6 Sufficiency Data reduction in terms of a particular statistic can be thought of as a partition of the sample space X. Definition T is sufficient for θ if the conditional
9.1 Principal Component Analysis for Portfolios
Chapter 9 Alpha Trading By the name of the strategies, an alpha trading strategy is to select and trade portfolios so the alpha is maximized. Two important mathematical objects are factor analysis and
Applied Statistics I
Applied Statistics I Liang Zhang Department of Mathematics, University of Utah July 14, 2008 Liang Zhang (UofU) Applied Statistics I July 14, 2008 1 / 18 Point Estimation Liang Zhang (UofU) Applied Statistics
Math Tech IIII, May 7
Math Tech IIII, May 7 The Normal Probability Models Book Sections: 5.1, 5.2, & 5.3 Essential Questions: How can I use the normal distribution to compute probability? Standards: S.ID.4 Properties of the
On Performance of Confidence Interval Estimate of Mean for Skewed Populations: Evidence from Examples and Simulations
On Performance of Confidence Interval Estimate of Mean for Skewed Populations: Evidence from Examples and Simulations Khairul Islam 1 * and Tanweer J Shapla 2 1,2 Department of Mathematics and Statistics
Basic Procedure for Histograms
Basic Procedure for Histograms 1. Compute the range of observations (min. & max. value) 2. Choose an initial # of classes (most likely based on the range of values, try and find a number of classes that
Hypothesis Tests: One Sample Mean Cal State Northridge Ψ320 Andrew Ainsworth PhD
Hypothesis Tests: One Sample Mean Cal State Northridge Ψ320 Andrew Ainsworth PhD MAJOR POINTS Sampling distribution of the mean revisited Testing hypotheses: sigma known An example Testing hypotheses:
1.017/1.010 Class 19 Analysis of Variance
.07/.00 Class 9 Analysis of Variance Concepts and Definitions Objective: dentify factors responsible for variability in observed data Specify one or more factors that could account for variability (e.g.
Value of Flexibility in Managing R&D Projects Revisited
Value of Flexibility in Managing R&D Projects Revisited Leonardo P. Santiago & Pirooz Vakili November 2004 Abstract In this paper we consider the question of whether an increase in uncertainty increases
Statistical properties of symmetrized percent change and percent change based on the bivariate power normal distribution
Int. Statistical Inst.: Proc. 58th World Statistical Congress, 2011, Dublin (Session CPS008) p.6061 Statistical properties of symmetrized percent change and percent change based on the bivariate power
An application of braid ordering to knot theory
January 11, 2009 Plan of the talk I The Dehornoy ordering, Dehornoy floor I Dehornoy floor and genus of closed braid I Nielsen-Thurston classification and Geometry of closed braid Braid groups I B n :
Actuarial Society of India EXAMINATIONS
Actuarial Society of India EXAMINATIONS 7 th June 005 Subject CT6 Statistical Models Time allowed: Three Hours (0.30 am 3.30 pm) INSTRUCTIONS TO THE CANDIDATES. Do not write your name anywhere on the answer
Chapter 7.2: Large-Sample Confidence Intervals for a Population Mean and Proportion. Instructor: Elvan Ceyhan
1 Chapter 7.2: Large-Sample Confidence Intervals for a Population Mean and Proportion Instructor: Elvan Ceyhan Outline of this chapter: Large-Sample Interval for µ Confidence Intervals for Population Proportion
Limit Theorems for the Empirical Distribution Function of Scaled Increments of Itô Semimartingales at high frequencies
Limit Theorems for the Empirical Distribution Function of Scaled Increments of Itô Semimartingales at high frequencies George Tauchen Duke University Viktor Todorov Northwestern University 2013 Motivation
ME3620. Theory of Engineering Experimentation. Spring Chapter III. Random Variables and Probability Distributions.
ME3620 Theory of Engineering Experimentation Chapter III. Random Variables and Probability Distributions Chapter III 1 3.2 Random Variables In an experiment, a measurement is usually denoted by a variable
ECO220Y Continuous Probability Distributions: Normal Readings: Chapter 9, section 9.10
ECO220Y Continuous Probability Distributions: Normal Readings: Chapter 9, section 9.10 Fall 2011 Lecture 8 Part 2 (Fall 2011) Probability Distributions Lecture 8 Part 2 1 / 23 Normal Density Function f
SOCIETY OF ACTUARIES EXAM STAM SHORT-TERM ACTUARIAL MATHEMATICS EXAM STAM SAMPLE QUESTIONS
SOCIETY OF ACTUARIES EXAM STAM SHORT-TERM ACTUARIAL MATHEMATICS EXAM STAM SAMPLE QUESTIONS Questions 1-307 have been taken from the previous set of Exam C sample questions. Questions no longer relevant
5/5/2014 یادگیري ماشین. (Machine Learning) ارزیابی فرضیه ها دانشگاه فردوسی مشهد دانشکده مهندسی رضا منصفی. Evaluating Hypothesis (بخش دوم)
یادگیري ماشین درس نوزدهم (Machine Learning) دانشگاه فردوسی مشهد دانشکده مهندسی رضا منصفی ارزیابی فرضیه ها Evaluating Hypothesis (بخش دوم) 1 فهرست مطالب خطاي نمونه Error) (Sample خطاي واقعی Error) (True
SHSU ECONOMICS WORKING PAPER
Sam Houston State University Department of Economics and International Business Working Paper Series Controlling Pollution with Fixed Inspection Capacity Lirong Liu SHSU Economics & Intl. Business Working
Estimation Y 3. Confidence intervals I, Feb 11,
Estimation Example: Cholesterol levels of heart-attack patients Data: Observational study at a Pennsylvania medical center blood cholesterol levels patients treated for heart attacks measurements 2, 4,
Supplementary Information: Facing uncertain climate change, immediate action is the best strategy
Supplementary Information: Facing uncertain climate change, immediate action is the best strategy Maria Abou Chakra 1,2, Silke Bumann 1, Hanna Schenk 1, Andreas Oschlies 3, and Arne Traulsen 1 1 Department
Dependence Structure and Extreme Comovements in International Equity and Bond Markets
Dependence Structure and Extreme Comovements in International Equity and Bond Markets René Garcia Edhec Business School, Université de Montréal, CIRANO and CIREQ Georges Tsafack Suffolk University Measuring
Collective bargaining, firm heterogeneity and unemployment
Collective bargaining, firm heterogeneity and unemployment Juan F. Jimeno and Carlos Thomas Banco de España ESSIM, May 25, 2012 Jimeno & Thomas (BdE) Collective bargaining ESSIM, May 25, 2012 1 / 39 Motivation
8.3 CI for μ, σ NOT known (old 8.4)
GOALS: 1. Learn the properties of the student t distribution and the t curve. 2. Understand how degrees of freedom, df, relate to t curves. 3. Recognize that t curves approach the SNC as df increases.
FINA 695 Assignment 1 Simon Foucher
Answer the following questions. Show your work. Due in the class on March 29. (postponed 1 week) You are expected to do the assignment on your own. Please do not take help from others. 1. (a) The current
On Quality Bias and Inflation Targets: Supplementary Material
On Quality Bias and Inflation Targets: Supplementary Material Stephanie Schmitt-Grohé Martín Uribe August 2 211 This document contains supplementary material to Schmitt-Grohé and Uribe (211). 1 A Two Sector
Chapter Seven: Confidence Intervals and Sample Size
Chapter Seven: Confidence Intervals and Sample Size A point estimate is: The best point estimate of the population mean µ is the sample mean X. Three Properties of a Good Estimator 1. Unbiased 2. Consistent
LaHC at INEX 2014: Social Book Search Track
LaHC at INEX 2014: Social Book Search Track Meriem Hafsi 1, Mathias Géry 1, Michel Beigbeder 2 1 Université de Lyon, F-42023, Saint-Étienne, France, CNRS, UMR 5516, Laboratoire Hubert Curien, F-42000,
Modeling multiple runoff tables
Modeling multiple runoff tables Vincent Lous www.posthuma-partners.nl Motivation Why creating a "model" for all lines of business is important Motivation Why creating a "model" for all lines of business
Likelihood Methods of Inference. Toss coin 6 times and get Heads twice.
Methods of Inference Toss coin 6 times and get Heads twice. p is probability of getting H. Probability of getting exactly 2 heads is 15p 2 (1 p) 4 This function of p, is likelihood function. Definition:
Interest rate models and Solvency II
www.nr.no Outline Desired properties of interest rate models in a Solvency II setting. A review of three well-known interest rate models A real example from a Norwegian insurance company 2 Interest rate
Two-Sample Z-Tests Assuming Equal Variance
Chapter 426 Two-Sample Z-Tests Assuming Equal Variance Introduction This procedure provides sample size and power calculations for one- or two-sided two-sample z-tests when the variances of the two groups
Online Appendix for Missing Growth from Creative Destruction
Online Appendix for Missing Growth from Creative Destruction Philippe Aghion Antonin Bergeaud Timo Boppart Peter J Klenow Huiyu Li January 17, 2017 A1 Heterogeneous elasticities and varying markups In
Statistics for Managers Using Microsoft Excel 7 th Edition
Statistics for Managers Using Microsoft Excel 7 th Edition Chapter 7 Sampling Distributions Statistics for Managers Using Microsoft Excel 7e Copyright 2014 Pearson Education, Inc. Chap 7-1 Learning Objectives