Week 3: Learning from Random Samples

Total Page:16

File Type:pdf, Size:1020Kb

Week 3: Learning from Random Samples Week 3: Learning from Random Samples Brandon Stewart1 Princeton September 26/28, 2016 1These slides are heavily influenced by Matt Blackwell, Adam Glynn, Justin Grimmer and Jens Hainmueller. Some illustrations by Shay O'Brien. Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 1 / 141 I random variables I joint distributions This Week I Monday: Point Estimation F sampling and sampling distributions F point estimates F properties (bias, variance, consistency) I Wednesday: Interval Estimation F confidence intervals F comparing two groups Next Week I hypothesis testing I what is regression? Long Run I probability ! inference ! regression Questions? Where We've Been and Where We're Going... Last Week Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 2 / 141 I joint distributions This Week I Monday: Point Estimation F sampling and sampling distributions F point estimates F properties (bias, variance, consistency) I Wednesday: Interval Estimation F confidence intervals F comparing two groups Next Week I hypothesis testing I what is regression? Long Run I probability ! inference ! regression Questions? Where We've Been and Where We're Going... Last Week I random variables Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 2 / 141 This Week I Monday: Point Estimation F sampling and sampling distributions F point estimates F properties (bias, variance, consistency) I Wednesday: Interval Estimation F confidence intervals F comparing two groups Next Week I hypothesis testing I what is regression? Long Run I probability ! inference ! regression Questions? Where We've Been and Where We're Going... Last Week I random variables I joint distributions Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 2 / 141 I Monday: Point Estimation F sampling and sampling distributions F point estimates F properties (bias, variance, consistency) I Wednesday: Interval Estimation F confidence intervals F comparing two groups Next Week I hypothesis testing I what is regression? Long Run I probability ! inference ! regression Questions? Where We've Been and Where We're Going... Last Week I random variables I joint distributions This Week Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 2 / 141 F point estimates F properties (bias, variance, consistency) I Wednesday: Interval Estimation F confidence intervals F comparing two groups Next Week I hypothesis testing I what is regression? Long Run I probability ! inference ! regression Questions? Where We've Been and Where We're Going... Last Week I random variables I joint distributions This Week I Monday: Point Estimation F sampling and sampling distributions Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 2 / 141 F properties (bias, variance, consistency) I Wednesday: Interval Estimation F confidence intervals F comparing two groups Next Week I hypothesis testing I what is regression? Long Run I probability ! inference ! regression Questions? Where We've Been and Where We're Going... Last Week I random variables I joint distributions This Week I Monday: Point Estimation F sampling and sampling distributions F point estimates Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 2 / 141 I Wednesday: Interval Estimation F confidence intervals F comparing two groups Next Week I hypothesis testing I what is regression? Long Run I probability ! inference ! regression Questions? Where We've Been and Where We're Going... Last Week I random variables I joint distributions This Week I Monday: Point Estimation F sampling and sampling distributions F point estimates F properties (bias, variance, consistency) Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 2 / 141 F comparing two groups Next Week I hypothesis testing I what is regression? Long Run I probability ! inference ! regression Questions? Where We've Been and Where We're Going... Last Week I random variables I joint distributions This Week I Monday: Point Estimation F sampling and sampling distributions F point estimates F properties (bias, variance, consistency) I Wednesday: Interval Estimation F confidence intervals Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 2 / 141 Next Week I hypothesis testing I what is regression? Long Run I probability ! inference ! regression Questions? Where We've Been and Where We're Going... Last Week I random variables I joint distributions This Week I Monday: Point Estimation F sampling and sampling distributions F point estimates F properties (bias, variance, consistency) I Wednesday: Interval Estimation F confidence intervals F comparing two groups Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 2 / 141 I hypothesis testing I what is regression? Long Run I probability ! inference ! regression Questions? Where We've Been and Where We're Going... Last Week I random variables I joint distributions This Week I Monday: Point Estimation F sampling and sampling distributions F point estimates F properties (bias, variance, consistency) I Wednesday: Interval Estimation F confidence intervals F comparing two groups Next Week Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 2 / 141 I what is regression? Long Run I probability ! inference ! regression Questions? Where We've Been and Where We're Going... Last Week I random variables I joint distributions This Week I Monday: Point Estimation F sampling and sampling distributions F point estimates F properties (bias, variance, consistency) I Wednesday: Interval Estimation F confidence intervals F comparing two groups Next Week I hypothesis testing Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 2 / 141 Long Run I probability ! inference ! regression Questions? Where We've Been and Where We're Going... Last Week I random variables I joint distributions This Week I Monday: Point Estimation F sampling and sampling distributions F point estimates F properties (bias, variance, consistency) I Wednesday: Interval Estimation F confidence intervals F comparing two groups Next Week I hypothesis testing I what is regression? Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 2 / 141 I probability ! inference ! regression Questions? Where We've Been and Where We're Going... Last Week I random variables I joint distributions This Week I Monday: Point Estimation F sampling and sampling distributions F point estimates F properties (bias, variance, consistency) I Wednesday: Interval Estimation F confidence intervals F comparing two groups Next Week I hypothesis testing I what is regression? Long Run Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 2 / 141 Questions? Where We've Been and Where We're Going... Last Week I random variables I joint distributions This Week I Monday: Point Estimation F sampling and sampling distributions F point estimates F properties (bias, variance, consistency) I Wednesday: Interval Estimation F confidence intervals F comparing two groups Next Week I hypothesis testing I what is regression? Long Run I probability ! inference ! regression Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 2 / 141 Where We've Been and Where We're Going... Last Week I random variables I joint distributions This Week I Monday: Point Estimation F sampling and sampling distributions F point estimates F properties (bias, variance, consistency) I Wednesday: Interval Estimation F confidence intervals F comparing two groups Next Week I hypothesis testing I what is regression? Long Run I probability ! inference ! regression Questions? Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 2 / 141 For the last few classes we have talked about probability|that is, if we knew how the world worked, we are describing what kind of data we should expect. Now we want to move the other way. If we have a set of data, can we estimate the various parts of the probability distributions that we have talked about. Can we estimate the mean, the variance, the covariance, etc? Moving forward this is going to be very important. Why? Because we are going to want to estimate the population conditional expectation in regression. Inference: given that we observe something in the data, what is our best guess of what it will be in other samples? Where We've Been and Where We're Going... Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 3 / 141 For the last few classes we have talked about probability|that is, if we knew how the world worked, we are describing what kind of data we should expect. Now we want to move the other way. If we have a set of data, can we estimate the various parts of the probability distributions that we have talked about. Can we estimate the mean, the variance, the covariance, etc? Moving forward this is going to be very important. Why? Because we are going to want to estimate the population conditional expectation in regression. Where We've Been and Where We're Going... Inference: given that we observe something in the data, what is our best guess of what it will be in other samples? Stewart (Princeton) Week 3: Learning From Random Samples September 26/28, 2016 3 / 141 Now we want to move the other way. If we have a set of data, can we estimate the various parts of the probability distributions that we have talked about. Can we estimate the mean, the variance, the covariance, etc? Moving forward this is going to be very important. Why? Because we are going to want to estimate the population conditional expectation in regression. Where We've Been and Where We're Going... Inference: given that we observe something in the data, what is our best guess of what it will be in other samples? For the last few classes we have talked about probability|that is, if we knew how the world worked, we are describing what kind of data we should expect.
Recommended publications
  • Confidence Interval Estimation in System Dynamics Models: Bootstrapping Vs
    Confidence Interval Estimation in System Dynamics Models: Bootstrapping vs. Likelihood Ratio Method Gokhan Dogan* MIT Sloan School of Management, 30 Wadsworth Street, E53-364, Cambridge, Massachusetts 02142 [email protected] Abstract In this paper we discuss confidence interval estimation for system dynamics models. Confidence interval estimation is important because without confidence intervals, we cannot determine whether an estimated parameter value is significantly different from 0 or any other value, and therefore we cannot determine how much confidence to place in the estimate. We compare two methods for confidence interval estimation. The first, the “likelihood ratio method,” is based on maximum likelihood estimation. This method has been used in the system dynamics literature and is built in to some popular software packages. It is computationally efficient but requires strong assumptions about the model and data. These assumptions are frequently violated by the autocorrelation, endogeneity of explanatory variables and heteroskedasticity properties of dynamic models. The second method is called “bootstrapping.” Bootstrapping requires more computation but does not impose strong assumptions on the model or data. We describe the methods and illustrate them with a series of applications from actual modeling projects. Considering the empirical results presented in the paper and the fact that the bootstrapping method requires less assumptions, we suggest that bootstrapping is a better tool for confidence interval estimation in system dynamics
    [Show full text]
  • WHAT DID FISHER MEAN by an ESTIMATE? 3 Ideas but Is in Conflict with His Ideology of Statistical Inference
    Submitted to the Annals of Applied Probability WHAT DID FISHER MEAN BY AN ESTIMATE? By Esa Uusipaikka∗ University of Turku Fisher’s Method of Maximum Likelihood is shown to be a proce- dure for the construction of likelihood intervals or regions, instead of a procedure of point estimation. Based on Fisher’s articles and books it is justified that by estimation Fisher meant the construction of likelihood intervals or regions from appropriate likelihood function and that an estimate is a statistic, that is, a function from a sample space to a parameter space such that the likelihood function obtained from the sampling distribution of the statistic at the observed value of the statistic is used to construct likelihood intervals or regions. Thus Problem of Estimation is how to choose the ’best’ estimate. Fisher’s solution for the problem of estimation is Maximum Likeli- hood Estimate (MLE). Fisher’s Theory of Statistical Estimation is a chain of ideas used to justify MLE as the solution of the problem of estimation. The construction of confidence intervals by the delta method from the asymptotic normal distribution of MLE is based on Fisher’s ideas, but is against his ’logic of statistical inference’. Instead the construc- tion of confidence intervals from the profile likelihood function of a given interest function of the parameter vector is considered as a solution more in line with Fisher’s ’ideology’. A new method of cal- culation of profile likelihood-based confidence intervals for general smooth interest functions in general statistical models is considered. 1. Introduction. ’Collected Papers of R.A.
    [Show full text]
  • Chapter 8 Fundamental Sampling Distributions And
    CHAPTER 8 FUNDAMENTAL SAMPLING DISTRIBUTIONS AND DATA DESCRIPTIONS 8.1 Random Sampling pling procedure, it is desirable to choose a random sample in the sense that the observations are made The basic idea of the statistical inference is that we independently and at random. are allowed to draw inferences or conclusions about a Random Sample population based on the statistics computed from the sample data so that we could infer something about Let X1;X2;:::;Xn be n independent random variables, the parameters and obtain more information about the each having the same probability distribution f (x). population. Thus we must make sure that the samples Define X1;X2;:::;Xn to be a random sample of size must be good representatives of the population and n from the population f (x) and write its joint proba- pay attention on the sampling bias and variability to bility distribution as ensure the validity of statistical inference. f (x1;x2;:::;xn) = f (x1) f (x2) f (xn): ··· 8.2 Some Important Statistics It is important to measure the center and the variabil- ity of the population. For the purpose of the inference, we study the following measures regarding to the cen- ter and the variability. 8.2.1 Location Measures of a Sample The most commonly used statistics for measuring the center of a set of data, arranged in order of mag- nitude, are the sample mean, sample median, and sample mode. Let X1;X2;:::;Xn represent n random variables. Sample Mean To calculate the average, or mean, add all values, then Bias divide by the number of individuals.
    [Show full text]
  • A Framework for Sample Efficient Interval Estimation with Control
    A Framework for Sample Efficient Interval Estimation with Control Variates Shengjia Zhao Christopher Yeh Stefano Ermon Stanford University Stanford University Stanford University Abstract timates, e.g. for each individual district or county, meaning that we need to draw a sufficient number of We consider the problem of estimating confi- samples for every district or county. Another diffi- dence intervals for the mean of a random vari- culty can arise when we need high accuracy (i.e. a able, where the goal is to produce the smallest small confidence interval), since confidence intervals possible interval for a given number of sam- producedp by typical concentration inequalities have size ples. While minimax optimal algorithms are O(1= number of samples). In other words, to reduce known for this problem in the general case, the size of a confidence interval by a factor of 10, we improved performance is possible under addi- need 100 times more samples. tional assumptions. In particular, we design This dilemma is generally unavoidable because concen- an estimation algorithm to take advantage of tration inequalities such as Chernoff or Chebychev are side information in the form of a control vari- minimax optimal: there exist distributions for which ate, leveraging order statistics. Under certain these inequalities cannot be improved. No-free-lunch conditions on the quality of the control vari- results (Van der Vaart, 2000) imply that any alter- ates, we show improved asymptotic efficiency native estimation algorithm that performs better (i.e. compared to existing estimation algorithms. outputs a confidence interval with smaller size) on some Empirically, we demonstrate superior perfor- problems, must perform worse on other problems.
    [Show full text]
  • Likelihood Confidence Intervals When Only Ranges Are Available
    Article Likelihood Confidence Intervals When Only Ranges are Available Szilárd Nemes Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg 405 30, Sweden; [email protected] Received: 22 December 2018; Accepted: 3 February 2019; Published: 6 February 2019 Abstract: Research papers represent an important and rich source of comparative data. The change is to extract the information of interest. Herein, we look at the possibilities to construct confidence intervals for sample averages when only ranges are available with maximum likelihood estimation with order statistics (MLEOS). Using Monte Carlo simulation, we looked at the confidence interval coverage characteristics for likelihood ratio and Wald-type approximate 95% confidence intervals. We saw indication that the likelihood ratio interval had better coverage and narrower intervals. For single parameter distributions, MLEOS is directly applicable. For location-scale distribution is recommended that the variance (or combination of it) to be estimated using standard formulas and used as a plug-in. Keywords: range, likelihood, order statistics, coverage 1. Introduction One of the tasks statisticians face is extracting and possibly inferring biologically/clinically relevant information from published papers. This aspect of applied statistics is well developed, and one can choose to form many easy to use and performant algorithms that aid problem solving. Often, these algorithms aim to aid statisticians/practitioners to extract variability of different measures or biomarkers that is needed for power calculation and research design [1,2]. While these algorithms are efficient and easy to use, they mostly are not probabilistic in nature, thus they do not offer means for statistical inference. Yet another field of applied statistics that aims to help practitioners in extracting relevant information when only partial data is available propose a probabilistic approach with order statistics.
    [Show full text]
  • Permutation Tests
    Permutation tests Ken Rice Thomas Lumley UW Biostatistics Seattle, June 2008 Overview • Permutation tests • A mean • Smallest p-value across multiple models • Cautionary notes Testing In testing a null hypothesis we need a test statistic that will have different values under the null hypothesis and the alternatives we care about (eg a relative risk of diabetes) We then need to compute the sampling distribution of the test statistic when the null hypothesis is true. For some test statistics and some null hypotheses this can be done analytically. The p- value for the is the probability that the test statistic would be at least as extreme as we observed, if the null hypothesis is true. A permutation test gives a simple way to compute the sampling distribution for any test statistic, under the strong null hypothesis that a set of genetic variants has absolutely no effect on the outcome. Permutations To estimate the sampling distribution of the test statistic we need many samples generated under the strong null hypothesis. If the null hypothesis is true, changing the exposure would have no effect on the outcome. By randomly shuffling the exposures we can make up as many data sets as we like. If the null hypothesis is true the shuffled data sets should look like the real data, otherwise they should look different from the real data. The ranking of the real test statistic among the shuffled test statistics gives a p-value Example: null is true Data Shuffling outcomes Shuffling outcomes (ordered) gender outcome gender outcome gender outcome Example: null is false Data Shuffling outcomes Shuffling outcomes (ordered) gender outcome gender outcome gender outcome Means Our first example is a difference in mean outcome in a dominant model for a single SNP ## make up some `true' data carrier<-rep(c(0,1), c(100,200)) null.y<-rnorm(300) alt.y<-rnorm(300, mean=carrier/2) In this case we know from theory the distribution of a difference in means and we could just do a t-test.
    [Show full text]
  • Sampling Distribution of the Variance
    Proceedings of the 2009 Winter Simulation Conference M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin, and R. G. Ingalls, eds. SAMPLING DISTRIBUTION OF THE VARIANCE Pierre L. Douillet Univ Lille Nord de France, F-59000 Lille, France ENSAIT, GEMTEX, F-59100 Roubaix, France ABSTRACT Without confidence intervals, any simulation is worthless. These intervals are quite ever obtained from the so called "sampling variance". In this paper, some well-known results concerning the sampling distribution of the variance are recalled and completed by simulations and new results. The conclusion is that, except from normally distributed populations, this distribution is more difficult to catch than ordinary stated in application papers. 1 INTRODUCTION Modeling is translating reality into formulas, thereafter acting on the formulas and finally translating the results back to reality. Obviously, the model has to be tractable in order to be useful. But too often, the extra hypotheses that are assumed to ensure tractability are held as rock-solid properties of the real world. It must be recalled that "everyday life" is not only made with "every day events" : rare events are rarely occurring, but they do. For example, modeling a bell shaped histogram of experimental frequencies by a Gaussian pdf (probability density function) or a Fisher’s pdf with four parameters is usual. Thereafter transforming this pdf into a mgf (moment generating function) by mgf (z)=Et (expzt) is a powerful tool to obtain (and prove) the properties of the modeling pdf . But this doesn’t imply that a specific moment (e.g. 4) is effectively an accessible experimental reality.
    [Show full text]
  • Arxiv:1804.01620V1 [Stat.ML]
    ACTIVE COVARIANCE ESTIMATION BY RANDOM SUB-SAMPLING OF VARIABLES Eduardo Pavez and Antonio Ortega University of Southern California ABSTRACT missing data case, as well as for designing active learning algorithms as we will discuss in more detail in Section 5. We study covariance matrix estimation for the case of partially ob- In this work we analyze an unbiased covariance matrix estima- served random vectors, where different samples contain different tor under sub-Gaussian assumptions on x. Our main result is an subsets of vector coordinates. Each observation is the product of error bound on the Frobenius norm that reveals the relation between the variable of interest with a 0 1 Bernoulli random variable. We − number of observations, sub-sampling probabilities and entries of analyze an unbiased covariance estimator under this model, and de- the true covariance matrix. We apply this error bound to the design rive an error bound that reveals relations between the sub-sampling of sub-sampling probabilities in an active covariance estimation sce- probabilities and the entries of the covariance matrix. We apply our nario. An interesting conclusion from this work is that when the analysis in an active learning framework, where the expected number covariance matrix is approximately low rank, an active covariance of observed variables is small compared to the dimension of the vec- estimation approach can perform almost as well as an estimator with tor of interest, and propose a design of optimal sub-sampling proba- complete observations. The paper is organized as follows, in Section bilities and an active covariance matrix estimation algorithm.
    [Show full text]
  • Examination of Residuals
    EXAMINATION OF RESIDUALS F. J. ANSCOMBE PRINCETON UNIVERSITY AND THE UNIVERSITY OF CHICAGO 1. Introduction 1.1. Suppose that n given observations, yi, Y2, * , y., are claimed to be inde- pendent determinations, having equal weight, of means pA, A2, * *X, n, such that (1) Ai= E ai,Or, where A = (air) is a matrix of given coefficients and (Or) is a vector of unknown parameters. In this paper the suffix i (and later the suffixes j, k, 1) will always run over the values 1, 2, * , n, and the suffix r will run from 1 up to the num- ber of parameters (t1r). Let (#r) denote estimates of (Or) obtained by the method of least squares, let (Yi) denote the fitted values, (2) Y= Eai, and let (zt) denote the residuals, (3) Zi =Yi - Yi. If A stands for the linear space spanned by (ail), (a,2), *-- , that is, by the columns of A, and if X is the complement of A, consisting of all n-component vectors orthogonal to A, then (Yi) is the projection of (yt) on A and (zi) is the projection of (yi) on Z. Let Q = (qij) be the idempotent positive-semidefinite symmetric matrix taking (y1) into (zi), that is, (4) Zi= qtj,yj. If A has dimension n - v (where v > 0), X is of dimension v and Q has rank v. Given A, we can choose a parameter set (0,), where r = 1, 2, * , n -v, such that the columns of A are linearly independent, and then if V-1 = A'A and if I stands for the n X n identity matrix (6ti), we have (5) Q =I-AVA'.
    [Show full text]
  • Lecture 14 Testing for Kurtosis
    9/8/2016 CHE384, From Data to Decisions: Measurement, Kurtosis Uncertainty, Analysis, and Modeling • For any distribution, the kurtosis (sometimes Lecture 14 called the excess kurtosis) is defined as Testing for Kurtosis 3 (old notation = ) • For a unimodal, symmetric distribution, Chris A. Mack – a positive kurtosis means “heavy tails” and a more Adjunct Associate Professor peaked center compared to a normal distribution – a negative kurtosis means “light tails” and a more spread center compared to a normal distribution http://www.lithoguru.com/scientist/statistics/ © Chris Mack, 2016Data to Decisions 1 © Chris Mack, 2016Data to Decisions 2 Kurtosis Examples One Impact of Excess Kurtosis • For the Student’s t • For a normal distribution, the sample distribution, the variance will have an expected value of s2, excess kurtosis is and a variance of 6 2 4 1 for DF > 4 ( for DF ≤ 4 the kurtosis is infinite) • For a distribution with excess kurtosis • For a uniform 2 1 1 distribution, 1 2 © Chris Mack, 2016Data to Decisions 3 © Chris Mack, 2016Data to Decisions 4 Sample Kurtosis Sample Kurtosis • For a sample of size n, the sample kurtosis is • An unbiased estimator of the sample excess 1 kurtosis is ∑ ̅ 1 3 3 1 6 1 2 3 ∑ ̅ Standard Error: • For large n, the sampling distribution of 1 24 2 1 approaches Normal with mean 0 and variance 2 1 of 24/n 3 5 • For small samples, this estimator is biased D. N. Joanes and C. A. Gill, “Comparing Measures of Sample Skewness and Kurtosis”, The Statistician, 47(1),183–189 (1998).
    [Show full text]
  • Statistics Sampling Distribution Note
    9/30/2015 Statistics •A statistic is any quantity whose value can be calculated from sample data. CH5: Statistics and their distributions • A statistic can be thought of as a random variable. MATH/STAT360 CH5 1 MATH/STAT360 CH5 2 Sampling Distribution Note • Any statistic, being a random variable, has • In this group of notes we will look at a probability distribution. examples where we know the population • The probability distribution of a statistic is and it’s parameters. sometimes referred to as its sampling • This is to give us insight into how to distribution. proceed when we have large populations with unknown parameters (which is the more typical scenario). MATH/STAT360 CH5 3 MATH/STAT360 CH5 4 1 9/30/2015 The “Meta-Experiment” Sample Statistics • The “Meta-Experiment” consists of indefinitely Meta-Experiment many repetitions of the same experiment. Experiment • If the experiment is taking a sample of 100 items Sample Sample Sample from a population, the meta-experiment is to Population Population Sample of n Statistic repeatedly take samples of 100 items from the of n Statistic population. Sample of n Sample • This is a theoretical construct to help us Statistic understand the probabilities involved in our Sample of n Sample experiment. Statistic . Etc. MATH/STAT360 CH5 5 MATH/STAT360 CH5 6 Distribution of the Sample Mean Example: Random Rectangles 100 Rectangles with µ=7.42 and σ=5.26. Let X1, X2,…,Xn be a random sample from Histogram of Areas a distribution with mean value µ and standard deviation σ. Then 1. E(X ) X 2 2 2.
    [Show full text]
  • 32. Statistics 1 32
    32. Statistics 1 32. STATISTICS Revised September 2007 by G. Cowan (RHUL). This chapter gives an overview of statistical methods used in High Energy Physics. In statistics, we are interested in using a given sample of data to make inferences about a probabilistic model, e.g., to assess the model’s validity or to determine the values of its parameters. There are two main approaches to statistical inference, which we may call frequentist and Bayesian. In frequentist statistics, probability is interpreted as the frequency of the outcome of a repeatable experiment. The most important tools in this framework are parameter estimation, covered in Section 32.1, and statistical tests, discussed in Section 32.2. Frequentist confidence intervals, which are constructed so as to cover the true value of a parameter with a specified probability, are treated in Section 32.3.2. Note that in frequentist statistics one does not define a probability for a hypothesis or for a parameter. Frequentist statistics provides the usual tools for reporting objectively the outcome of an experiment without needing to incorporate prior beliefs concerning the parameter being measured or the theory being tested. As such they are used for reporting most measurements and their statistical uncertainties in High Energy Physics. In Bayesian statistics, the interpretation of probability is more general and includes degree of belief (called subjective probability). One can then speak of a probability density function (p.d.f.) for a parameter, which expresses one’s state of knowledge about where its true value lies. Bayesian methods allow for a natural way to input additional information, such as physical boundaries and subjective information; in fact they require as input the prior p.d.f.
    [Show full text]