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5. Completeness and Sufficiency 5.1. Complete Statistics. Definition 5.1. a Statistic T Is Called Complete If Eg(T) = 0 For
5. Completeness and sufficiency 5.1. Complete statistics. Definition 5.1. A statistic T is called complete if Eg(T ) = 0 for all θ and some function g implies that P (g(T ) = 0; θ) = 1 for all θ. This use of the word complete is analogous to calling a set of vectors v1; : : : ; vn complete if they span the whole space, that is, any v can P be written as a linear combination v = ajvj of these vectors. This is equivalent to the condition that if w is orthogonal to all vj's, then w = 0. To make the connection with Definition 5.1, let's consider the discrete case. Then completeness means that P g(t)P (T = t; θ) = 0 implies that g(t) = 0. Since the sum may be viewed as the scalar prod- uct of the vectors (g(t1); g(t2);:::) and (p(t1); p(t2);:::), with p(t) = P (T = t), this is the analog of the orthogonality condition just dis- cussed. We also see that the terminology is somewhat misleading. It would be more accurate to call the family of distributions p(·; θ) complete (rather than the statistic T ). In any event, completeness means that the collection of distributions for all possible values of θ provides a sufficiently rich set of vectors. In the continuous case, a similar inter- pretation works. Completeness now refers to the collection of densities f(·; θ), and hf; gi = R fg serves as the (abstract) scalar product in this case. Example 5.1. Let's take one more look at the coin flip example. -
1 One Parameter Exponential Families
1 One parameter exponential families The world of exponential families bridges the gap between the Gaussian family and general dis- tributions. Many properties of Gaussians carry through to exponential families in a fairly precise sense. • In the Gaussian world, there exact small sample distributional results (i.e. t, F , χ2). • In the exponential family world, there are approximate distributional results (i.e. deviance tests). • In the general setting, we can only appeal to asymptotics. A one-parameter exponential family, F is a one-parameter family of distributions of the form Pη(dx) = exp (η · t(x) − Λ(η)) P0(dx) for some probability measure P0. The parameter η is called the natural or canonical parameter and the function Λ is called the cumulant generating function, and is simply the normalization needed to make dPη fη(x) = (x) = exp (η · t(x) − Λ(η)) dP0 a proper probability density. The random variable t(X) is the sufficient statistic of the exponential family. Note that P0 does not have to be a distribution on R, but these are of course the simplest examples. 1.0.1 A first example: Gaussian with linear sufficient statistic Consider the standard normal distribution Z e−z2=2 P0(A) = p dz A 2π and let t(x) = x. Then, the exponential family is eη·x−x2=2 Pη(dx) / p 2π and we see that Λ(η) = η2=2: eta= np.linspace(-2,2,101) CGF= eta**2/2. plt.plot(eta, CGF) A= plt.gca() A.set_xlabel(r'$\eta$', size=20) A.set_ylabel(r'$\Lambda(\eta)$', size=20) f= plt.gcf() 1 Thus, the exponential family in this setting is the collection F = fN(η; 1) : η 2 Rg : d 1.0.2 Normal with quadratic sufficient statistic on R d As a second example, take P0 = N(0;Id×d), i.e. -
Lecture 4: Sufficient Statistics 1 Sufficient Statistics
ECE 830 Fall 2011 Statistical Signal Processing instructor: R. Nowak Lecture 4: Sufficient Statistics Consider a random variable X whose distribution p is parametrized by θ 2 Θ where θ is a scalar or a vector. Denote this distribution as pX (xjθ) or p(xjθ), for short. In many signal processing applications we need to make some decision about θ from observations of X, where the density of X can be one of many in a family of distributions, fp(xjθ)gθ2Θ, indexed by different choices of the parameter θ. More generally, suppose we make n independent observations of X: X1;X2;:::;Xn where p(x1 : : : xnjθ) = Qn i=1 p(xijθ). These observations can be used to infer or estimate the correct value for θ. This problem can be posed as follows. Let x = [x1; x2; : : : ; xn] be a vector containing the n observations. Question: Is there a lower dimensional function of x, say t(x), that alone carries all the relevant information about θ? For example, if θ is a scalar parameter, then one might suppose that all relevant information in the observations can be summarized in a scalar statistic. Goal: Given a family of distributions fp(xjθ)gθ2Θ and one or more observations from a particular dis- tribution p(xjθ∗) in this family, find a data compression strategy that preserves all information pertaining to θ∗. The function identified by such strategyis called a sufficient statistic. 1 Sufficient Statistics Example 1 (Binary Source) Suppose X is a 0=1 - valued variable with P(X = 1) = θ and P(X = 0) = 1 − θ. -
Mathematical Statistics (Math 30) – Course Project for Spring 2011
Mathematical Statistics (Math 30) – Course Project for Spring 2011 Goals: To explore methods from class in a real life (historical) estimation setting To learn some basic statistical computational skills To explore a topic of interest via a short report on a selected paper The course project has been divided into 2 parts. Each part will account for 5% of your grade, because the project accounts for 10%. Part 1: German Tank Problem Due Date: April 8th Our Setting: The Purple (Amherst) and Gold (Williams) armies are at war. You are a highly ranked intelligence officer in the Purple army and you are given the job of estimating the number of tanks in the Gold army’s tank fleet. Luckily, the Gold army has decided to put sequential serial numbers on their tanks, so you can consider their tanks labeled as 1,2,3,…,N, and all you have to do is estimate N. Suppose you have access to a random sample of k serial numbers obtained by spies, found on abandoned/destroyed equipment, etc. Using your k observations, you need to estimate N. Note that we won’t deal with issues of sampling without replacement, so you can consider each observation as an independent observation from a discrete uniform distribution on 1 to N. The derivations below should lead you to consider several possible estimators of N, from which you will be picking one to propose as your estimate of N. Historical Setting: This was a real problem encountered by statisticians/intelligence in WWII. The Allies wanted to know how many tanks the Germans were producing per month (the procedure was used for things other than tanks too). -
Minimum Rates of Approximate Sufficient Statistics
1 Minimum Rates of Approximate Sufficient Statistics Masahito Hayashi,y Fellow, IEEE, Vincent Y. F. Tan,z Senior Member, IEEE Abstract—Given a sufficient statistic for a parametric family when one is given X, then Y is called a sufficient statistic of distributions, one can estimate the parameter without access relative to the family fPXjZ=zgz2Z . We may then write to the data. However, the memory or code size for storing the sufficient statistic may nonetheless still be prohibitive. Indeed, X for n independent samples drawn from a k-nomial distribution PXjZ=z(x) = PXjY (xjy)PY jZ=z(y); 8 (x; z) 2 X × Z with d = k − 1 degrees of freedom, the length of the code scales y2Y as d log n + O(1). In many applications, we may not have a (1) useful notion of sufficient statistics (e.g., when the parametric or more simply that X (−− Y (−− Z forms a Markov chain family is not an exponential family) and we also may not need in this order. Because Y is a function of X, it is also true that to reconstruct the generating distribution exactly. By adopting I(Z; X) = I(Z; Y ). This intuitively means that the sufficient a Shannon-theoretic approach in which we allow a small error in estimating the generating distribution, we construct various statistic Y provides as much information about the parameter approximate sufficient statistics and show that the code length Z as the original data X does. d can be reduced to 2 log n + O(1). We consider errors measured For concreteness in our discussions, we often (but not according to the relative entropy and variational distance criteria. -
The Likelihood Function - Introduction
The Likelihood Function - Introduction • Recall: a statistical model for some data is a set { f θ : θ ∈ Ω} of distributions, one of which corresponds to the true unknown distribution that produced the data. • The distribution fθ can be either a probability density function or a probability mass function. • The joint probability density function or probability mass function of iid random variables X1, …, Xn is n θ ()1 ,..., n = ∏ θ ()xfxxf i . i=1 week 3 1 The Likelihood Function •Let x1, …, xn be sample observations taken on corresponding random variables X1, …, Xn whose distribution depends on a parameter θ. The likelihood function defined on the parameter space Ω is given by L|(θ x1 ,..., xn ) = θ f( 1,..., xn ) x . • Note that for the likelihood function we are fixing the data, x1,…, xn, and varying the value of the parameter. •The value L(θ | x1, …, xn) is called the likelihood of θ. It is the probability of observing the data values we observed given that θ is the true value of the parameter. It is not the probability of θ given that we observed x1, …, xn. week 3 2 Examples • Suppose we toss a coin n = 10 times and observed 4 heads. With no knowledge whatsoever about the probability of getting a head on a single toss, the appropriate statistical model for the data is the Binomial(10, θ) model. The likelihood function is given by • Suppose X1, …, Xn is a random sample from an Exponential(θ) distribution. The likelihood function is week 3 3 Sufficiency - Introduction • A statistic that summarizes all the information in the sample about the target parameter is called sufficient statistic. -
Statistical Inference
GU4204: Statistical Inference Bodhisattva Sen Columbia University February 27, 2020 Contents 1 Introduction5 1.1 Statistical Inference: Motivation.....................5 1.2 Recap: Some results from probability..................5 1.3 Back to Example 1.1...........................8 1.4 Delta method...............................8 1.5 Back to Example 1.1........................... 10 2 Statistical Inference: Estimation 11 2.1 Statistical model............................. 11 2.2 Method of Moments estimators..................... 13 3 Method of Maximum Likelihood 16 3.1 Properties of MLEs............................ 20 3.1.1 Invariance............................. 20 3.1.2 Consistency............................ 21 3.2 Computational methods for approximating MLEs........... 21 3.2.1 Newton's Method......................... 21 3.2.2 The EM Algorithm........................ 22 1 4 Principles of estimation 23 4.1 Mean squared error............................ 24 4.2 Comparing estimators.......................... 25 4.3 Unbiased estimators........................... 26 4.4 Sufficient Statistics............................ 28 5 Bayesian paradigm 33 5.1 Prior distribution............................. 33 5.2 Posterior distribution........................... 34 5.3 Bayes Estimators............................. 36 5.4 Sampling from a normal distribution.................. 37 6 The sampling distribution of a statistic 39 6.1 The gamma and the χ2 distributions.................. 39 6.1.1 The gamma distribution..................... 39 6.1.2 The Chi-squared distribution.................. 41 6.2 Sampling from a normal population................... 42 6.3 The t-distribution............................. 45 7 Confidence intervals 46 8 The (Cramer-Rao) Information Inequality 51 9 Large Sample Properties of the MLE 57 10 Hypothesis Testing 61 10.1 Principles of Hypothesis Testing..................... 61 10.2 Critical regions and test statistics.................... 62 10.3 Power function and types of error.................... 64 10.4 Significance level............................ -
Local Variation As a Statistical Hypothesis Test
Int J Comput Vis (2016) 117:131–141 DOI 10.1007/s11263-015-0855-4 Local Variation as a Statistical Hypothesis Test Michael Baltaxe1 · Peter Meer2 · Michael Lindenbaum3 Received: 7 June 2014 / Accepted: 1 September 2015 / Published online: 16 September 2015 © Springer Science+Business Media New York 2015 Abstract The goal of image oversegmentation is to divide Keywords Image segmentation · Image oversegmenta- an image into several pieces, each of which should ideally tion · Superpixels · Grouping be part of an object. One of the simplest and yet most effec- tive oversegmentation algorithms is known as local variation (LV) Felzenszwalb and Huttenlocher in Efficient graph- 1 Introduction based image segmentation. IJCV 59(2):167–181 (2004). In this work, we study this algorithm and show that algorithms Image segmentation is the procedure of partitioning an input similar to LV can be devised by applying different statisti- image into several meaningful pieces or segments, each of cal models and decisions, thus providing further theoretical which should be semantically complete (i.e., an item or struc- justification and a well-founded explanation for the unex- ture by itself). Oversegmentation is a less demanding type of pected high performance of the LV approach. Some of these segmentation. The aim is to group several pixels in an image algorithms are based on statistics of natural images and on into a single unit called a superpixel (Ren and Malik 2003) a hypothesis testing decision; we denote these algorithms so that it is fully contained within an object; it represents a probabilistic local variation (pLV). The best pLV algorithm, fragment of a conceptually meaningful structure. -
An Empirical Test of the Sufficient Statistic Result for Monetary Shocks
An Empirical Test of the Sufficient Statistic Result for Monetary Shocks Andrea Ferrara Advisor: Prof. Francesco Lippi Thesis submitted to Einaudi Institute for Economics and Finance Department of Economics, LUISS Guido Carli to satisfy the requirements of the Master in Economics and Finance June 2020 Acknowledgments I thank my advisor Francesco Lippi for his guidance, availability and patience; I have been fortunate to have a continuous and close discussion with him throughout my master thesis’ studies and I am grateful for his teachings. I also thank Erwan Gautier and Herve´ Le Bihan at the Banque de France for providing me the data and for their comments. I also benefited from the comments of Felipe Berrutti, Marco Lippi, Claudio Michelacci, Tommaso Proietti and workshop participants at EIEF. I am grateful to the entire faculty of EIEF and LUISS for the teachings provided during my master. I am thankful to my classmates for the time spent together studying and in particular for their friendship. My friends in Florence have been a reference point in hard moments. Finally, I thank my family for their presence in any circumstance during these years. Abstract We empirically test the sufficient statistic result of Alvarez, Lippi and Oskolkov (2020). This theoretical result predicts that the cumulative effect of a monetary shock is summarized by the ratio of two steady state moments: frequency and kurtosis of price changes. Our strategy consists of three steps. In the first step, we employ a Factor Augmented VAR to estimate the response of different sectors to a monetary shock. In the second step, using microdata, we compute the sectorial frequency and the kurtosis of price changes. -
1 Sufficient Statistic Theorem
Mathematical Statistics (NYU, Spring 2003) Summary (answers to his potential exam questions) By Rebecca Sela 1Sufficient statistic theorem (1) Let X1, ..., Xn be a sample from the distribution f(x, θ).LetT (X1, ..., Xn) be asufficient statistic for θ with continuous factor function F (T (X1,...,Xn),θ). Then, P (X A T (X )=t) = lim P (X A (T (X ) t h) ∈ | h 0 ∈ | − ≤ → ¯ ¯ P (X A, (T (X ) t h¯)/h ¯ ¯ ¯ = lim ∈ − ≤ h 0 ¯ ¯ → P ( T (X¯ ) t h¯)/h ¯ − ≤ ¯ d ¯ ¯ P (X A,¯ T (X ) ¯t) = dt ∈ ¯ ≤ ¯ d P (T (X ) t) dt ≤ Consider first the numerator: d d P (X A, T (X ) t)= f(x1,θ)...f(xn,θ)dx1...dxn dt ∈ ≤ dt A x:T (x)=t Z ∩{ } d = F (T (x),θ),h(x)dx1...dxn dt A x:T (x)=t Z ∩{ } 1 = lim F (T (x),θ),h(x)dx1...dxn h 0 h A x: T (x) t h → Z ∩{ | − |≤ } Since mins [t,t+h] F (s, θ) F (t, θ) maxs [t,t+h] on the interval [t, t + h], we find: ∈ ≤ ≤ ∈ 1 1 lim (min F (s, θ)) h(x)dx lim F (T (x),θ)h(x)dx h 0 s [t,t+h] h A x: T (x) t h ≤ h 0 h A x: T (x) t h → ∈ Z ∩{ k − k≤ } → Z ∩{ k − k≤ } 1 lim (max F (s, θ)) h(x)dx ≤ h 0 s [t,t+h] h A x: T (x) t h → ∈ Z ∩{ k − k≤ } 1 By the continuity of F (t, θ), limh 0(mins [t,t+h] F (s, θ)) h h(x)dx = → ∈ A x: T (x) t h 1 ∩{ k − k≤ } limh 0(maxs [t,t+h] F (s, θ)) h A x: T (x) t h h(x)dx = F (t, θ).Thus, → ∈ ∩{ k − k≤ } R R 1 1 lim F (T (x),θ),h(x)dx1...dxn = F (t, θ) lim h(x)dx h 0 h A x: T (x) t h h 0 h A x: T (x) t h → Z ∩{ | − |≤ } → Z ∩{ | − |≤ } d = F (t, θ) h(x)dx dt A x:T (x) t Z ∩{ ≤ } 1 If we let A be all of Rn, then we have the case of the denominator. -
STAT 517:Sufficiency
STAT 517:Sufficiency Minimal sufficiency and Ancillary Statistics. Sufficiency, Completeness, and Independence Prof. Michael Levine March 1, 2016 Levine STAT 517:Sufficiency Motivation I Not all sufficient statistics created equal... I So far, we have mostly had a single sufficient statistic for one parameter or two for two parameters (with some exceptions) I Is it possible to find the minimal sufficient statistics when further reduction in their number is impossible? I Commonly, for k parameters one can get k minimal sufficient statistics Levine STAT 517:Sufficiency Example I X1;:::; Xn ∼ Unif (θ − 1; θ + 1) so that 1 f (x; θ) = I (x) 2 (θ−1,θ+1) where −∞ < θ < 1 I The joint pdf is −n 2 I(θ−1,θ+1)(min xi ) I(θ−1,θ+1)(max xi ) I It is intuitively clear that Y1 = min xi and Y2 = max xi are joint minimal sufficient statistics Levine STAT 517:Sufficiency Occasional relationship between MLE's and minimal sufficient statistics I Earlier, we noted that the MLE θ^ is a function of one or more sufficient statistics, when the latter exists I If θ^ is itself a sufficient statistic, then it is a function of others...and so it may be a sufficient statistic 2 2 I E.g. the MLE θ^ = X¯ of θ in N(θ; σ ), σ is known, is a minimal sufficient statistic for θ I The MLE θ^ of θ in a P(θ) is a minimal sufficient statistic for θ ^ I The MLE θ = Y(n) = max1≤i≤n Xi of θ in a Unif (0; θ) is a minimal sufficient statistic for θ ^ ¯ ^ n−1 2 I θ1 = X and θ2 = n S of θ1 and θ2 in N(θ1; θ2) are joint minimal sufficient statistics for θ1 and θ2 Levine STAT 517:Sufficiency Formal definition I A sufficient statistic -
Stats 512 513 Review ♥
Stats 512 513 Review ♥ Eileen Burns, FSA, MAAA June 16, 2009 Contents 1 Basic Probability 4 1.1 Experiment, Sample Space, RV, and Probability . .... 4 1.2 Density, Value Number Urn Model, and Dice . ... 5 1.3 Expected Value Descriptive Parameters . ..... 5 1.4 More About Normal Distributions ............................... 5 1.5 Independent Trials and a Pictorial CLT . .... 5 1.6 The Population, the Sample, and Data ............................. 5 1.7 Elementary Probability, Stressing Independence . ... 5 1.8 Expectation, Variance, and the CLT . .... 6 1.9 Applications of the CLT . .... 7 2 Introduction to Statistics 8 2.1 PresentationofData ................................ ....... 8 2.2 Estimation of µ and σ2 ..................................... 8 2.3 Elementary Classical Statistics . ......... 8 2.4 Elementary Statistical Applications . ........ 9 3 Probability Models 10 3.1 MathFacts ........................................ 10 3.2 Combinatorics and Hypergeometric RVs . ...... 11 3.3 Independent Bernoulli Trials . 11 3.4 ThePoissonDistribution ............................. ....... 12 3.5 The Poisson Process N ...................................... 12 3.6 The Failure Rate Function λ( ) ................................. 13 · 3.7 Min, Max, Median, and Order Statistics . 13 3.8 Multinomial Distributions . ....... 14 3.9 Sampling from a Finite Populations, with a CLT ....................... 14 4 Dependent Random Variables 17 4.1 Two-Dimensional Discrete RVs . ..... 17 4.2 Two-Dimensional Continuous RVs . ..... 18 4.3 Conditional Expectation