DOCSLIB.ORG

Lecture 13: Simple Linear Regression in Matrix Format

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Lecture 13: Simple Linear Regression in Matrix Format 11:55 Wednesday 14th October, 2015 See updates and corrections at http://www.stat.cmu.edu/~cshalizi/mreg/ Lecture 13: Simple Linear Regression in Matrix Format 36-401, Section B, Fall 2015 13 October 2015 Contents 1 Least Squares in Matrix Form 2 1.1 The Basic Matrices . .2 1.2 Mean Squared Error . .3 1.3 Minimizing the MSE . .4 2 Fitted Values and Residuals 5 2.1 Residuals . .7 2.2 Expectations and Covariances . .7 3 Sampling Distribution of Estimators 8 4 Derivatives with Respect to Vectors 9 4.1 Second Derivatives . 11 4.2 Maxima and Minima . 11 5 Expectations and Variances with Vectors and Matrices 12 6 Further Reading 13 1 2 So far, we have not used any notions, or notation, that goes beyond basic algebra and calculus (and probability). This has forced us to do a fair amount of book-keeping, as it were by hand. This is just about tolerable for the simple linear model, with one predictor variable. It will get intolerable if we have multiple predictor variables. Fortunately, a little application of linear algebra will let us abstract away from a lot of the book-keeping details, and make multiple linear regression hardly more complicated than the simple version1. These notes will not remind you of how matrix algebra works. However, they will review some results about calculus with matrices, and about expectations and variances with vectors and matrices. Throughout, bold-faced letters will denote matrices, as a as opposed to a scalar a. 1 Least Squares in Matrix Form Our data consists of n paired observations of the predictor variable X and the response variable Y , i.e., (x1; y1);::: (xn; yn). We wish to fit the model Y = β0 + β1X + (1) where E [jX = x] = 0, Var [jX = x] = σ2, and is uncorrelated across mea- surements2. 1.1 The Basic Matrices Group all of the observations of the response into a single column (n×1) matrix y, 2 3 y1 6 y2 7 y = 6 7 (2) 6 . 7 4 . 5 yn Similarly, we group both the coefficients into a single vector (i.e., a 2 × 1 matrix) β β = 0 (3) β1 We'd also like to group the observations of the predictor variable together, but we need something which looks a little unusual at first: 2 3 1 x1 6 1 x2 7 x = 6 7 (4) 6 . 7 4 . 5 1 xn 1Historically, linear models with multiple predictors evolved before the use of matrix alge- bra for regression. You may imagine the resulting drudgery. 2When I need to also assume that is Gaussian, and strengthen \uncorrelated" to \inde- pendent", I'll say so. 11:55 Wednesday 14th October, 2015 3 1.2 Mean Squared Error This is an n×2 matrix, where the first column is always 1, and the second column contains the actual observations of X. We have this apparently redundant first column because of what it does for us when we multiply x by β: 2 3 β0 + β1x1 6 β0 + β1x2 7 xβ = 6 7 (5) 6 . 7 4 . 5 β0 + β1xn That is, xβ is the n × 1 matrix which contains the point predictions. The matrix x is sometimes called the design matrix. 1.2 Mean Squared Error At each data point, using the coefficients β results in some error of prediction, so we have n prediction errors. These form a vector: e(β) = y − xβ (6) (You can check that this subtracts an n × 1 matrix from an n × 1 matrix.) When we derived the least squares estimator, we used the mean squared error, n 1 X MSE(β) = e2(β) (7) n i i=1 How might we express this in terms of our matrices? I claim that the correct form is 1 MSE(β) = eT e (8) n To see this, look at what the matrix multiplication really involves: 2 3 e1 6 e2 7 [e e : : : e ] 6 7 (9) 1 2 n 6 . 7 4 . 5 en P 2 This, clearly equals i ei , so the MSE has the claimed form. Let us expand this a little for further use. 1 MSE(β) = eT e (10) n 1 = (y − xβ)T (y − xβ) (11) n 1 = (yT − βT xT )(y − xβ) (12) n 1 = yT y − yT xβ − βT xT y + βT xT xβ (13) n 11:55 Wednesday 14th October, 2015 4 1.3 Minimizing the MSE Notice that (yT xβ)T = βT xT y. Further notice that this is a 1 × 1 matrix, so yT xβ = βT xT y. Thus 1 MSE(β) = yT y − 2βT xT y + βT xT xβ (14) n 1.3 Minimizing the MSE First, we find the gradient of the MSE with respect to β: 1 rMSE(β = ryT y − 2rβT xT y + rβT xT xβ (15) n 1 = 0 − 2xT y + 2xT xβ (16) n 2 = xT xβ − xT y (17) n We now set this to zero at the optimum, βb: xT xβb − xT y = 0 (18) This equation, for the two-dimensional vector βb, corresponds to our pair of nor- ^ ^ mal or estimating equations for β0 and β1. Thus, it, too, is called an estimating equation. Solving, βb = (xT x)−1xT y (19) That is, we've got one matrix equation which gives us both coefficient estimates. If this is right, the equation we've got above should in fact reproduce the least-squares estimates we've already derived, which are of course ^ cXY xy − x¯y¯ β1 = = (20) 2 2 2 sX x − x¯ and ^ ^ β0 = y − β1x (21) Let's see if that's right. As a first step, let's introduce normalizing factors of 1=n into both the matrix products: βb = (n−1xT x)−1(n−1xT y) (22) Now let's look at the two factors in parentheses separately, from right to left. 2 3 y1 1 1 1 1 ::: 1 6 y2 7 xT y = 6 7 (23) x x : : : x 6 . 7 n n 1 2 n 4 . 5 yn P 1 yi = P i (24) n i xiyi y = (25) xy 11:55 Wednesday 14th October, 2015 5 Similarly for the other factor: P 1 T 1 n i xi x x = P P 2 (26) n n i xi i xi 1 x = (27) x x2 Now we need to take the inverse: −1 1 1 x2 −x xT x = (28) n x2 − x¯2 −x 1 1 x2 −x = 2 (29) sX −x 1 Let's multiply together the pieces. 2 T −1 T 1 x −x y (x x) x y = 2 (30) sX −x 1 xy 1 x2y − xxy = 2 (31) sX −xy + xy 2 2 1 (sX +x ¯ )y − x(cXY +x ¯y¯) = 2 (32) sX cXY 2 2 2 1 sxy +x ¯ y − xcXY − x y¯ = 2 (33) sX cXY " cXY # y − 2 x sX = cXY (34) 2 sX which is what it should be. So: n−1xT y is keeping track of y and xy, and n−1xT x keeps track of x and x2. The matrix inversion and multiplication then handles all the book- keeping to put these pieces together to get the appropriate (sample) variances, covariance, and intercepts. We don't have to remember that any more; we can just remember the one matrix equation, and then trust the linear algebra to take care of the details. 2 Fitted Values and Residuals Remember that when the coefficient vector is β, the point predictions for each data point are xβ. Thus the vector of fitted values, m\(x), or mb for short, is mb = xβb (35) Using our equation for βb, T −1 T mb = x(x x) x y (36) 11:55 Wednesday 14th October, 2015 6 Notice that the fitted values are linear in y. The matrix H ≡ x(xT x)−1xT (37) does not depend on y at all, but does control the fitted values: mb = Hy (38) If we repeat our experiment (survey, observation. ) many times at the same x, we get different y every time. But H does not change. The properties of the fitted values are thus largely determined by the properties of H. It thus deserves a name; it's usually called the hat matrix, for obvious reasons, or, if we want to sound more respectable, the influence matrix. Let's look at some of the properties of the hat matrix. 1. Influence Since H is not a function of y, we can easily verify that @mb i=@yj = th Hij. Thus, Hij is the rate at which the i fitted value changes as we vary the jth observation, the “influence” that observation has on that fitted value. 2. Symmetry It's easy to see that HT = H. 3. Idempotency A square matrix a is called idempotent3 when a2 = a (and so ak = a for any higher power k). Again, by writing out the multiplication, H2 = H, so it's idempotent. Idemopotency, Projection, Geometry Idempotency seems like the most obscure of these properties, but it's actually one of the more important. y and mb are n-dimensional vectors. If we project a vector u on to the line in the direction of the length-one vector v, we get vvT u (39) (Check the dimensions: u and v are both n × 1, so vT is 1 × n, and vT u is 1 × 1.) If we group the first two terms together, like so, (vvT )u (40) where vvT is the n × n project matrix or projection operator for that line. Since v is a unit vector, vT v = 1, and (vvT )(vvT ) = vvT (41) so the projection operator for the line is idempotent.
Load more

Recommended publications
  • Introducing the Game Design Matrix: a Step-By-Step Process for Creating Serious Games
    Air Force Institute of Technology AFIT Scholar Theses and Dissertations Student Graduate Works 3-2020 Introducing the Game Design Matrix: A Step-by-Step Process for Creating Serious Games Aaron J. Pendleton Follow this and additional works at: https://scholar.afit.edu/etd Part of the Educational Assessment, Evaluation, and Research Commons, Game Design Commons, and the Instructional Media Design Commons Recommended Citation Pendleton, Aaron J., "Introducing the Game Design Matrix: A Step-by-Step Process for Creating Serious Games" (2020). Theses and Dissertations. 4347. https://scholar.afit.edu/etd/4347 This Thesis is brought to you for free and open access by the Student Graduate Works at AFIT Scholar. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AFIT Scholar. For more information, please contact [email protected]. INTRODUCING THE GAME DESIGN MATRIX: A STEP-BY-STEP PROCESS FOR CREATING SERIOUS GAMES THESIS Aaron J. Pendleton, Captain, USAF AFIT-ENG-MS-20-M-054 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE INSTITUTE OF TECHNOLOGY Wright-Patterson Air Force Base, Ohio DISTRIBUTION STATEMENT A APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. The views expressed in this document are those of the author and do not reflect the official policy or position of the United States Air Force, the United States Department of Defense or the United States Government. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. AFIT-ENG-MS-20-M-054 INTRODUCING THE GAME DESIGN MATRIX: A STEP-BY-STEP PROCESS FOR CREATING LEARNING OBJECTIVE BASED SERIOUS GAMES THESIS Presented to the Faculty Department of Electrical and Computer Engineering Graduate School of Engineering and Management Air Force Institute of Technology Air University Air Education and Training Command in Partial Fulfillment of the Requirements for the Degree of Master of Science in Cyberspace Operations Aaron J.
    [Show full text]
  • Stat 5102 Notes: Regression
    Stat 5102 Notes: Regression Charles J. Geyer April 27, 2007 In these notes we do not use the “upper case letter means random, lower case letter means nonrandom” convention. Lower case normal weight letters (like x and β) indicate scalars (real variables). Lowercase bold weight letters (like x and β) indicate vectors. Upper case bold weight letters (like X) indicate matrices. 1 The Model The general linear model has the form p X yi = βjxij + ei (1.1) j=1 where i indexes individuals and j indexes different predictor variables. Ex- plicit use of (1.1) makes theory impossibly messy. We rewrite it as a vector equation y = Xβ + e, (1.2) where y is a vector whose components are yi, where X is a matrix whose components are xij, where β is a vector whose components are βj, and where e is a vector whose components are ei. Note that y and e have dimension n, but β has dimension p. The matrix X is called the design matrix or model matrix and has dimension n × p. As always in regression theory, we treat the predictor variables as non- random. So X is a nonrandom matrix, β is a nonrandom vector of unknown parameters. The only random quantities in (1.2) are e and y. As always in regression theory the errors ei are independent and identi- cally distributed mean zero normal. This is written as a vector equation e ∼ Normal(0, σ2I), where σ2 is another unknown parameter (the error variance) and I is the identity matrix. This implies y ∼ Normal(µ, σ2I), 1 where µ = Xβ.
    [Show full text]
  • Uncertainty of the Design and Covariance Matrices in Linear Statistical Model*
    Acta Univ. Palacki. Olomuc., Fac. rer. nat., Mathematica 48 (2009) 61–71 Uncertainty of the design and covariance matrices in linear statistical model* Lubomír KUBÁČEK 1, Jaroslav MAREK 2 Department of Mathematical Analysis and Applications of Mathematics, Faculty of Science, Palacký University, tř. 17. listopadu 12, 771 46 Olomouc, Czech Republic 1e-mail: [email protected] 2e-mail: [email protected] (Received January 15, 2009) Abstract The aim of the paper is to determine an influence of uncertainties in design and covariance matrices on estimators in linear regression model. Key words: Linear statistical model, uncertainty, design matrix, covariance matrix. 2000 Mathematics Subject Classification: 62J05 1 Introduction Uncertainties in entries of design and covariance matrices influence the variance of estimators and cause their bias. A problem occurs mainly in a linearization of nonlinear regression models, where the design matrix is created by deriva- tives of some functions. The question is how precise must these derivatives be. Uncertainties of covariance matrices must be suppressed under some reasonable bound as well. The aim of the paper is to give the simple rules which enables us to decide how many ciphers an entry of the mentioned matrices must be consisted of. *Supported by the Council of Czech Government MSM 6 198 959 214. 61 62 Lubomír KUBÁČEK, Jaroslav MAREK 2 Symbols used In the following text a linear regression model (in more detail cf. [2]) is denoted as k Y ∼n (Fβ, Σ), β ∈ R , (1) where Y is an n-dimensional random vector with the mean value E(Y) equal to Fβ and with the covariance matrix Var(Y)=Σ.ThesymbolRk means the k-dimensional linear vector space.
    [Show full text]
  • In This Segment, We Discuss a Little More the Mean Squared Error
    MITOCW | MITRES6_012S18_L20-04_300k In this segment, we discuss a little more the mean squared error. Consider some estimator. It can be any estimator, not just the sample mean. We can decompose the mean squared error as a sum of two terms. Where does this formula come from? Well, we know that for any random variable Z, this formula is valid. And if we let Z be equal to the difference between the estimator and the value that we're trying to estimate, then we obtain this formula here. The expected value of our random variable Z squared is equal to the variance of that random variable plus the square of its mean. Let us now rewrite these two terms in a more suggestive way. We first notice that theta is a constant. When you add or subtract the constant from a random variable, the variance does not change. So this term is the same as the variance of theta hat. This quantity here, we will call it the bias of the estimator. It tells us whether theta hat is systematically above or below than the unknown parameter theta that we're trying to estimate. And using this terminology, this term here is just equal to the square of the bias. So the mean squared error consists of two components, and these capture different aspects of an estimator's performance. Let us see what they are in a concrete setting. Suppose that we're estimating the unknown mean of some distribution, and that our estimator is the sample mean. In this case, the mean squared error is the variance, which we know to be sigma squared over n, plus the bias term.
    [Show full text]
  • The Concept of a Generalized Inverse for Matrices Was Introduced by Moore(1920)
    J. Japanese Soc. Comp. Statist. 2(1989), 1-7 THE MOORE-PENROSE INVERSE MATRIX POR THE BALANCED ANOVA MODELS Byung Chun Kim* and Ha Sik Sunwoo* ABSTRACT Since the design matrix of the balanced linear model with no interactions has special form, the general solution of the normal equations can be easily found. From the relationships between the minimum norm least squares solution and the Moore-Penrose inverse we can obtain the explicit form of the Moore-Penrose inverse X+ of the design matrix of the model y = XƒÀ + ƒÃ for the balanced model with no interaction. 1. Introduction The concept of a generalized inverse for matrices was introduced by Moore(1920). He developed it in the context of linear transformations from n-dimensional to m-dimensional vector space over a complex field with usual Euclidean norm. Unaware of Moore's work, Penrose(1955) established the existence and the uniqueness of the generalized inverse that satisfies the Moore's condition under L2 norm. It is commonly known as the Moore-Penrose inverse. In the early 1970's many statisticians, Albert (1972), Ben-Israel and Greville(1974), Graybill(1976),Lawson and Hanson(1974), Pringle and Raynor(1971), Rao and Mitra(1971), Rao(1975), Schmidt(1976), and Searle(1971, 1982), worked on this subject and yielded sev- eral texts that treat the mathematics and its application to statistics in some depth in detail. Especially, Shinnozaki et. al.(1972a, 1972b) have surveyed the general methods to find the Moore-Penrose inverse in two subject: direct methods and iterative methods. Also they tested their accuracy for some cases.
    [Show full text]
  • Minimum Mean Squared Error Model Averaging in Likelihood Models
    Statistica Sinica 26 (2016), 809-840 doi:http://dx.doi.org/10.5705/ss.202014.0067 MINIMUM MEAN SQUARED ERROR MODEL AVERAGING IN LIKELIHOOD MODELS Ali Charkhi1, Gerda Claeskens1 and Bruce E. Hansen2 1KU Leuven and 2University of Wisconsin, Madison Abstract: A data-driven method for frequentist model averaging weight choice is developed for general likelihood models. We propose to estimate the weights which minimize an estimator of the mean squared error of a weighted estimator in a local misspecification framework. We find that in general there is not a unique set of such weights, meaning that predictions from multiple model averaging estimators might not be identical. This holds in both the univariate and multivariate case. However, we show that a unique set of empirical weights is obtained if the candidate models are appropriately restricted. In particular a suitable class of models are the so-called singleton models where each model only includes one parameter from the candidate set. This restriction results in a drastic reduction in the computational cost of model averaging weight selection relative to methods which include weights for all possible parameter subsets. We investigate the performance of our methods in both linear models and generalized linear models, and illustrate the methods in two empirical applications. Key words and phrases: Frequentist model averaging, likelihood regression, local misspecification, mean squared error, weight choice. 1. Introduction We study a focused version of frequentist model averaging where the mean squared error plays a central role. Suppose we have a collection of models S 2 S to estimate a population quantity µ, this is the focus, leading to a set of estimators fµ^S : S 2 Sg.
    [Show full text]
  • Calibration: Calibrate Your Model
    DUE TODAYCOMPUTER FILES AND QUESTIONS for Assgn#6 Assignment # 6 Steady State Model Calibration: Calibrate your model. If you want to conduct a transient calibration, talk with me first. Perform calibration using UCODE. Be sure your report addresses global, graphical, and spatial measures of error as well as common sense. Consider more than one conceptual model and compare the results. Remember to make a prediction with your calibrated models and evaluate confidence in your prediction. Be sure to save your files because you will want to use them later in the semester. Suggested Calibration Report Outline Title Introduction describe the system to be calibrated (use portions of your previous report as appropriate) Observations to be matched in calibration type of observations locations of observations observed values uncertainty associated with observations explain specifically what the observation will be matched to in the model Calibration Procedure Evaluation of calibration residuals parameter values quality of calibrated model Calibrated model results Predictions Uncertainty associated with predictions Problems encountered, if any Comparison with uncalibrated model results Assessment of future work needed, if appropriate Summary/Conclusions Summary/Conclusions References submit the paper as hard copy and include it in your zip file of model input and output submit the model files (input and output for both simulations) in a zip file labeled: ASSGN6_LASTNAME.ZIP Calibration (Parameter Estimation, Optimization, Inversion, Regression) adjusting parameter values, boundary conditions, model conceptualization, and/or model construction until the model simulation matches field observations We calibrate because 1. the field measurements are not accurate reflecti ons of the model scale properties, and 2.
    [Show full text]
  • Bayes Estimator Recap - Example
    Recap Bayes Risk Consistency Summary Recap Bayes Risk Consistency Summary . Last Lecture . Biostatistics 602 - Statistical Inference Lecture 16 • What is a Bayes Estimator? Evaluation of Bayes Estimator • Is a Bayes Estimator the best unbiased estimator? . • Compared to other estimators, what are advantages of Bayes Estimator? Hyun Min Kang • What is conjugate family? • What are the conjugate families of Binomial, Poisson, and Normal distribution? March 14th, 2013 Hyun Min Kang Biostatistics 602 - Lecture 16 March 14th, 2013 1 / 28 Hyun Min Kang Biostatistics 602 - Lecture 16 March 14th, 2013 2 / 28 Recap Bayes Risk Consistency Summary Recap Bayes Risk Consistency Summary . Recap - Bayes Estimator Recap - Example • θ : parameter • π(θ) : prior distribution i.i.d. • X1, , Xn Bernoulli(p) • X θ fX(x θ) : sampling distribution ··· ∼ | ∼ | • π(p) Beta(α, β) • Posterior distribution of θ x ∼ | • α Prior guess : pˆ = α+β . Joint fX(x θ)π(θ) π(θ x) = = | • Posterior distribution : π(p x) Beta( xi + α, n xi + β) | Marginal m(x) | ∼ − • Bayes estimator ∑ ∑ m(x) = f(x θ)π(θ)dθ (Bayes’ rule) | α + x x n α α + β ∫ pˆ = i = i + α + β + n n α + β + n α + β α + β + n • Bayes Estimator of θ is ∑ ∑ E(θ x) = θπ(θ x)dθ | θ Ω | ∫ ∈ Hyun Min Kang Biostatistics 602 - Lecture 16 March 14th, 2013 3 / 28 Hyun Min Kang Biostatistics 602 - Lecture 16 March 14th, 2013 4 / 28 Recap Bayes Risk Consistency Summary Recap Bayes Risk Consistency Summary . Loss Function Optimality Loss Function Let L(θ, θˆ) be a function of θ and θˆ.
    [Show full text]
  • An Analysis of Random Design Linear Regression
    An Analysis of Random Design Linear Regression Daniel Hsu1,2, Sham M. Kakade2, and Tong Zhang1 1Department of Statistics, Rutgers University 2Department of Statistics, Wharton School, University of Pennsylvania Abstract The random design setting for linear regression concerns estimators based on a random sam- ple of covariate/response pairs. This work gives explicit bounds on the prediction error for the ordinary least squares estimator and the ridge regression estimator under mild assumptions on the covariate/response distributions. In particular, this work provides sharp results on the \out-of-sample" prediction error, as opposed to the \in-sample" (fixed design) error. Our anal- ysis also explicitly reveals the effect of noise vs. modeling errors. The approach reveals a close connection to the more traditional fixed design setting, and our methods make use of recent ad- vances in concentration inequalities (for vectors and matrices). We also describe an application of our results to fast least squares computations. 1 Introduction In the random design setting for linear regression, one is given pairs (X1;Y1);:::; (Xn;Yn) of co- variates and responses, sampled from a population, where each Xi are random vectors and Yi 2 R. These pairs are hypothesized to have the linear relationship > Yi = Xi β + i for some linear map β, where the i are noise terms. The goal of estimation in this setting is to find coefficients β^ based on these (Xi;Yi) pairs such that the expected prediction error on a new > 2 draw (X; Y ) from the population, measured as E[(X β^ − Y ) ], is as small as possible.
    [Show full text]
  • Kriging Models for Linear Networks and Non‐Euclidean Distances
    Received: 13 November 2017 | Accepted: 30 December 2017 DOI: 10.1111/2041-210X.12979 RESEARCH ARTICLE Kriging models for linear networks and non-Euclidean distances: Cautions and solutions Jay M. Ver Hoef Marine Mammal Laboratory, NOAA Fisheries Alaska Fisheries Science Center, Abstract Seattle, WA, USA 1. There are now many examples where ecological researchers used non-Euclidean Correspondence distance metrics in geostatistical models that were designed for Euclidean dis- Jay M. Ver Hoef tance, such as those used for kriging. This can lead to problems where predictions Email: [email protected] have negative variance estimates. Technically, this occurs because the spatial co- Handling Editor: Robert B. O’Hara variance matrix, which depends on the geostatistical models, is not guaranteed to be positive definite when non-Euclidean distance metrics are used. These are not permissible models, and should be avoided. 2. I give a quick review of kriging and illustrate the problem with several simulated examples, including locations on a circle, locations on a linear dichotomous net- work (such as might be used for streams), and locations on a linear trail or road network. I re-examine the linear network distance models from Ladle, Avgar, Wheatley, and Boyce (2017b, Methods in Ecology and Evolution, 8, 329) and show that they are not guaranteed to have a positive definite covariance matrix. 3. I introduce the reduced-rank method, also called a predictive-process model, for creating valid spatial covariance matrices with non-Euclidean distance metrics. It has an additional advantage of fast computation for large datasets. 4. I re-analysed the data of Ladle et al.
    [Show full text]
  • Linear Regression with Shuffled Data: Statistical and Computational
    3286 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 64, NO. 5, MAY 2018 Linear Regression With Shuffled Data: Statistical and Computational Limits of Permutation Recovery Ashwin Pananjady ,MartinJ.Wainwright,andThomasA.Courtade Abstract—Consider a noisy linear observation model with an permutation matrix, and w Rn is observation noise. We refer ∈ unknown permutation, based on observing y !∗ Ax∗ w, to the setting where w 0asthenoiseless case.Aswithlinear d = + where x∗ R is an unknown vector, !∗ is an unknown = ∈ n regression, there are two settings of interest, corresponding to n n permutation matrix, and w R is additive Gaussian noise.× We analyze the problem of∈ permutation recovery in a whether the design matrix is (i) deterministic (the fixed design random design setting in which the entries of matrix A are case), or (ii) random (the random design case). drawn independently from a standard Gaussian distribution and There are also two complementary problems of interest: establish sharp conditions on the signal-to-noise ratio, sample recovery of the unknown !∗,andrecoveryoftheunknownx∗. size n,anddimensiond under which !∗ is exactly and approx- In this paper, we focus on the former problem; the latter imately recoverable. On the computational front, we show that problem is also known as unlabelled sensing [5]. the maximum likelihood estimate of !∗ is NP-hard to compute for general d,whilealsoprovidingapolynomialtimealgorithm The observation model (1) is frequently encountered in when d 1. scenarios where there is uncertainty in the order in which = Index Terms—Correspondenceestimation,permutationrecov- measurements are taken. An illustrative example is that of ery, unlabeled sensing, information-theoretic bounds, random sampling in the presence of jitter [6], in which the uncertainty projections.
    [Show full text]
  • Week 7: Multiple Regression
    Week 7: Multiple Regression Brandon Stewart1 Princeton October 24, 26, 2016 1These slides are heavily influenced by Matt Blackwell, Adam Glynn, Jens Hainmueller and Danny Hidalgo. Stewart (Princeton) Week 7: Multiple Regression October 24, 26, 2016 1 / 145 Where We've Been and Where We're Going... Last Week I regression with two variables I omitted variables, multicollinearity, interactions This Week I Monday: F matrix form of linear regression I Wednesday: F hypothesis tests Next Week I break! I then ::: regression in social science Long Run I probability ! inference ! regression Questions? Stewart (Princeton) Week 7: Multiple Regression October 24, 26, 2016 2 / 145 1 Matrix Algebra Refresher 2 OLS in matrix form 3 OLS inference in matrix form 4 Inference via the Bootstrap 5 Some Technical Details 6 Fun With Weights 7 Appendix 8 Testing Hypotheses about Individual Coefficients 9 Testing Linear Hypotheses: A Simple Case 10 Testing Joint Significance 11 Testing Linear Hypotheses: The General Case 12 Fun With(out) Weights Stewart (Princeton) Week 7: Multiple Regression October 24, 26, 2016 3 / 145 Why Matrices and Vectors? Here's one way to write the full multiple regression model: yi = β0 + xi1β1 + xi2β2 + ··· + xiK βK + ui Notation is going to get needlessly messy as we add variables Matrices are clean, but they are like a foreign language You need to build intuitions over a long period of time (and they will return in Soc504) Reminder of Parameter Interpretation: β1 is the effect of a one-unit change in xi1 conditional on all other xik . We are going to review the key points quite quickly just to refresh the basics.
    [Show full text]