Understanding Statistical Methods Through Elliptical Geometry
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Estimating Confidence Regions of Common Measures of (Baseline, Treatment Effect) On
Estimating confidence regions of common measures of (baseline, treatment effect) on dichotomous outcome of a population Li Yin1 and Xiaoqin Wang2* 1Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Box 281, SE- 171 77, Stockholm, Sweden 2Department of Electronics, Mathematics and Natural Sciences, University of Gävle, SE-801 76, Gävle, Sweden (Email: [email protected]). *Corresponding author Abstract In this article we estimate confidence regions of the common measures of (baseline, treatment effect) in observational studies, where the measure of baseline is baseline risk or baseline odds while the measure of treatment effect is odds ratio, risk difference, risk ratio or attributable fraction, and where confounding is controlled in estimation of both baseline and treatment effect. To avoid high complexity of the normal approximation method and the parametric or non-parametric bootstrap method, we obtain confidence regions for measures of (baseline, treatment effect) by generating approximate distributions of the ML estimates of these measures based on one logistic model. Keywords: baseline measure; effect measure; confidence region; logistic model 1 Introduction Suppose that one conducts a randomized trial to investigate the effect of a dichotomous treatment z on a dichotomous outcome y of certain population, where = 0, 1 indicate the 푧 1 active respective control treatments while = 0, 1 indicate positive respective negative outcomes. With a sufficiently large sample,푦 covariates are essentially unassociated with treatments z and thus are not confounders. Let R = pr( = 1 | ) be the risk of = 1 given 푧 z. Then R is marginal with respect to covariates and thus푦 conditional푧 on treatment푦 z only, so 푧 R is also called marginal risk. -
Chapter 11. Three Dimensional Analytic Geometry and Vectors
Chapter 11. Three dimensional analytic geometry and vectors. Section 11.5 Quadric surfaces. Curves in R2 : x2 y2 ellipse + =1 a2 b2 x2 y2 hyperbola − =1 a2 b2 parabola y = ax2 or x = by2 A quadric surface is the graph of a second degree equation in three variables. The most general such equation is Ax2 + By2 + Cz2 + Dxy + Exz + F yz + Gx + Hy + Iz + J =0, where A, B, C, ..., J are constants. By translation and rotation the equation can be brought into one of two standard forms Ax2 + By2 + Cz2 + J =0 or Ax2 + By2 + Iz =0 In order to sketch the graph of a quadric surface, it is useful to determine the curves of intersection of the surface with planes parallel to the coordinate planes. These curves are called traces of the surface. Ellipsoids The quadric surface with equation x2 y2 z2 + + =1 a2 b2 c2 is called an ellipsoid because all of its traces are ellipses. 2 1 x y 3 2 1 z ±1 ±2 ±3 ±1 ±2 The six intercepts of the ellipsoid are (±a, 0, 0), (0, ±b, 0), and (0, 0, ±c) and the ellipsoid lies in the box |x| ≤ a, |y| ≤ b, |z| ≤ c Since the ellipsoid involves only even powers of x, y, and z, the ellipsoid is symmetric with respect to each coordinate plane. Example 1. Find the traces of the surface 4x2 +9y2 + 36z2 = 36 1 in the planes x = k, y = k, and z = k. Identify the surface and sketch it. Hyperboloids Hyperboloid of one sheet. The quadric surface with equations x2 y2 z2 1. -
Radar Back-Scattering from Non-Spherical Scatterers
REPORT OF INVESTIGATION NO. 28 STATE OF ILLINOIS WILLIAM G. STRATION, Governor DEPARTMENT OF REGISTRATION AND EDUCATION VERA M. BINKS, Director RADAR BACK-SCATTERING FROM NON-SPHERICAL SCATTERERS PART 1 CROSS-SECTIONS OF CONDUCTING PROLATES AND SPHEROIDAL FUNCTIONS PART 11 CROSS-SECTIONS FROM NON-SPHERICAL RAINDROPS BY Prem N. Mathur and Eugam A. Mueller STATE WATER SURVEY DIVISION A. M. BUSWELL, Chief URBANA. ILLINOIS (Printed by authority of State of Illinois} REPORT OF INVESTIGATION NO. 28 1955 STATE OF ILLINOIS WILLIAM G. STRATTON, Governor DEPARTMENT OF REGISTRATION AND EDUCATION VERA M. BINKS, Director RADAR BACK-SCATTERING FROM NON-SPHERICAL SCATTERERS PART 1 CROSS-SECTIONS OF CONDUCTING PROLATES AND SPHEROIDAL FUNCTIONS PART 11 CROSS-SECTIONS FROM NON-SPHERICAL RAINDROPS BY Prem N. Mathur and Eugene A. Mueller STATE WATER SURVEY DIVISION A. M. BUSWELL, Chief URBANA, ILLINOIS (Printed by authority of State of Illinois) Definitions of Terms Part I semi minor axis of spheroid semi major axis of spheroid wavelength of incident field = a measure of size of particle prolate spheroidal coordinates = eccentricity of ellipse = angular spheroidal functions = Legendse polynomials = expansion coefficients = radial spheroidal functions = spherical Bessel functions = electric field vector = magnetic field vector = back scattering cross section = geometric back scattering cross section Part II = semi minor axis of spheroid = semi major axis of spheroid = Poynting vector = measure of size of spheroid = wavelength of the radiation = back scattering -
Theory Pest.Pdf
Theory for PEST Users Zhulu Lin Dept. of Crop and Soil Sciences University of Georgia, Athens, GA 30602 [email protected] October 19, 2005 Contents 1 Linear Model Theory and Terminology 2 1.1 Amotivationexample ...................... 2 1.2 General linear regression model . 3 1.3 Parameterestimation. 6 1.3.1 Ordinary least squares estimator . 6 1.3.2 Weighted least squares estimator . 7 1.4 Uncertaintyanalysis ....................... 9 1.4.1 Variance-covariance matrix of βˆ and estimation of σ2 . 9 1.4.2 Confidence interval for βj ................ 9 1.4.3 Confidence region for β ................. 10 1.4.4 Confidence interval for E(y0) .............. 10 1.4.5 Prediction interval for a future observation y0 ..... 11 2 Nonlinear regression model 13 2.1 Linearapproximation. 13 2.2 Nonlinear least squares estimator . 14 2.3 Numericalmethods ........................ 14 2.3.1 Steepest Descent algorithm . 16 2.3.2 Gauss-Newton algorithm . 16 2.3.3 Levenberg-Marquardt algorithm . 18 2.3.4 Newton’smethods . 19 1 2.4 Uncertainty analysis . 22 2.4.1 Confidence intervals for parameter and model prediction 22 2.4.2 Nonlinear calibration-constrained method . 23 3 Miscellaneous 27 3.1 Convergence criteria . 27 3.2 Derivatives computation . 28 3.3 Parameter estimation of compartmental models . 28 3.4 Initial values and prior information . 29 3.5 Parametertransformation . 30 1 Linear Model Theory and Terminology Before discussing parameter estimation and uncertainty analysis for nonlin- ear models, we need to review linear model theory as many of the ideas and methods of estimation and analysis (inference) in nonlinear models are essentially linear methods applied to a linear approximate of the nonlinear models. -
Random Vectors
Random Vectors x is a p×1 random vector with a pdf probability density function f(x): Rp→R. Many books write X for the random vector and X=x for the realization of its value. E[X]= ∫ x f.(x) dx = µ Theorem: E[Ax+b]= AE[x]+b Covariance Matrix E[(x-µ)(x-µ)’]=var(x)=Σ (note the location of transpose) Theorem: Σ=E[xx’]-µµ’ If y is a random variable: covariance C(x,y)= E[(x-µ)(y-ν)’] Theorem: For constants a, A, var (a’x)=a’Σa, var(Ax+b)=AΣA’, C(x,x)=Σ, C(x,y)=C(y,x)’ Theorem: If x, y are independent RVs, then C(x,y)=0, but not conversely. Theorem: Let x,y have same dimension, then var(x+y)=var(x)+var(y)+C(x,y)+C(y,x) Normal Random Vectors The Central Limit Theorem says that if a focal random variable x consists of the sum of many other independent random variables, then the focal random variable will asymptotically have a 2 distribution that is basically of the form e−x , which we call “normal” because it is so common. 2 ⎛ x−µ ⎞ 1 − / 2 −(x−µ) (x−µ) / 2 1 ⎜ ⎟ 1 2 Normal random variable has pdf f (x) = e ⎝ σ ⎠ = e σ 2πσ2 2πσ2 Denote x p×1 normal random variable with pdf 1 −1 f (x) = e−(x−µ)'Σ (x−µ) (2π)p / 2 Σ 1/ 2 where µ is the mean vector and Σ is the covariance matrix: x~Np(µ,Σ). -
John Ellipsoid 5.1 John Ellipsoid
CSE 599: Interplay between Convex Optimization and Geometry Winter 2018 Lecture 5: John Ellipsoid Lecturer: Yin Tat Lee Disclaimer: Please tell me any mistake you noticed. The algorithm at the end is unpublished. Feel free to contact me for collaboration. 5.1 John Ellipsoid In the last lecture, we discussed that any convex set is very close to anp ellipsoid in probabilistic sense. More precisely, after renormalization by covariance matrix, we have kxk2 = n ± Θ(1) with high probability. In this lecture, we will talk about how convex set is close to an ellipsoid in a strict sense. If the convex set is isotropic, it is close to a sphere as follows: Theorem 5.1.1. Let K be a convex body in Rn in isotropic position. Then, rn + 1 B ⊆ K ⊆ pn(n + 1)B : n n n Roughly speaking, this says that any convex set can be approximated by an ellipsoid by a n factor. This result has a lot of applications. Although the bound is tight, making a body isotropic is pretty time- consuming. In fact, making a body isotropic is the current bottleneck for obtaining faster algorithm for sampling in convex sets. Currently, it can only be done in O∗(n4) membership oracle plus O∗(n5) total time. Problem 5.1.2. Find a faster algorithm to approximate the covariance matrix of a convex set. In this lecture, we consider another popular position of a convex set called John position and its correspond- ing ellipsoid is called John ellipsoid. Definition 5.1.3. Given a convex set K. -
Automatic Estimation of Sphere Centers from Images of Calibrated Cameras
Automatic Estimation of Sphere Centers from Images of Calibrated Cameras Levente Hajder 1 , Tekla Toth´ 1 and Zoltan´ Pusztai 1 1Department of Algorithms and Their Applications, Eotv¨ os¨ Lorand´ University, Pazm´ any´ Peter´ stny. 1/C, Budapest, Hungary, H-1117 fhajder,tekla.toth,[email protected] Keywords: Ellipse Detection, Spatial Estimation, Calibrated Camera, 3D Computer Vision Abstract: Calibration of devices with different modalities is a key problem in robotic vision. Regular spatial objects, such as planes, are frequently used for this task. This paper deals with the automatic detection of ellipses in camera images, as well as to estimate the 3D position of the spheres corresponding to the detected 2D ellipses. We propose two novel methods to (i) detect an ellipse in camera images and (ii) estimate the spatial location of the corresponding sphere if its size is known. The algorithms are tested both quantitatively and qualitatively. They are applied for calibrating the sensor system of autonomous cars equipped with digital cameras, depth sensors and LiDAR devices. 1 INTRODUCTION putation and memory costs. Probabilistic Hough Transform (PHT) is a variant of the classical HT: it Ellipse fitting in images has been a long researched randomly selects a small subset of the edge points problem in computer vision for many decades (Prof- which is used as input for HT (Kiryati et al., 1991). fitt, 1982). Ellipses can be used for camera calibra- The 5D parameter space can be divided into two tion (Ji and Hu, 2001; Heikkila, 2000), estimating the pieces. First, the ellipse center is estimated, then the position of parts in an assembly system (Shin et al., remaining three parameters are found in the second 2011) or for defect detection in printed circuit boards stage (Yuen et al., 1989; Tsuji and Matsumoto, 1978). -
Exploding the Ellipse Arnold Good
Exploding the Ellipse Arnold Good Mathematics Teacher, March 1999, Volume 92, Number 3, pp. 186–188 Mathematics Teacher is a publication of the National Council of Teachers of Mathematics (NCTM). More than 200 books, videos, software, posters, and research reports are available through NCTM’S publication program. Individual members receive a 20% reduction off the list price. For more information on membership in the NCTM, please call or write: NCTM Headquarters Office 1906 Association Drive Reston, Virginia 20191-9988 Phone: (703) 620-9840 Fax: (703) 476-2970 Internet: http://www.nctm.org E-mail: [email protected] Article reprinted with permission from Mathematics Teacher, copyright May 1991 by the National Council of Teachers of Mathematics. All rights reserved. Arnold Good, Framingham State College, Framingham, MA 01701, is experimenting with a new approach to teaching second-year calculus that stresses sequences and series over integration techniques. eaders are advised to proceed with caution. Those with a weak heart may wish to consult a physician first. What we are about to do is explode an ellipse. This Rrisky business is not often undertaken by the professional mathematician, whose polytechnic endeavors are usually limited to encounters with administrators. Ellipses of the standard form of x2 y2 1 5 1, a2 b2 where a > b, are not suitable for exploding because they just move out of view as they explode. Hence, before the ellipse explodes, we must secure it in the neighborhood of the origin by translating the left vertex to the origin and anchoring the left focus to a point on the x-axis. -
Algebraic Study of the Apollonius Circle of Three Ellipses
EWCG 2005, Eindhoven, March 9–11, 2005 Algebraic Study of the Apollonius Circle of Three Ellipses Ioannis Z. Emiris∗ George M. Tzoumas∗ Abstract methods readily extend to arbitrary inputs. The algorithms for the Apollonius diagram of el- We study the external tritangent Apollonius (or lipses typically use the following 2 main predicates. Voronoi) circle to three ellipses. This problem arises Further predicates are examined in [4]. when one wishes to compute the Apollonius (or (1) given two ellipses and a point outside of both, Voronoi) diagram of a set of ellipses, but is also of decide which is the ellipse closest to the point, independent interest in enumerative geometry. This under the Euclidean metric paper is restricted to non-intersecting ellipses, but the extension to arbitrary ellipses is possible. (2) given 4 ellipses, decide the relative position of the We propose an efficient representation of the dis- fourth one with respect to the external tritangent tance between a point and an ellipse by considering a Apollonius circle of the first three parametric circle tangent to an ellipse. The distance For predicate (1) we consider a circle, centered at the of its center to the ellipse is expressed by requiring point, with unknown radius, which corresponds to the that their characteristic polynomial have at least one distance to be compared. A tangency point between multiple real root. We study the complexity of the the circle and the ellipse exists iff the discriminant tritangent Apollonius circle problem, using the above of the corresponding pencil’s determinant vanishes. representation for the distance, as well as sparse (or Hence we arrive at a method using algebraic numbers toric) elimination. -
2.3 Conic Sections: Ellipse
2.3 Conic Sections: Ellipse Ellipse: (locus definition) set of all points (x, y) in the plane such that the sum of each of the distances from F1 and F2 is d. Standard Form of an Ellipse: Horizontal Ellipse Vertical Ellipse 22 22 (xh−−) ( yk) (xh−−) ( yk) +=1 +=1 ab22 ba22 center = (hk, ) 2a = length of major axis 2b = length of minor axis c = distance from center to focus cab222=− c eccentricity e = ( 01<<e the closer to 0 the more circular) a 22 (xy+12) ( −) Ex. Graph +=1 925 Center: (−1, 2 ) Endpoints of Major Axis: (-1, 7) & ( -1, -3) Endpoints of Minor Axis: (-4, -2) & (2, 2) Foci: (-1, 6) & (-1, -2) Eccentricity: 4/5 Ex. Graph xyxy22+4224330−++= x2 + 4y2 − 2x + 24y + 33 = 0 x2 − 2x + 4y2 + 24y = −33 x2 − 2x + 4( y2 + 6y) = −33 x2 − 2x +12 + 4( y2 + 6y + 32 ) = −33+1+ 4(9) (x −1)2 + 4( y + 3)2 = 4 (x −1)2 + 4( y + 3)2 4 = 4 4 (x −1)2 ( y + 3)2 + = 1 4 1 Homework: In Exercises 1-8, graph the ellipse. Find the center, the lines that contain the major and minor axes, the vertices, the endpoints of the minor axis, the foci, and the eccentricity. x2 y2 x2 y2 1. + = 1 2. + = 1 225 16 36 49 (x − 4)2 ( y + 5)2 (x +11)2 ( y + 7)2 3. + = 1 4. + = 1 25 64 1 25 (x − 2)2 ( y − 7)2 (x +1)2 ( y + 9)2 5. + = 1 6. + = 1 14 7 16 81 (x + 8)2 ( y −1)2 (x − 6)2 ( y − 8)2 7. -
Introduction to Aberrations OPTI 518 Lecture 13
Introduction to aberrations OPTI 518 Lecture 13 Prof. Jose Sasian OPTI 518 Topics • Aspheric surfaces • Stop shifting • Field curve concept Prof. Jose Sasian OPTI 518 Aspheric Surfaces • Meaning not spherical • Conic surfaces: Sphere, prolate ellipsoid, hyperboloid, paraboloid, oblate ellipsoid or spheroid • Cartesian Ovals • Polynomial surfaces • Infinite possibilities for an aspheric surface • Ray tracing for quadric surfaces uses closed formulas; for other surfaces iterative algorithms are used Prof. Jose Sasian OPTI 518 Aspheric surfaces The concept of the sag of a surface 2 cS 4 6 8 10 ZS ASASASAS4 6 8 10 ... 1 1 K ( c2 1 ) 2 S Sxy222 K 2 K is the conic constant K=0, sphere K=-1, parabola C is 1/r where r is the radius of curvature; K is the K<-1, hyperola conic constant (the eccentricity squared); -1<K<0, prolate ellipsoid A’s are aspheric coefficients K>0, oblate ellipsoid Prof. Jose Sasian OPTI 518 Conic surfaces focal properties • Focal points for Ellipsoid case mirrors Hyperboloid case Oblate ellipsoid • Focal points of lenses K n2 Hecht-Zajac Optics Prof. Jose Sasian OPTI 518 Refraction at a spherical surface Aspheric surface description 2 cS 4 6 8 10 ZS ASASASAS4 6 8 10 ... 1 1 K ( c2 1 ) 2 S 1122 2222 22 y x Aicrasphe y 1 x 4 K y Zx 28rr Prof. Jose Sasian OTI 518P Cartesian Ovals ln l'n' Cte . Prof. Jose Sasian OPTI 518 Aspheric cap Aspheric surface The aspheric surface can be thought of as comprising a base sphere and an aspheric cap Cap Spherical base surface Prof. -
Geodetic Position Computations
GEODETIC POSITION COMPUTATIONS E. J. KRAKIWSKY D. B. THOMSON February 1974 TECHNICALLECTURE NOTES REPORT NO.NO. 21739 PREFACE In order to make our extensive series of lecture notes more readily available, we have scanned the old master copies and produced electronic versions in Portable Document Format. The quality of the images varies depending on the quality of the originals. The images have not been converted to searchable text. GEODETIC POSITION COMPUTATIONS E.J. Krakiwsky D.B. Thomson Department of Geodesy and Geomatics Engineering University of New Brunswick P.O. Box 4400 Fredericton. N .B. Canada E3B5A3 February 197 4 Latest Reprinting December 1995 PREFACE The purpose of these notes is to give the theory and use of some methods of computing the geodetic positions of points on a reference ellipsoid and on the terrain. Justification for the first three sections o{ these lecture notes, which are concerned with the classical problem of "cCDputation of geodetic positions on the surface of an ellipsoid" is not easy to come by. It can onl.y be stated that the attempt has been to produce a self contained package , cont8.i.ning the complete development of same representative methods that exist in the literature. The last section is an introduction to three dimensional computation methods , and is offered as an alternative to the classical approach. Several problems, and their respective solutions, are presented. The approach t~en herein is to perform complete derivations, thus stqing awrq f'rcm the practice of giving a list of for11111lae to use in the solution of' a problem.