Question One: [20 marks] Question Five: [5 marks] Which is the best predicting model from Assignments Two and Three? Justify your choice. STATS 326 Applied Time Series ASSIGNMENT THREE Due: 2 May 2019, 11.00 am For Questions Three and Four, use the best predicting model from Questions One and Two. (Worth 6% of your final […]
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COMP330 Assignment 2 2018- Transformations Due: Week 8, Friday May 3, 2018 6:00pm. Value: 10%. Marked out of 100. Objective The purpose of this assignment is to test your knowledge of: • 2D & 3D transformations: translation, rotation, scale and shear • Homogeneous matrices • Nested Coordinate frames Instructions Throughout this assignment, all diagrams that
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STA 104 Exam I Project, due Friday, May 3th in lecture before 9:50AM Read the following instructions carefully: • You may work in a group of two, or by yourself. • You are not allowed to discuss the questions with anyone other than the instructor or TA and your group mate. • Any outside help
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ECE 213 Spring 2019 Continuous-Time Signals & Systems Computing Exercises Exercise SS-C2: Using Laplace to find the impulse response [20pts] Consider the RLC circuit shown to the right. The “input” voltage is x(t), and the “output” voltage is y(t). We will use the Laplace transform to find the impulse response for different values of R,
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ECE 213 Spring 2019 Continuous-Time Signals & Systems Computing Exercises Exercise SS-C1: Understanding Convolution, part I [20pts] Consider the RC circuit shown to the right, with R = 10Ω and C = 2mF. The “input” voltage is x(t), and the “output” voltage is y(t). For now, the input voltage is given by: ⎛t −τ ⎞
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STA 104 Exam I Project, due Friday, May 3th in lecture before 9:50AM Read the following instructions carefully: • You may work in a group of two, or by yourself. • You are not allowed to discuss the questions with anyone other than the instructor or TA and your group mate. • Any outside help
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 MAT 4376F/5313F R – Value-at-Risk, Expected Shortfall • Value-at-Risk • Comparison of parametric and nonparametric method for Exponential distribution n=1000; • lambda=2; • p=seq(1,n,by=1)/(n+1); • true.VAR=-log(p)/lambda; • plot(p,true.VAR,type=”l”); • X=rexp(n,lambda); • hatlambda=1/mean(X); • parametric.VAR=-log(p)/hatlambda; • points(p,parametric.VAR,type=”l”,col=”blue”) • nonparametric.VAR=sort(X,decreasing=TRUE); • points(p,nonparametric.VAR,type=”p”,col=”red”); • • p0=0.05; • -log(p0)/lambda; -log(p0)/hatlambda; • index=floor(n*p0); nonparametric.VAR[index+1]; • • Comparison of
 MAT 4376F/5313F R – Fitting multivariate distributions library(mvtnorm); library(MASS); library(QRM); library(evir); # Simulate bivariate normal data mu
 MAT 4376F/5313F R – Time series. Backtesting GARCH models • Load a package: library(tseries); • • GARCH analysis of EU stock exchange. plot(EuStockMarkets) ; • MyTS
Algorithms 3027 Assignment 3 The University of Sydney 2019 Semester 1 School of Computer Science This assignment is for COMP3027 students only. If you have printed this using a black-and-white printer, look at the PDF on a computer to see the colors below. Description Congratulations! You have been hired as the lead developer of the