'Linear predictor' presentation slideshows

Regression in R

786 views • 53 slides

State Space Models

State Space Models. Let { x t : t ? T } and { y t : t ? T } denote two vector valued time series that satisfy the system of equations:. y t = A t x t + v t (The observation equation) x t = B t x t- 1 + u t (The state equation).

619 views • 47 slides

State Space Models

State Space Models. Let { x t : t ? T } and { y t : t ? T } denote two vector valued time series that satisfy the system of equations:. y t = A t x t + v t (The observation equation) x t = B t x t- 1 + u t (The state equation).

616 views • 47 slides

State Space Models

State Space Models. Let { x t : t ? T } and { y t : t ? T } denote two vector valued time series that satisfy the system of equations:. y t = A t x t + v t (The observation equation) x t = B t x t- 1 + u t (The state equation).

493 views • 47 slides

Results from LIGO's Second Search for Gravitational-Wave Bursts

Results from LIGO's Second Search for Gravitational-Wave Bursts. Patrick Sutton LIGO Laboratory, Caltech, for the LIGO Scientific Collaboration. Outline. Gravitational Wave Bursts: Goals Un-triggered Searches Scan entire data set looking for coincident signals in each detector

606 views • 46 slides

Contents:

Analysis of ordinal repeated categorical response data by using marginal model (Maximum likelihood approach) by Abdul Salam Instructor: K.C. Carriere Stat 562. Contents:. Introduction Background of data Objective of the study Basic theory Marginal model Model fitting using ML SAS Codes

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Today: Lab 9ab due after lecture: CEQ Monday: Quizz 11: review

562 views • 39 slides

Linear Prediction

Linear Prediction . Problem: Forward Prediction Observing Predict Backward Prediction Observing Predict. Forward Linear Prediction. Problem: Forward Prediction Observing the past Predict the future i.e. find the predictor filter taps w f,1 , w f,2 ,...,w f,M.

1.28k views • 37 slides

Advanced Section # 5 : Generalized Linear Models: Logistic Regression and Beyond

Advanced Section # 5 : Generalized Linear Models: Logistic Regression and Beyond. Marios Mattheakis and Pavlos Protopapas. Outline. Generalized Linear Models (GLMs): Motivation. Linear Regression Model (Recap): jumping-off point Generalize the Linear Model:

378 views • 37 slides

Flexible Survival Modeling in SAS

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16. Mean Square Estimation

16. Mean Square Estimation. Given some information that is related to an unknown quantity of interest, the problem is to obtain a good estimate for the unknown in terms of the observed data. Suppose represent a sequence of random

340 views • 33 slides

Agenda

Two Distribution Families for Modelling Over- and Underdispersed Binomial Frequencies Feirer V. , Hirn U., Friedl H., Bauer W. Institute for Paper, Pulp and Fiber Technology & Institute for Statistics Graz University of Technology. Agenda. Motivation Generalized Linear Models

465 views • 33 slides

GEE Approach

627 views • 29 slides

Huffman Coding

Huffman Coding. Vida Movahedi October 2006. Contents. A simple example Definitions Huffman Coding Algorithm Image Compression. A simple example. Suppose we have a message consisting of 5 symbols, e.g. [ ??????????]

633 views • 24 slides

R-INLA package

R-INLA package. Disease mapping in Germany. Disease mapping. Larynx cancer mortality count in Germany, 1986 to 1990 Spatial resolution: 544 regions. BYM Model. For each region: Assume the data to be conditionally independent Poisson random variables: Linear predictor:. Linear predictor.

351 views • 9 slides