Modeling and Multivariate Methods - SAS

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376 Performing Time Series Analysis Chapter 14 Smoothing Models Model Comparison Table The model comparison table works like its ARIMA counterpart by accumulating statistics on the models you specify. Fitting Notes A regression model with serially correlated errors can be specified by including regressors in the model and not specifying any polynomial orders. Intervention analysis can also be conducted, but prewhitening is no longer meaningful. Currently, the transfer function model platform has limited capability of supporting missing values. Smoothing Models JMP offers a variety of smoothing techniques.
Chapter 14 Performing Time Series Analysis 377 Smoothing Models Smoothing models represent the evolution of a time series by the model: y t = μ t + β t t+ s() t + a t where μ t is the time-varying mean term, β t is the time-varying slope term, st () is one of the s time-varying seasonal terms, a t are the random shocks. β t 0 Models without a trend have = and nonseasonal models have st () = 0 . The estimators for these time-varying terms are L t smoothed level that estimatesμ t T t is a smoothed trend that estimatesβ t S t– j for j = 01… , , , s – 1 are the estimates of the st (). Each smoothing model defines a set of recursive smoothing equations that describes the evolution of these estimators. The smoothing equations are written in terms of model parameters called smoothing weights. They are α, the level smoothing weight γ, the trend smoothing weight ϕ, the trend damping weight δ, the seasonal smoothing weight. While these parameters enter each model in a different way (or not at all), they have the common property that larger weights give more influence to recent data while smaller weights give less influence to recent data. Each smoothing model has an ARIMA model equivalent. You may not be able to specify the equivalent ARIMA model using the ARIMA command because some smoothing models intrinsically constrain the ARIMA model parameters in ways the ARIMA command will not allow. Simple Moving Average A simple moving average model (SMA) produces forecasted values that are equal to the average of consecutive observations in a time window. The forecasts can be uncentered or centered in the time window. To fit a simple moving average model, select Smoothing > Simple Moving Average. A window appears with the following options: Enter smoothing window width Enter the width of the smoothing window. Centered Choose whether to center the forecasted values.
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Version 10 Modeling and Multivariat
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INDIRECT OR CONSEQUENTIAL DAMAGES O
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6 Emphasis Options for Standard Lea
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8 Models with Crossed, Interaction,
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10 8 Analyzing Screening Designs Us
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12 Examining the Residuals . . . .
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14 Response Frequencies . . . . . .
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16 19 Analyzing Principal Component
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18 Interpreting the Profiles . . .
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20 Discriminant Analysis . . . . .
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Contents Book Conventions. . . . .
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24 Learn about JMP Chapter 1 Book C
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26 Learn about JMP Chapter 1 Book C
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28 Learn about JMP Chapter 1 Additi
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30 Learn about JMP Chapter 1 Additi
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Contents Launch the Fit Model Platf
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Contents Example Using Standard Lea
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56 Fitting Standard Least Squares M
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Contents Overview of Stepwise Regre
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136 Fitting Stepwise Regression Mod
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Contents Example of a Multiple Resp
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158 Fitting Multiple Response Model
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Contents Overview of Generalized Li
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180 Fitting Generalized Linear Mode
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Contents Introduction to Logistic M
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200 Performing Logistic Regression
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Contents Overview of the Screening
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232 Analyzing Screening Designs Cha
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Contents Introduction to the Nonlin
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244 Performing Nonlinear Regression
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Contents Overview of Neural Network
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280 Creating Neural Networks Chapte
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Contents Launching the Platform. .
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296 Modeling Relationships With Gau
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Contents Overview of the Loglinear
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306 Fitting Dispersion Effects with
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Contents Introduction to Partitioni
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318 Recursively Partitioning Data C
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Contents Launch the Multivariate Pl
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444 Correlations and Multivariate T
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Contents Introduction to Clustering
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462 Clustering Data Chapter 18 The
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464 Clustering Data Chapter 18 Hier
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470 Clustering Data Chapter 18 K-Me
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474 Clustering Data Chapter 18 Norm
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476 Clustering Data Chapter 18 Norm
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478 Clustering Data Chapter 18 Self
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480 Clustering Data Chapter 18 Self
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Contents Principal Components. . .
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484 Analyzing Principal Components
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Contents Introduction . . . . . . .
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494 Performing Discriminant Analysi
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Contents Overview of the Partial Le
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Contents Introduction to Item Respo
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Contents Surface Plots . . . . . .
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538 Plotting Surfaces Chapter 23 La
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544 Plotting Surfaces Chapter 23 Th
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Contents Introduction to Profiling
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556 Visualizing, Optimizing, and Si
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640 References Becker, R.A. and Cle
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642 References Draper, N. and Smith
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646 References Matsumoto, M. and Ni
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648 References Rawlings, J.O. (1988
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650 References Umetrics (1995), Mul
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Contents The Response Models . . .
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654 Statistical Details Appendix A
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690 Index Birth Death Subset.jmp 46
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692 Index Ellipses Transparency 451
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694 Index test for curvature 46 Kru
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696 Index Customizing 267 Nonlinear
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698 Index reliability analysis 453-
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700 Index Stable 363 Stable Inverti
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702 Index
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Modeling and Multivariate Methods - SAS

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