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Course: SS 3861, Fall 2009School: UWO
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Sciences 1 Statistical 3861B Todays Topics 1. Model identication 2. Modelling philosophies 3. Identication methods 4. Examples Model identication The process of choosing a proper model The rst step of a model construction Also the most dicult and important step Modelling philosophies Randomness (uncertainty) Natural uncertainty Parameter uncertainty 2 Model uncertainty (model misspecication) Model...
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Sciences 1
Statistical 3861B
Todays Topics 1. Model identication 2. Modelling philosophies 3. Identication methods 4. Examples
Model identication
The process of choosing a proper model The rst step of a model construction Also the most dicult and important step
Modelling philosophies
Randomness (uncertainty) Natural uncertainty Parameter uncertainty
2
Model uncertainty (model misspecication)
Model discrimination There is a model overtting issue
Modelling principles
Model building: Chapters 6,7,8,12
Identication methods
Know the data (background information) and Quantity quality of data? Enough sample size? (time series contains less information than iid data) The ratio of sample size to number of parameters
3
Did an intervention(s) occur?
Intervention is also refer to structure change
Intervention analysis Plot of the data Autocorrelation describes linear dependence Seasonality Chapter 12 Non...
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1Statistical Sciences 3861BToday's Topics 1. Review 2. Examples 3. Chapter 6: Parameter estimation 4. Yule-Walker estimator 5. Some estimation theory Review Model identification: The process of choosing a proper model The most difficult and impo
UWO - SS - 3861
1Statistical Sciences 3861BTodays Topics 1. Review 2. Yule-Walker estimator 3. Some estimation theory 4. Eciency of estimatorsReview Chapter 6 will do Find a criteria to choose proper p and q Estimate 1, . . . , p and 1, . . . , q Estimate the
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1Statistical Sciences 3861BToday's Topics 1. Review 2. Model discrimination using AIC 3. Examples of ARMA parameter estimation 4. Purposes of Chapter 7 5. Overfitting Review For time series, L() = f (x1, . . . , xN ) = f1(x1)f2(x2|x1) fN (xN
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