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Least squares linear lags and limited memory filters
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Discenza, Joseph H. |
| Copyright Year | 1987 |
| Description | Pure autoregressive (AR) models which are linear in the short term, that is when a variable can be predicted by linear regression on a limited number of past observations, are discussed. When evenly spaced observations are available, a fixed set of AR coefficients can be calculated independent of the data. For filtering purposes, such a lag structure can be implemented recursively with an efficient algorithm. The method of computing variance recursively is also derived. A complete algorithm is presented in the appendix. |
| File Size | 563385 |
| Page Count | 29 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_19880002957 |
| Archival Resource Key | ark:/13960/t9g49kr7p |
| Language | English |
| Publisher Date | 1987-10-01 |
| Access Restriction | Open |
| Subject Keyword | Statistics And Probability Linear Systems Filters Statistical Analysis Least Squares Method Predictions Regression Analysis Memory Computers Autoregressive Processes Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Technical Report |
