<div class="description">
<dl><dd>"NPS-55-90-10."</dd>
<dd>Cover title</dd>
<dd>"April 1990."</dd>
<dd>AD A222 710</dd>
<dd>Includes bibliographical references (p. 25-26)</dd>
<dd>MARS(Multivariate Adaptive Regression Splines). Abstract: MARS is a new methodology, due to Friedman, for nonlinear regression modeling. MARS can be conceptualized as a generalization of recursive partitioning that uses spline fitting in lieu of other simple functions. Given a set of predictor variables, MARS fits a model in a form of an expansion of product spline basis functions of predictors chosen during a forward and backward recursive partitioning strategy. MARS produces continuous models for discrete data that can have multiple partitions and multilinear terms. Predictor variable contributions and interactions in a MARS model may be analyzed using an ANOVA style decomposition. By letting the predictor variables in MARS be lagged values of a time series, one obtains a new method for nonlinear autoregressive threshold modeling of time series. A significant feature of this extension of MARS is its ability to produce models with limit cycles when modeling time series data that exhibit periodic behavior. In a physical context, limit cycles represent a stationary state of sustained oscillations, a satisfying behavior for any model of a time series with periodic behavior. Analysis of the Wolf sunspot numbers with MARS appears to give an improvement over existing nonlinear Threshold and Bilinear models</dd></dl><br>Subjects: TIME-SERIES ANALYSIS--MATHEMATICAL MODELS.MULTIVARIATE ANALYSIS--MATHEMATICAL MODELS.REGRESSION ANALYSIS--MATHEMATICAL MODELS.NONLINEAR THEORIES--MATHEMATICAL MODELS.</div>
<div class="fn value">
Lewis, Peter A. W., 1932-;Stevens, James G.</div>
Internet Archive identifier