An On-line Method for Estimation of Piecewise Constant Parameters in Linear Regression Models

An On-line Method for Estimation of Piecewise Constant Parameters in Linear Regression Models

We present an on-line method for detecting changes and estimating parameters in AR(X) models. The method is based on the assumption of piecewise constant parameters resulting in a sparse structure of their derivative. To illustrate the algorithm and its performance, we apply it to the change in the model parameters of some ARX models. The example illustrates that the new method shows good perfo...

Autoregressive Models with Piecewise Constant Volatility and Regression Parameters

We introduce herein a new class of autoregressive models in which the regression parameters and error variances may undergo changes at unknown time points while staying constant between adjacent change-points. Assuming conjugate priors, we derive closed-form recursive Bayes estimates of the regression parameters and error variances. Approximations to the Bayes estimates are developed that have ...

Robust Estimation in Linear Regression with Molticollinearity and Sparse Models

‎One of the factors affecting the statistical analysis of the data is the presence of outliers‎. ‎The methods which are not affected by the outliers are called robust methods‎. ‎Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers‎. ‎Besides outliers‎, ‎the linear dependency of regressor variables‎, ‎which is called multicollinearity...

Piecewise Constant Controlled Linear Fuzzy Systems

Segmentation of Signals Using Piecewise Constant Linear Regression Models Submitted to Ieee Transactions on Signal Processing

The signal segmentation approach described herein assumes that the signal can be accurately modelled by a linear regression with piece-wise constant parameters. A simultaneous estimate of the change times is considered. The maximum likelihood and maximum a posteriori probability estimates are derived after marginalization of the linear regression parameters and the measurement noise variance, w...