  We introduce and analyse univariate, linear, and stationary subdivision
schemes for refining noisy data, by fitting local least squares polynomials. We
first present primal schemes, based on fitting linear polynomials to the data,
and study their convergence, smoothness, and basic limit functions. We provide
several numerical experiments that illustrate the limit functions generated by
these schemes from initial noisy data, and compare the results with
approximations obtained from noisy data by an advanced local linear regression
method. We conclude by discussing several extension and variants.
