Robust trend inference with series variance estimator and testing-optimal smoothing parameter

Robust Trend Inference with Series Variance Estimator and Testing-optimal Smoothing Parameter

The paper develops a novel testing procedure for hypotheses on deterministic trends in a multivariate trend stationary model. The trends are estimated by the OLS estimator and the long run variance (LRV) matrix is estimated by a series type estimator with carefully selected basis functions. Regardless of whether the number of basis functions K is …xed or grows with the sample size, the Wald sta...

Median-Unbiased Optimal Smoothing and Trend Extraction

Robust smoothing: Smoothing parameter selection and applications to fluorescence spectroscopy

Fluorescence spectroscopy has emerged in recent years as an effective way to detect cervical cancer. Investigation of the data preprocessing stage uncovered a need for a robust smoothing to extract the signal from the noise. Various robust smoothing methods for estimating fluorescence emission spectra are compared and data driven methods for the selection of smoothing parameter are suggested. T...

1983: Variance Estimation for a Time Series with Linear Trend

The problem of variance estimation for a t ime series with linear trend is studied using model based procedures. The work done by Royall and Cumberland ((2), (3)) is refined and adapted to the special problems of the Current Employment Survey (790 Survey) at the Bureau of Labor Statistics. A variety of variance estimation techniques are examined; including a variation of jackknife estimation an...

Robust Performance Hypothesis Testing with the Variance

Applied researchers often test for the difference of the variance of two investment strategies; in particular, when the investment strategies under consideration aim to implement the global minimum variance portfolio. A popular tool to this end is the F -test for the equality of variances. Unfortunately, this test is not valid when the returns are correlated, have tails heavier than the normal ...