Determining if a solution is optimal or near optimal is fundamental in optimization theory, algorithms, and computation. For instance, Karush-Kuhn-Tucker conditions provide necessa...
The problem of consistent estimation in measurement error models in a linear relation with not necessarily normally distributed measurement errors is considered. Three possible es...
The paper presents a comparative study on the performance of commonly used estimators of the fractional order of integration when data is contaminated by noise. In particular, meas...
— We use statistical estimates of the entropy rate of spike train data in order to make inferences about the underlying structure of the spike train itself. We first examine a n...
—In many channel measurement applications, one needs to estimate some characteristics of the channels based on a limited set of measurements. This is mainly due to the highly tim...
We present the Optimizing Control Variate (OCV) estimator, a new estimator for Monte Carlo rendering. Based upon a deterministic sampling framework, OCV allows multiple importance...
Shaohua Fan, Stephen Chenney, Bo Hu, Kam-Wah Tsui,...
We propose some ratio-type variance estimators using ratio estimators for the population mean in literature. We obtain mean square error (MSE) equations of proposed estimators and...
The finite sample properties of the Fourier estimator of integrated volatility under market microstructure noise are studied. Analytic expressions for the bias and the mean square...
Robust estimators of the prediction error of a linear model are proposed. The estimators are based on the resampling techniques cross-validation and bootstrap. The robustness of t...
Maximum likelihood estimators are often of limited practical use due to the intensive computation they require. We propose a family of alternative estimators that maximize a stoch...