We propose a family of nonparametric estimators for an option price that require only the use underlying return data, but can also easily incorporate information from observed prices. Each estimator comes risk-neutral measure minimizing generalized entropy according to different Cressie–Read discrepancy. apply our method S&P 500 options and cross-section individual equity options, using distinc...

In a number of estimation problems in bioinformatics, accuracy measures of the target problem are usually given, and it is important to design estimators that are suitable to those accuracy measures. However, there is often a discrepancy between an employed estimator and a given accuracy measure of the problem. In this study, we introduce a general class of efficient estimators for estimation p...

The order-q Tsallis (Hq ) and Rényi entropy (Kq ) receive broad applications in the statistical analysis of complex phenomena. A generic problem arises, however, when these entropies need to be estimated from observed data. The finite size of data sets can lead to serious systematic and statistical errors in numerical estimates. In this paper, we focus upon the problem of estimating generalized...

Finite population estimation is the overall goal of sample surveys. When information regarding auxiliary variables are available, one may take advantage of general regression estimators (GREG) to improve sample estimate precision. GREG estimators may be derived when the relationship between interest and auxiliary variables is represented by a normal linear model. However, in some scenarios, suc...

The sensitivity of the least-squares estimation in a regression model is impacted by multicollinearity and autocorrelation problems. To deal with multicollinearity, Ridge, Liu, Ridge-type biased estimators have been presented statistical literature. recently proposed Kibria-Lukman estimator one estimators. literature has compared others using mean square error criterion for linear model. It was...

In the linear regression models with AR (1) error structure when collinearity exists, stochastic linear restrictions or modifications of biased estimators (including Liu estimators) can be used to reduce the estimated variance of the regression coefficients estimates. In this paper, the combination of the biased Liu estimator and stochastic linear restrictions estimator is considered to overcom...