We handle two major issues in applying extreme value analysis to financial time series, bias and serial dependence, jointly. This is achieved by studying bias correction method when observations exhibit weakly serial dependence, namely the β−mixing series. For estimating the extreme value index, we propose an asymptotically unbiased estimator and prove its asymptotic normality under the β−mixin...

To analyze day trading dynamics for Nifty Index futures and options contracts, a detailed study is steered to understand the quantum of volume traded how affects underlying volatility. Day trades are about 30% 46% total respectively. This signifies high Volume by individuals bulk compared other categories both intraday non-day trades. estimates volatility dynamics. Volatility assessed minimum-v...

Sample coordination, where similar instances have similar samples, was proposed by statisticiansfour decades ago as a way to maximize overlap in repeated surveys. Coordinated sampling had beensince used for summarizing massive data sets.The usefulness of a sampling scheme hinges on the scope and accuracy within which queries posedover the original data can be answered from t...

Nearly all estimators in statistical prediction come with an associated tuning parameter, in one way or another. Common practice, given data, is to choose the tuning parameter value that minimizes a constructed estimate of the prediction error of the estimator; we focus on Stein’s unbiased risk estimator, or SURE (Stein, 1981; Efron, 1986), which forms an unbiased estimate of the prediction err...

We show that the variance of the Monte Carlo estimator that is importance sampled from an exponential family is a convex function of the natural parameter of the distribution. With this insight, we propose an adaptive importance sampling algorithm that simultaneously improves the choice of sampling distribution while accumulating a Monte Carlo estimate. Exploiting convexity, we prove that the m...

This paper presents some optimal real-time and post-processing estimators of vehicle position using odometer and map-matched GPS measurements. These estimators were based on a simple statistical error model of the odometer and the GPS which makes the model generalizable to other applications. Firstly, an asymptotically minimum variance unbiased estimator and two optimal moving fixed interval fi...

This paper considers the problem of shear building damage estimation subject to earthquake ground excitation using the Kalman filtering approach. The structural damage is assumed to take the form of reduced elemental stiffness. Two damage estimation algorithms are proposed: one is the multiple model approach via the optimal two-stage Kalman estimator (OTSKE), and the other is the robust two-sta...

Many continuous control tasks have bounded action spaces and clip out-of-bound actions before execution. Policy gradient methods often optimize policies as if actions were not clipped. We propose clipped action policy gradient (CAPG) as an alternative policy gradient estimator that exploits the knowledge of actions being clipped to reduce the variance in estimation. We prove that CAPG is unbias...

In earlier studies, the estimation of the volatility of a stock using information on the daily opening, closing, high and low prices has been developed; the additional information in the high and low prices can be incorporated to produce unbiased (or near-unbiased) estimators with substantially lower variance than the simple open-close estimator. This paper tackles the more difficult task of es...

| In this article, we generalize the input{domain based software reliability measures by Nelson and by Weiss and Weyuker, introducing expected failure costs under the operational distribution as a measure for software unreliability. This approach incorporates in the reliability concept a distinction between diierent degrees of failure severity. It is shown how to estimate the proposed quantity ...