Open AccessBook
Probability and Measure
Reads0
Chats0
TLDR
In this paper, the convergence of distributions is considered in the context of conditional probability, i.e., random variables and expected values, and the probability of a given distribution converging to a certain value.Abstract:
Probability. Measure. Integration. Random Variables and Expected Values. Convergence of Distributions. Derivatives and Conditional Probability. Stochastic Processes. Appendix. Notes on the Problems. Bibliography. List of Symbols. Index.read more
Citations
More filters
Journal ArticleDOI
On Parameters of Increasing Dimensions
Xuming He,Qi-Man Shao +1 more
TL;DR: In this paper, the authors consider M-estimators of general parametric models and show that the component-wise asymptotic normality of the estimate remains valid if the dimension of the parameter space grows more slowly than some root of the sample size.
Journal ArticleDOI
GMM estimation of spatial autoregressive models with unknown heteroskedasticity
Xu Lin,Lung-fei Lee +1 more
TL;DR: In the presence of heteroskedastic disturbances, the MLE for the SAR models without taking into account the heteroSkedasticity is generally inconsistent as discussed by the authors, and 2SLS estimates can have large variances and biases for cases where regressors do not have strong effects.
Journal ArticleDOI
Velocity filtered density function for large eddy simulation of turbulent flows
TL;DR: In this article, the effects of the unresolved subgrid scales (SGS) are taken into account by considering the joint probability density function of all of the components of the velocity vector.
Journal ArticleDOI
Simultaneously Learning and Optimizing Using Controlled Variance Pricing
Arnoud V. den Boer,Bert Zwart +1 more
TL;DR: The key idea of the policy is to enhance the certainty equivalent pricing policy with a taboo interval around the average of previously chosen prices, which means that eventually the value of the optimal price will be learned, and derive upper bounds on the regret.
Book ChapterDOI
On the detection of anomalous system call arguments
TL;DR: In this article, learning-based anomaly detection systems build models of the expected behavior of applications by analyzing events that are generated during their normal operation, and subsequent events are analyzed to identify deviations, given the assumption that anomalies usually represent evidence of an attack.