Probability Inequalities for the Sum of Random Variables When Sampling Without Replacement
نویسندگان
چکیده
منابع مشابه
Probability Inequalities for Kernel Embeddings in Sampling without Replacement
The kernel embedding of distributions is a popular machine learning technique to manipulate probability distributions and is an integral part of numerous applications. Its empirical counterpart is an estimate from a finite set of samples from the distribution under consideration. However, for large-scale learning problems the empirical kernel embedding becomes infeasible to compute and approxim...
متن کاملSome Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables
In this paper, we obtain the upper exponential bounds for the tail probabilities of the quadratic forms for negatively dependent subgaussian random variables. In particular the law of iterated logarithm for quadratic forms of independent subgaussian random variables is generalized to the case of negatively dependent subgaussian random variables.
متن کاملSOME PROBABILISTIC INEQUALITIES FOR FUZZY RANDOM VARIABLES
In this paper, the concepts of positive dependence and linearlypositive quadrant dependence are introduced for fuzzy random variables. Also,an inequality is obtained for partial sums of linearly positive quadrant depen-dent fuzzy random variables. Moreover, a weak law of large numbers is estab-lished for linearly positive quadrant dependent fuzzy random variables. Weextend some well known inequ...
متن کاملMaximal Inequalities for Associated Random Variables
In a celebrated work by Shao [13] several inequalities for negatively associated random variables were proved. In this paper we obtain some maximal inequalities for associated random variables. Also we establish a maximal inequality for demimartingales which generalizes and improves the result of Christofides [4].
متن کاملsome probability inequalities for quadratic forms of negatively dependent subgaussian random variables
in this paper, we obtain the upper exponential bounds for the tail probabilities of the quadratic forms for negatively dependent subgaussian random variables. in particular the law of iterated logarithm for quadratic forms of independent subgaussian random variables is generalized to the case of negatively dependent subgaussian random variables.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Statistics and Probability
سال: 2013
ISSN: 1927-7040,1927-7032
DOI: 10.5539/ijsp.v2n4p75