设(X,Y)是取值于 R^d×R^1 的随机变量,其 X 的边缘分布为 v,Y 关于 X 的条件分布函数为 F(y|x).于是变量 Y 关于 X 的回归函数即条件期望为r(x)=∫_(R^1)ydF(y|x).(1.1)设(X_1,Y_1),…,(X_n,Y_n)是(X,Y) 的一组独立观测值,或称为(X...设(X,Y)是取值于 R^d×R^1 的随机变量,其 X 的边缘分布为 v,Y 关于 X 的条件分布函数为 F(y|x).于是变量 Y 关于 X 的回归函数即条件期望为r(x)=∫_(R^1)ydF(y|x).(1.1)设(X_1,Y_1),…,(X_n,Y_n)是(X,Y) 的一组独立观测值,或称为(X,Y)的一组样本.对固定的 x∈R^d,记(R_(1,x)^(?),…,R_(n,x)^(?)为(1,…,n)的一个随机置换,展开更多
We show sharp bounds for probabilities of large deviations for sums of independent random variables satisfying Bernstein's condition. One such bound is very close to the tail of the standard Gaussian law in certai...We show sharp bounds for probabilities of large deviations for sums of independent random variables satisfying Bernstein's condition. One such bound is very close to the tail of the standard Gaussian law in certain case; other bounds improve the inequalities of Bennett and Hoeffding by adding missing factors in the spirit of Talagrand(1995). We also complete Talagrand's inequality by giving a lower bound of the same form, leading to an equality. As a consequence, we obtain large deviation expansions similar to those of Cram′er(1938),Bahadur-Rao(1960) and Sakhanenko(1991). We also show that our bound can be used to improve a recent inequality of Pinelis(2014).展开更多
A local probability exponential inequality for the tail of large deviation of an empirical process over an unbounded class of functions is proposed and studied. A new method of truncating the original probability spac...A local probability exponential inequality for the tail of large deviation of an empirical process over an unbounded class of functions is proposed and studied. A new method of truncating the original probability space and a new symmetrization method are given. Using these methods, the local probability exponential inequalities for the tails of large deviations of empirical processes with non-i.i.d. independent samples over unbounded class of functions are established. Some applications of the inequalities are discussed. As an additional result of this paper, under the conditions of Kolmogorov theorem, the strong convergence results of Kolmogorov on sums of non-i.i.d. independent random variables are extended to the cases of empirical processes indexed by unbounded classes of functions, the local probability exponential inequalities and the laws of the logarithm for the empirical processes are obtained.展开更多
文摘设(X,Y)是取值于 R^d×R^1 的随机变量,其 X 的边缘分布为 v,Y 关于 X 的条件分布函数为 F(y|x).于是变量 Y 关于 X 的回归函数即条件期望为r(x)=∫_(R^1)ydF(y|x).(1.1)设(X_1,Y_1),…,(X_n,Y_n)是(X,Y) 的一组独立观测值,或称为(X,Y)的一组样本.对固定的 x∈R^d,记(R_(1,x)^(?),…,R_(n,x)^(?)为(1,…,n)的一个随机置换,
基金国家自然科学基金(No.10571159)教育部博士点专项基金(No.20060335032)the Korea Research Foundation Grant Funded by Korea Government(MoEHRD Basic Research Fund)(No.Mol-2003-000-10302-0)资助的项目
基金supported by the Post-Graduate Study Abroad Program sponsored by China Scholarship CouncilNational Natural Science Foundation of China(Grant Nos.11171044 and11401590)
文摘We show sharp bounds for probabilities of large deviations for sums of independent random variables satisfying Bernstein's condition. One such bound is very close to the tail of the standard Gaussian law in certain case; other bounds improve the inequalities of Bennett and Hoeffding by adding missing factors in the spirit of Talagrand(1995). We also complete Talagrand's inequality by giving a lower bound of the same form, leading to an equality. As a consequence, we obtain large deviation expansions similar to those of Cram′er(1938),Bahadur-Rao(1960) and Sakhanenko(1991). We also show that our bound can be used to improve a recent inequality of Pinelis(2014).
基金This work was supported partially by the National Natural Science Foundation of China(Grant No.19661001)the Social Science Foundation of Ministry of Education of China.
文摘A local probability exponential inequality for the tail of large deviation of an empirical process over an unbounded class of functions is proposed and studied. A new method of truncating the original probability space and a new symmetrization method are given. Using these methods, the local probability exponential inequalities for the tails of large deviations of empirical processes with non-i.i.d. independent samples over unbounded class of functions are established. Some applications of the inequalities are discussed. As an additional result of this paper, under the conditions of Kolmogorov theorem, the strong convergence results of Kolmogorov on sums of non-i.i.d. independent random variables are extended to the cases of empirical processes indexed by unbounded classes of functions, the local probability exponential inequalities and the laws of the logarithm for the empirical processes are obtained.
