设{Xn;n≥1}是一均值为0、方差有限的正相伴平稳序列.记Sn=sum Xk,Mn from k=1 to n =maxk≤n︱Sk︱,n≥1.证明了在一定条件下,由E︱X1︱p(︱X1︱1/α)<∞可推出对任意的ε>0,有sum npα-2-αh from n=1 to ∞ (n)E{Mn-εn1/p}+<∞,其中h(n)为一在无穷处的缓变函数,{x}+=max{x,0}.
设{X,X_n;n≥1}是一独立同分布的随机变量序列.如果|X_m|是新序列{|X_k|;k≤n}中的第r大元素,则令X_n^((r)=X_m.同时记部分和与修整和分别为S_n=sum from k=1 to n X_k和^((r))S_n=S_n-(X_n^((1))+…+X_n^((r))).该文在EX^2可能是无穷的条件下,得到了修整和^((r))S_n的广义强逼近定理.作为应用,建立了关于修整和以及修整和乘积的广义泛函重对数律.
We consider efficient methods for the recovery of block sparse signals from underdetermined system of linear equations. We show that if the measurement matrix satisfies the block RIP with δ2s 〈 0.4931, then every block s-sparse signal can be recovered through the proposed mixed l2/ll-minimization approach in the noiseless case and is stably recovered in the presence of noise and mismodeling error. This improves the result of Eldar and Mishali （in IEEE Trans. Inform. Theory 55： 5302-5316, 2009）. We also give another sufficient condition on block RIP for such recovery method： 58 〈 0.307.
Let {Xn; n ≥ 1} be a sequence of independent and identically distributed U[0,1]-distributed random variables. Define the uniform empirical process Fn（t） = n^-1/2 ∑^ni=1 （I{xi≤t} - t）, 0 ≤ t 〈 1, ││Fn││ = sup0≤t≤ 1 │Fn（t）│. In this paper, the exact convergence rates of a general law of weighted infinite series of E{││Fn││ -εg^s（n）}＋ are obtained.
Let {X, Xn; n ≥ 0} be a sequence of independent and identically distributed random variables with EX=0, and assume that EX^2I（｜X｜ ≤ x） is slowly varying as x →∞, i.e., X is in the domain of attraction of the normal law. In this paper, a self-normalized law of the iterated logarithm for the geometrically weighted random series Σ~∞（n=0）β~nXn（0 〈 β 〈 1） is obtained, under some minimal conditions.
