Results 111 to 120 of about 2,680 (148)

Exploring α-ψ-ϕ contractive mapping: novel fixed point theorems in complete b-metric spaces. [PDF]

F1000Res
Raji T, Ali N, Qousini M, Hanchalu G, Tolasa FT, Tolasa FT, Seboka B.   +6 more
europepmc   +1 more source

Hawking-Type Singularity Theorems for Worldvolume Energy Inequalities. [PDF]

Ann Henri Poincare
Graf M, Kontou EA, Ohanyan A, Schinnerl B.   +3 more
europepmc   +1 more source

Strong limit theorems for random fields

, 2011
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On the Lin Condition in Strong Ratio Limit Theorems

Mathematical Notes, 2004
Let \((X_n,n\geq 0)\) be a Markov chain with state space \((E,{\mathcal B})\) and \(P\) be a transition operator. It is proved that for a wide class of Markov chains and, in particular, for many random walks on groups the formula (Lin condition) \(\liminf (v(P^{n+1}f)/ v(P^nf))=1\) holds, where \(v\) and \(f\) are a probability measure on \({\mathcal B}
M G Shur
exaly   +2 more sources

On strong versions of the central limit theorem

Statistics & Probability Letters, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rychlik, Zdzisław, Szuster, Konrad S.
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A Strong Limit Theorem on Random Selection

Southeast Asian Bulletin of Mathematics, 2002
The idea of random selection on gambling systems has been extended to Markov chains using the notion of likelihood ratio and an analytic technique. A strong limit theorem on the relative frequency of ordered couple under random selection has been established.
Shi, Yimin, Xu, Yong, Kang, Huiguang
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Strong Limit Theorems in Noncommutative L2-Spaces

Lecture Notes in Mathematics, 1991
Ryszard Jajte
exaly   +2 more sources

On Strong Versions of the Central Limit Theorem

Mathematische Nachrichten, 1988
AbstractLet Sn be the sum of n i.i.d.r.v. and let 1(‐∞,x)(·) be the indicator function of the interval (‐∞, x). Then the sequence 1(‐∞, x)(Sn/√n) does not converge for any x. Likewise the arithmetic means of this sequence converge only with probability zero. But the logarithmic means converge with probability one to the standard normal distribution Ø(x)
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