• Title/Summary/Keyword: $K_n$-family of distributions

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A M-TYPE RISK MODEL WITH MARKOV-MODULATED PREMIUM RATE

  • Yu, Wen-Guang
    • Journal of applied mathematics & informatics
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    • v.27 no.5_6
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    • pp.1033-1047
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    • 2009
  • In this paper, we consider a m-type risk model with Markov-modulated premium rate. A integral equation for the conditional ruin probability is obtained. A recursive inequality for the ruin probability with the stationary initial distribution and the upper bound for the ruin probability with no initial reserve are given. A system of Laplace transforms of non-ruin probabilities, given the initial environment state, is established from a system of integro-differential equations. In the two-state model, explicit formulas for non-ruin probabilities are obtained when the initial reserve is zero or when both claim size distributions belong to the $K_n$-family, n $\in$ $N^+$ One example is given with claim sizes that have exponential distributions.

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THE LATTICE DISTRIBUTIONS INDUCED BY THE SUM OF I.I.D. UNIFORM (0, 1) RANDOM VARIABLES

  • PARK, C.J.;CHUNG, H.Y.
    • Journal of the Korean Mathematical Society
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    • v.15 no.1
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    • pp.59-61
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    • 1978
  • Let $X_1$, $X_2$, ${\cdots}$, $X_n$ be i.i.d. uniform (0,1) random variables. Let $f_n(x)$ denote the probability density function (p.d.f.) of $T_n={\sum}^n_{i=1}X_i$. Consider a set S(x ; ${\delta}$) of lattice points defined by S(x ; ${\delta}$) = $x{\mid}x={\delta}+j$, j=0, 1, ${\cdots}$, n-1, $0{\leq}{\delta}{\leq}1$} The lattice distribution induced by the p.d.f. of $T_n$ is defined as follow: (1) $f_n^{(\delta)}(x)=\{f_n(x)\;if\;x{\in}S(x;{\delta})\\0\;otherwise.$. In this paper we show that $f_n{^{(\delta)}}(x)$ is a probability function thus we obtain a family of lattice distributions {$f_n{^{(\delta)}}(x)$ : $0{\leq}{\delta}{\leq}1$}, that the mean and variance of the lattice distributions are independent of ${\delta}$.

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ON CHARACTERIZATIONS OF THE CONTINUOUS DISTRIBUTIONS BY INDEPENDENCE PROPERTY OF RECORD VALUES

  • JIN, HYUN-WOO;LEE, MIN-YOUNG
    • Journal of applied mathematics & informatics
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    • v.35 no.5_6
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    • pp.651-657
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    • 2017
  • A sequence {$X_n,\;n{\geq}1$} of independent and identically distributed random variables with absolutely continuous (with respect to Lebesque measure) cumulative distribution function F(x) is considered. We obtain two characterizations of a family of continuous probability distribution by independence property of record values.

METRIC FOLIATIONS ON HYPERBOLIC SPACES

  • Lee, Kyung-Bai;Yi, Seung-Hun
    • Journal of the Korean Mathematical Society
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    • v.48 no.1
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    • pp.63-82
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    • 2011
  • On the hyperbolic space $D^n$, codimension-one totally geodesic foliations of class $C^k$ are classified. Except for the unique parabolic homogeneous foliation, the set of all such foliations is in one-one correspondence (up to isometry) with the set of all functions z : [0, $\pi$] $\rightarrow$ $S^{n-1}$ of class $C^{k-1}$ with z(0) = $e_1$ = z($\pi$) satisfying |z'(r)| ${\leq}1$ for all r, modulo an isometric action by O(n-1) ${\times}\mathbb{R}{\times}\mathbb{Z}_2$. Since 1-dimensional metric foliations on $D^n$ are always either homogeneous or flat (that is, their orthogonal distributions are integrable), this classifies all 1-dimensional metric foliations as well. Equations of leaves for a non-trivial family of metric foliations on $D^2$ (called "fifth-line") are found.

ON SOME APPLICATIONS OF THE ARCHIMEDEAN COPULAS IN THE PROOFS OF THE ALMOST SURE CENTRAL LIMIT THEOREMS FOR CERTAIN ORDER STATISTICS

  • Dudzinski, Marcin;Furmanczyk, Konrad
    • Bulletin of the Korean Mathematical Society
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    • v.54 no.3
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    • pp.839-874
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    • 2017
  • Our goal is to establish and prove the almost sure central limit theorems for some order statistics $\{M_n^{(k)}\}$, $k=1,2,{\ldots}$, formed by stochastic processes ($X_1,X_2,{\ldots},X_n$), $n{\in}N$, the distributions of which are defined by certain Archimedean copulas. Some properties of generators of such the copulas are intensively used in our proofs. The first class of theorems stated and proved in the paper concerns sequences of ordinary maxima $\{M_n\}$, the second class of the presented results and proofs applies for sequences of the second largest maxima $\{M_n^{(2)}\}$ and the third (and the last) part of our investigations is devoted to the proofs of the almost sure central limit theorems for the k-th largest maxima $\{M_n^{(k)}\}$ in general. The assumptions imposed in the first two of the mentioned groups of claims significantly differ from the conditions used in the last - the most general - case.

Structural Analysis of Conductive Polypyrroles Synthesized in an Ionic Liquid

  • Song, Eun-Ah;Jung, Woo-Gwang;Ihm, Dae-Woo;Kim, Jin-Yeol
    • Bulletin of the Korean Chemical Society
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    • v.30 no.5
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    • pp.1009-1011
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    • 2009
  • Nano-structured conducting polypyrroles were synthesized in the ionic liquids (ILs) based on 1-alkyl-3-methylimidazolium family with tetrachloroferrate as an anion ($C_n\;mim\;[FeCl_4]\;with\;n\;=\;4,\;8,\;and\;12$). The polypyrrole nanostructures synthesized in ILs were formed as spherical shapes. For ionic liquids with alkyl side chain length $C_4,\;C_4\;mim\;[FeCl_4]$, the size of particles was ranged around 60-nm with a relatively narrow size distribution. As the length of alkyl chain increases, the particle sizes become larger and their distributions become wider. The self-assembled local structures in the solvent ionic liquids are likely to serve as templates of highly organized nano-structured polymers. The length of the alkyl chain in ionic liquids seems to affect these local structures.

Empirical Bayes Problem With Random Sample Size Components

  • Jung, Inha
    • Journal of the Korean Statistical Society
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    • v.20 no.1
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    • pp.67-76
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    • 1991
  • The empirical Bayes version involves ″independent″ repetitions(a sequence) of the component decision problem. With the varying sample size possible, these are not identical components. However, we impose the usual assumption that the parameters sequence $\theta$=($\theta$$_1$, $\theta$$_2$, …) consists of independent G-distributed parameters where G is unknown. We assume that G $\in$ g, a known family of distributions. The sample size $N_i$ and the decisin rule $d_i$ for component i of the sequence are determined in an evolutionary way. The sample size $N_1$ and the decision rule $d_1$$\in$ $D_{N1}$ used in the first component are fixed and chosen in advance. The sample size $N_2$and the decision rule $d_2$ are functions of *see full text($\underline{X}^1$equation omitted), the observations in the first component. In general, $N_i$ is an integer-valued function of *see full text(equation omitted) and, given $N_i$, $d_i$ is a $D_{Ni}$/-valued function of *see full text(equation omitted). The action chosen in the i-th component is *(equation omitted) which hides the display of dependence on *(equation omitted). We construct an empirical Bayes decision rule for estimating normal mean and show that it is asymptotically optimal.

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