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On runs, bivariate Poisson mixtures and distributions that arise in Bernoulli arrays

机译:在运行中,伯努利阵列中出现的二元泊松混合物和分布

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Distributional findings are obtained relative to various quantities arising in Bernoulli arrays ${ X_{k,j}, k geq 1, j =1, ldots, r+1}$, where the rows?$(X_{k,1}, ldots, X_{k,r+1})$ are independently distributed as $hbox{Multinomial}(1,p_{k,1}, ldots,p_{k,r+1})$ for $k geq 1$ with the homogeneity across the first $r$ columns assumption $p_{k,1}= cdots = p_{k,r}$. The quantities of interest relate to the measure of the number of runs of length $2$ and are $underline{S}_n (S_{n,1}, ldots, S_{n,r})$, $underline{S}=lim_{n o infty} underline{S}_n$, $T_n=sum_{j=1}^r S_{n,j}$, and $T=lim_{n o infty} T_n$, where $S_{n,j}= sum_{k=1}^n X_{k,j} X_{k+1,j}$. With various known results applicable to the marginal distributions of the $S_{n,j}$'s and to their limiting quantities $S_j=lim_{n o infty} S_{n,j},$, we investigate joint distributions in the bivariate ($r=2$) case and the distributions of their totals $T_n$ and $T$ for $r geq 2$. In the latter case, we derive a key relationship between multivariate problems and univariate ($r=1$) problems opening up the path for several derivations and representations such as Poisson mixtures. In the former case, we obtain general expressions for the probability generating functions, the binomial moments and the probability mass functions through conditioning, an analysis of a resulting recursive system of equations, and again by exploiting connections with the univariate problem. More precisely, for cases where $p_{k,j}= rac){b+k}$ for $j=1,2$ with $b geq 1$, we obtain explicit expressions for the probability generating function of?$underline{S}_n$, $n geq 1$, and $underline{S}$, as well as a Poisson mixture representation : $underline{S}|(V_1=v_1, V_2=v_2) sim^{ind.} mbox{Poisson}(v_i)$ with $(V_1,V_2) sim mbox{Dirichlet}(1,1,b-1)$ which nicely captures both the marginal distributions and the dependence structure. From this, we derive the fact that $S_1|S_1+S_2=t$ is uniformly distributed on ${0,1,ldots,t}$ whenever $b=1$. We conclude with yet another mixture representation for $p_{k,j}= rac) {b+k}$ for $j=1,2$ with $b geq 1$, where we show that $underline{S}|lpha sim p_{lpha}$, $lpha sim hbox{Beta}(1,b)$ with $p_{lpha}$ a bivariate mass function with Poisson$(lpha)$ marginals given by $p_{lpha} (s_1,s_2)= rac{e^{-lpha} {lpha}^{s_1+s_2}} {(s_1+s_2+1)!} , (s_1+s_2+1-lpha),.$
机译:获得相对于伯努利数组$ {X_ {k,j},k geq 1,j = 1, ldots,r + 1 } $中出现的各种数量的分布发现,其中行?$(X_ {k ,1}, ldots,X_ {k,r + 1})$独立分发为$ hbox {Multinomial}(1,p_ {k,1}, ldots,p_ {k,r + 1})$对于$ k geq 1 $,假设第一个$ r $列的同质性为$ p_ {k,1} = cdots = p_ {k,r} $。感兴趣的数量与长度为$ 2 $的游程数的度量有关,分别为$ 下划线{S} _n(S_ {n,1}, ldots,S_ {n,r})$,$ 下划线{ S} = lim_ {n to infty} 下划线{S} _n $,$ T_n = sum_ {j = 1} ^ r S_ {n,j} $和$ T = lim_ {n to infty} T_n $,其中$ S_ {n,j} = sum_ {k = 1} ^ n X_ {k,j} X_ {k + 1,j} $。利用适用于$ S_ {n,j} $的边际分布及其限制量$ S_j = lim_ {n to infty} S_ {n,j} ,$的各种已知结果,我们进行了研究在二元($ r = 2 $)情况下的联合分布,以及$ r geq 2 $的总分布$ T_n $和$ T $。在后一种情况下,我们得出多元问题和单变量($ r = 1 $)问题之间的关键关系,从而为几种派生和表示(例如泊松混合物)开辟了道路。在前一种情况下,我们通过条件,对所得方程的递归系统进行分析,以及通过利用与单变量问题的联系,来获得概率生成函数,二项式矩和概率质量函数的一般表达式。更精确地,对于其中$ j = 1,2 $且$ b geq 1 $的$ p_ {k,j} = frac){b + k} $的情况,我们获得了?的概率生成函数的显式表达式。 $ underline {S} _n $,$ n geq 1 $和$ underline {S} $,以及泊松混合表示:$ underline {S} |(V_1 = v_1,V_2 = v_2) sim ^ {ind。} mbox {Poisson}(v_i)$和$(V_1,V_2) sim mbox {Dirichlet}(1,1,b-1)$很好地捕获了边际分布和依赖结构。由此得出的事实是,每当$ b = 1 $时,$ S_1 | S_1 + S_2 = t $就会均匀分布在$ {0,1, ldots,t } $上。我们以$ p_ {k,j} = frac){b + k} $和$ b geq 1 $的$ j = 1,2 $的混合表示来结束,其中我们显示$ 下划线{S } | alpha sim p _ { alpha} $,$ alpha sim hbox {Beta}(1,b)$与$ p _ { alpha} $具有Poisson $( alpha)$边际的双变量质量函数由$ p _ { alpha}(s_1,s_2)= frac {e ^ {- alpha} { alpha} ^ {s_1 + s_2}} {(s_1 + s_2 + 1)!} ,(s_1 + s_2 + 1- alpha),。$

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