The article is devoted to the problem of calculating the probability density of a strictly stable law at $x\to\infty$. To solve this problem, it was proposed to use the expansion of the probability density in a power series. A representation of the probability density in the form of a power series and an estimate for the remainder term was obtained. This power series is convergent in the case $0<\alpha<1$ and asymptotic at $x\to\infty$ in the case $1<\alpha<2$. The case $\alpha=1$ was considered separately. It was shown that in the case $\alpha=1$ the obtained power series was convergent for any $|x|>1$ at $N\to\infty$. It was also shown that in this case it was convergent to the density of $g(x,1,\theta)$. An estimate of the threshold coordinate $x_\varepsilon^N$, was obtained which determines the range of applicability of the resulting expansion of the probability density in a power series. It was shown that in the domain $|x|\geqslant x_\varepsilon^N$ this power series could be used to calculate the probability density.
翻译:文章专门用$x\ to\ infty$来计算严格稳定的法律的概率密度问题。 为了解决这个问题, 提议在电力序列中使用扩大概率密度的方法。 获得了以电序列形式表示的概率密度表示, 剩余时间的估计值。 这个电源序列在案件 $0 alpha < 1 $ 和 asymptaty $x\to\ inty$ $$ ALpha < 2$ 中是趋同的。 案例 $\ alpha=1$ =1$ 。 为了解决这个问题, 有人提议, 在案件 $\ alpha=1 中, 获得的电量序列以 $x% 1$ 和 剩余时间的估计值表示。 在此案中, 该电源序列与 $( x, 1,\ thethetta) 的密度一致。 获得了一个阈值坐标坐标坐标坐标 $xvarepepslon@n 。 它显示, 在域中, 将使用概率 $_xxxxqlangsalges 。</s>