论文标题
在耦合引理和随机特性上,具有无限的可观察到1-D扩展地图的可观测值
On Coupling Lemma and Stochastic Properties with Unbounded Observables for 1-d Expanding Maps
论文作者
论文摘要
在本文中,我们为标准家族建立了一个耦合引理,以设置分段扩展间隔地图,并具有许多分支。我们的方法仅要求扩展地图以$ q $ scale满足切尔诺夫的一步扩展,并最终覆盖磁铁间隔。因此,我们的方法对于雅各布逆的规律性和不满足大图像属性的地图特别有力。我们耦合方法的主要成分是两个至关重要的引理:从磁体间隔上的特征$ \ cz $函数和覆盖率的引理来看,生长引理。我们首先证明存在绝对连续的不变措施。更重要的是,我们进一步表明,生长引理可以使Lebesgue度量的提升到相关的Hofbauer塔,并且塔上的最终不变措施承认Pesin-Sinai类型的分解。此外,我们获得了相关性的指数衰减和几乎确定的不变性原理(这是中心极限定理的功能版本)。我们第一次能够在混合率和$ \ cz $函数之间建立直接关系,请参见(\ ref {equ:equal variation1})。我们的结果的新颖性依赖于建立不变密度的规律性,并验证一大类无限可观察物的随机特性。 最后,我们验证了以前在文献中研究的几个知名示例的假设,并将结果统一到我们框架中的这些示例。
In this paper, we establish a coupling lemma for standard families in the setting of piecewise expanding interval maps with countably many branches. Our method merely requires that the expanding map satisfies Chernov's one-step expansion at $q$-scale and eventually covers a magnet interval. Therefore, our approach is particularly powerful for maps whose inverse Jacobian has low regularity and those who does not satisfy the big image property. The main ingredients of our coupling method are two crucial lemmas: the growth lemma in terms of the characteristic $\cZ$ function and the covering ratio lemma over the magnet interval. We first prove the existence of an absolutely continuous invariant measure. What is more important, we further show that the growth lemma enables the liftablity of the Lebesgue measure to the associated Hofbauer tower, and the resulting invariant measure on the tower admits a decomposition of Pesin-Sinai type. Furthermore, we obtain the exponential decay of correlations and the almost sure invariance principle (which is a functional version of the central limit theorem). For the first time, we are able to make a direct relation between the mixing rates and the $\cZ$ function, see (\ref{equ:totalvariation1}). The novelty of our results relies on establishing the regularity of invariant density, as well as verifying the stochastic properties for a large class of unbounded observables. Finally, we verify our assumptions for several well known examples that were previously studied in the literature, and unify results to these examples in our framework.