文章主要研究了安徽省某气象台站年平均气温、月平均气温的结构性变化情况。该文在对气温数据进行正态性检验的基础上,运用ASAMC(annealing stochastic approximation Monte Carlo)算法对气温数据进行统计分析,估计出平均气温结构性变化的位置,并探索发生结构性变化的气象因素和非气象因素。
This paper considers the problem of change point in single index models.In order to obtain asymptotically valid confidence intervals for the estimation of the change point,the convergence rate and asymptotic distribution of the change point estimate is studied.Some simulation results are presented which show that the numerical performance of our estimator is satisfactory.
We propose a nonparametric change point estimator in the distributions of a sequence of independent observations in terms of the test statistics given by Huˇskov′a and Meintanis(2006) that are based on weighted empirical characteristic functions. The weight function ω(t; a) under consideration includes the two weight functions from Huˇskov′a and Meintanis(2006) plus the weight function used by Matteson and James(2014),where a is a tuning parameter. Under the local alternative hypothesis, we establish the consistency, convergence rate, and asymptotic distribution of this change point estimator which is the maxima of a two-side Brownian motion with a drift. Since the performance of the change point estimator depends on a in use, we thus propose an algorithm for choosing an appropriate value of a, denoted by a_s which is also justified. Our simulation study shows that the change point estimate obtained by using a_s has a satisfactory performance. We also apply our method to a real dataset.
