【摘 要】
：
We propose a new and easy-to-use method for identifying cointegrated components of nonstationary time series, consisting of an eigenalysis for a certain non-neg
论文部分内容阅读
We propose a new and easy-to-use method for identifying cointegrated components of nonstationary time series, consisting of an eigenalysis for a certain non-negative definite matrix. Our setting is model-free, and we allow the integer-valued integration orders of the observable series to be unknown, and to possibly differ.
其他文献
We are interested in an arithmetic of some orders generated by a unit(or units).Especially,we focus on the problem of determining a system of fundamental un
While the pointwise MM algorithm adapted from Hunter & Lange (2000) can be implemented for local linear quantile regression, it suffers from limitations such as
This paper is concerned with the problem of click fraud detection. We assume each visitor carries a latent indicator, which labels him/her as a regular or malic
The use of zeta integrals is pervasive in the study of automorphic forms,as exemplified in Tates famous thesis.Nevertheless,the relevant integrals and test
Current status data occur in contexts including demographic studies and tumorigenicity experiments. In this case, each subject is observed only once and the fai
In this talk, we discuss the problem of optimal allocation of standby [active] redundancies in coherent systems. We will present some new interesting comparison
Studying model averaging for high-dimensional models with possibly sparse relevant covariates, we incorporate penalized regression for narrowing the set of cand
In the area of sufficient dimension reduction, many methods, including the well-known sliced inverse regression, requires that the covariates satisfy the linear
In survival analysis, it is routine to test equality of two survival curves, which is often conducted by using the log-rank test. Although it is optimal under t
This work is concerned with testing the independence between a categorical random variable Y and a continuous one X based on mean variance index.