搜索结果: 1-15 共查到“数理统计学 q-series”相关记录21条 . 查询时间(0.075 秒)
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Estimating Time-Varying Networks for High-Dimensional Time Series
高维 时间序列 时变网络
2023/4/25
We explore time-varying networks for high-dimensional locally stationary time series, using the large VAR model framework with both the transition and (error) precision matrices evolving smoothly over...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Policy Choice in Time Series by Empirical Welfare Maximization
经验福利 最大化 时间序列 政策选择
2023/4/25
This paper develops a novel method for policy choice in a dynamic setting where the available data is a multivariate time series. Building on the statistical treatment choice framework, we propose Tim...
中山大学岭南学院高级计量经济学课件（II：Time series）Ch6 Cointegration
中山大学岭南学院 高级计量经济学 课件（II：Time series） Ch6 Cointegration
2017/6/14
中山大学岭南学院高级计量经济学课件（II：Time series）Ch6 Cointegration。
中山大学岭南学院高级计量经济学课件（II：Time series）CH5 Vector Autoregression (VAR) Models
中山大学岭南学院 高级计量经济学 课件（II：Time series） CH5 Vector Autoregression (VAR) Models
2017/6/14
中山大学岭南学院高级计量经济学课件（II：Time series）CH5 Vector Autoregression (VAR) Models。
中山大学岭南学院高级计量经济学课件（II：Time series）CH4 Unstationary Autoregressive Process
中山大学岭南学院 高级计量经济学 课件（II：Time series） CH4 Unstationary Autoregressive Process
2017/6/14
中山大学岭南学院高级计量经济学课件（II：Time series）CH4 Unstationary Autoregressive Process。
中山大学岭南学院高级计量经济学课件（II：Time series）CH3 ARCH and GARCH
中山大学岭南学院 高级计量经济学 课件（II：Time series） CH3 ARCH and GARCH
2017/6/14
中山大学岭南学院高级计量经济学课件（II：Time series）CH3 ARCH and GARCH。
中山大学岭南学院高级计量经济学课件（II：Time series）CH2 Stationary Autoregressive Process
中山大学岭南学院 高级计量经济学 课件（II：Time series） CH2 Stationary Autoregressive Process
2017/6/14
中山大学岭南学院高级计量经济学课件（II：Time series）CH2 Stationary Autoregressive Process。
中山大学岭南学院高级计量经济学课件（II：Time series）CH1 Basic Regression with Time Series
中山大学岭南学院 高级计量经济学 课件（II：Time series） CH1 Basic Regression with Time Series
2017/6/14
中山大学岭南学院高级计量经济学课件（II：Time series）CH1 Basic Regression with Time Series。
Factor modeling for high-dimensional time series: Inference for the number of factors
Autocovariance matrices blessing of dimensionality eigenanalysis fast convergence rates multivariate time series
2012/6/19
This paper deals with the factor modeling for high-dimensional time series based on a dimension-reduction viewpoint. Under stationary settings, the inference is simple in the sense that both the numbe...
SEMIPARAMETRIC DENSITY ESTIMATION FOR TIME SERIES WITH
semiparametric density estimation time series multiplicative adjustment
2011/11/11
In this paper, we extend a class of semiparametric density estimators to time series context. The asymptotic theory and simulation study are discussed. Theoretical results and numerical comparison sho...
Estimating Extremal Dependence in Univariate and Multivariate Time Series via the Extremogram
Extremogram extremal dependence stationary bootstrap financial time series
2011/10/9
Abstract: Davis and Mikosch [7] introduced the extremogram as a flexible quantitative tool for measuring various types of extremal dependence in a stationary time series. There we showed some standard...
Uniform hypothesis testing for ergodic time series distributions
Uniform hypothesis ergodic time series distributions Statistics Theory
2011/9/16
Abstract: Given a discrete-valued sample $X_1,...,X_n$ we wish to decide whether it was generated by a distribution belonging to a family $H_0$, or it was generated by a distribution belonging to a fa...
Estimation of the mean of functional time series and a two sample problem
Functional data analysis Time series Two sample problem Long–run variance Eurodollar futures
2011/6/15
This paper is concerned with inference based on the mean function of a functional
time series, which is defined as a collection of curves obtained by splitting a continuous
time record, e.g. into da...
Some fascinating series and their sums
identities series sums algebraic and trigonometric series
2011/3/1
In this paper we present some interesting results involving summation of series in particular trigonometric ones.
The problem of filtering of finite–alphabet stationary ergodic time series is considered. A method for constructing a confidence set for the (unknown) signal is proposed.