Results 41 to 50 of about 2,756 (146)

Nonparametric Detection of a Time‐Varying Mean

Journal of Time Series Analysis, EarlyView.
ABSTRACT We propose a nonparametric portmanteau test for detecting changes in the unconditional mean of a univariate time series which may display either long or short memory. Our approach is designed to have power against, among other things, cases where the mean component of the series displays abrupt level shifts, deterministic trending behaviour ...
Fabrizio Iacone, A. M. Robert Taylor
wiley   +1 more source

Adaptive Estimation for Weakly Dependent Functional Times Series

Journal of Time Series Analysis, EarlyView.
ABSTRACT We propose adaptive mean and autocovariance function estimators for stationary functional time series under 𝕃p−m‐approximability assumptions. These estimators are designed to adapt to the regularity of the curves and to accommodate both sparse and dense data designs.
Hassan Maissoro, Valentin Patilea, Myriam Vimond   +2 more
wiley   +1 more source

Discussion of ‘Robust distance covariance’ by S. Leyder, J. Raymaekers and P. J. Rousseeuw

International Statistical Review, EarlyView.
Hallin Marc, Davide La Vecchia, Hang Liu, Xinyi Xu   +3 more
wiley   +1 more source

Nonparametric Inference of Conditional Expectile Functions in Large‐Scale Time Series Data With Improved Efficiency

Journal of Time Series Analysis, EarlyView.
ABSTRACT Expectile is a coherent and elicitable law‐invariant risk measure widely applied in risk management. Existing methods based on iteratively reweighted least squares (IWLS) are not computationally efficient for large‐scale sample sizes. To overcome the issue, we develop a direct nonparametric conditional expectile function estimator by inverting
Feipeng Zhang, Ping‐Shou Zhong
wiley   +1 more source

Functional Vašiček Model

Journal of Time Series Analysis, EarlyView.
ABSTRACT We propose a new formulation of the Vašičekmodel within the framework of functional data analysis. We treat observations (continuous‐time rates) within a suitably defined trading day as a single statistical object. We then consider a sequence of such objects, indexed by day.
Piotr Kokoszka, Neda Mohammadi, Leonardo Santoro, Hong Miao, Haonan Wang   +4 more
wiley   +1 more source

Density‐Valued ARMA Models by Spline Mixtures

Journal of Time Series Analysis, EarlyView.
ABSTRACT This paper proposes a novel framework for modeling time series of probability density functions by extending autoregressive moving average (ARMA) models to density‐valued data. The method is based on a transformation approach, wherein each density function on a compact domain [0,1]d$$ {\left[0,1\right]}^d $$ is approximated by a B‐spline ...
Yasumasa Matsuda, Rei Iwafuchi
wiley   +1 more source

Fully Modified GLS Estimation for Seemingly Unrelated Cointegrating Polynomial Regressions

Oxford Bulletin of Economics and Statistics, EarlyView.
ABSTRACT A new feasible generalized least squares estimator is proposed. Our estimator incorporates (1) the inverse autocovariance matrix of multidimensional errors, and (2) second‐order bias corrections. The resulting estimator has the intuitive interpretation of applying a weighted least squares objective function to filtered data series.
Yicong Lin, Hanno Reuvers
wiley   +1 more source

Multivariate representations of univariate marked Hawkes processes

Scandinavian Journal of Statistics, EarlyView.
Abstract Univariate marked Hawkes processes are used to model a range of real‐world phenomena including earthquake aftershock sequences, contagious disease spread, content diffusion on social media platforms, and order book dynamics. This paper illustrates a fundamental connection between univariate marked Hawkes processes and multivariate Hawkes ...
Louis Davis, Conor Kresin, Boris Baeumer, Ting Wang   +3 more
wiley   +1 more source

Efficient multiple‐robust estimation for nonresponse data under informative sampling

Scandinavian Journal of Statistics, EarlyView.
Abstract Nonresponse in probability sampling presents a long‐standing challenge in survey sampling, often necessitating simultaneous adjustments to address sampling and selection biases. We develop a statistical framework that explicitly models sampling weights as random variables and establish the semiparametric efficiency bound for the parameter of ...
Kosuke Morikawa, Kenji Beppu, Wataru Aida   +2 more
wiley   +1 more source

Adaptive blind image deblurring and denoising

Scandinavian Journal of Statistics, EarlyView.
Abstract Blind image deblurring aims to reconstruct the original image from its blurred version without knowing the blurring mechanism. This is a challenging ill‐posed problem because there are infinitely many possible solutions. The ill‐posedness is further exacerbated if the blurring mechanism depends on the pixel location.
Yicheng Kang, Anik Roy, Partha Sarathi Mukherjee   +2 more
wiley   +1 more source