Missing not at random and the nonparametric estimation of the spectral density

DOI10.1111/jtsa.12527zbMath1458.62197OpenAlexW3036562063MaRDI QIDQ5135316

Sam Efromovich

Publication date: 20 November 2020

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/jtsa.12527

zbMATH Keywords

time seriesadaptationARMAMISEmultiple myelomaexploratory sampling

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Inference from stochastic processes and spectral analysis (62M15) Missing data (62D10)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Correlation testing in time series, spatial and cross-sectional data
• Mixing properties of ARMA processes
• Time series: theory and methods.
• Nonparametric curve estimation. Methods, theory, and applications
• Mixing: Properties and examples
• A new weak dependence condition and applications to moment inequalities
• Spectral density estimation with amplitude modulation and outlier detection
• Data-Driven Efficient Estimation of the Spectral Density
• Spectral estimation in the presence of missing data
• STRONG CONSISTENCY FOR AR MODEL WITH MISSING DATA
• EFFICIENT NON‐PARAMETRIC ESTIMATION OF THE SPECTRAL DENSITY IN THE PRESENCE OF MISSING OBSERVATIONS
• Spectral Analysis with Randomly Missed Observations: The Binomial Case
• Missing Data Analysis in Practice
• Univariate Nonparametric Regression in the Presence of Auxiliary Covariates