Results 31 to 40 of about 56,489 (266)
Empirical likelihood inference in autoregressive models with time-varying variances
Statistical Theory and Related Fields, 2022 This paper develops the empirical likelihood ( $ \mathrm {EL} $ ) inference procedure for parameters in autoregressive models with the error variances scaled by an unknown nonparametric time-varying function.
Yu Han, Chunming Zhang
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International Entrepreneurship Review, 2022 Objective: The aim of the article was to forecast the necessary pace of changes in the share of RES in the V4 countries resulting from the EU’s renewable energy sources directive compared to other European Union countries.
Krzysztof Adam Firlej, Marcin Stanuch
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CoRRAutoRegressive (AR) models have demonstrated competitive performance in image generation, achieving results comparable to those of diffusion models. However, their token-by-token image generation mechanism remains computationally intensive and existing solutions such as VAR often lead to limited sample diversity.
Hongyu Wu +3 more
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Profit rates in the developed capitalist economies: a time series investigation [PDF]
PSL Quarterly Review, 2017 This paper examines whether there is empirical evidence to support the hypothesis of a secular decline in the economy-wide profit rates, as predicted by classical economic theories. We specifically consider profit rates in the OECD economies based on the
Ivan D. Trofimov
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Energies, 2022 Renewable energy is crucial for achieving net zero emissions. Taiwan has abundant wind resources and most major wind farms are offshore over the Taiwan Strait due to a lack of space on land.
Ke-Sheng Cheng +2 more
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First-order planar autoregressive model
Modern Stochastics: Theory and ApplicationsThis paper establishes the conditions for the existence of a stationary solution to the first-order autoregressive equation on a plane as well as properties of the stationary solution.
Sergiy Shklyar
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Fishes, 2022 Nonlinearity and spatial autocorrelation are common features observed in marine fish datasets but are often ignored or not considered simultaneously in modeling. Both features are often present within ecological data obtained across extensive spatial and
Yafei Zhang, Yan Jiao, Robert J. Latour
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Advanced Engineering Materials, EarlyView.Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch +3 more
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Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning
Sensors, 2021 Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle.
Alessio Staffini +3 more
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The Grid Bootstrap and the Autoregressive Model [PDF]
Review of Economics and Statistics, 1999 A “grid” bootstrap method is proposed for confidence-interval construction, which has improved performance over conventional bootstrap methods when the sampling distribution depends upon the parameter of interest. The basic idea is to calculate the bootstrap distribution over a grid of values of the parameter of interest and form the confidence ...
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