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This research attempts to investigate how uncertainty in fiscal policy (FPU) and monetary policy (MPU) affects the US energy transition. While previous literature took the total renewable energy consumption (REC) as an indicator for the energy transition,
Mohammed Amine Mouffok +3 more
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High precision and reliable wind speed forecasting is a challenge for meteorologists. We used multiple nonparametric tree-based machine learning techniques, for predicting the maximum wind speed at 10 m using selected convective weather variables ...
Bhuiyan Md Abul Ehsan +3 more
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Aim: This study examines how management reports affect stock liquidity and compares the responses of stocks with low, medium, and high liquidity. Methodology: Stocks were classified into three liquidity groups based on the median Amihud (ILLIQ) ratio ...
Tohid Zeinali, Jan Makary Fryczak
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Parameter Estimation of the Partially Linear Quantile Regression Model Under Monotonic Constraints
The paper brings forward the partially linear quantile regression model by incorporating monotonic constraints, which are common in real-world relationships between variables.
Shujin Wu +3 more
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In this paper, the statistical inference of the partially linear varying coefficient quantile regression model is studied under random missing responses.
Shuanghua Luo, Yuxin Yan, Cheng-yi Zhang
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: The ability of a dairy cow to perform reliably over time is an interesting trait to include in dairy cattle breeding programs aimed at improving dairy cow resilience.
Fiona L. Guinan +3 more
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This work addresses the problem of the nonparametric estimation of the regression function, namely the conditional distribution and the conditional quantile in the single functional index model (SFIM) under the independent and identically distributed ...
Anis Allal, Nadia Kadiri, Abbes Rabhi
doaj
Soft Bayesian Additive Regression Trees (SBART) for correlated survey response with non-Gaussian error. [PDF]
Mandal A +3 more
europepmc +1 more source
Nonparametric Quantile Regression with Heavy-Tailed and Strongly Dependent Errors
We consider nonparametric estimation of the conditional qth quantile for stationary time series. We deal with stationary time series with strong time dependence and heavy tails under the setting of random design. We estimate the conditional qth quantile by local linear regression and investigate the asymptotic properties.
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