Results 101 to 110 of about 3,774,080 (285)
NONPARAMETRIC METHOD: KERNEL DENSITY ESTIMATION APPLIED TO FORESTRY DATA
FLORESTA, 2019 Probability density function can be fitted through parametric or non-parametric methods. The use of a non-parametric method is interesting and appropriate, considering its flexibility and better adjustment to multimodal data. The objective of the present study was to compare the performance of the non-parametric distribution in relation to the ...
Rafael Romualdo Wandresen +4 more
openaire +2 more sources
Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models
Journal of Forecasting, EarlyView.ABSTRACT Predicting carbon allowance prices has grown more crucial in relation to carbon market regulation, financial strategy, and environmental policy development. This study examines a hybrid forecasting system that combines deep learning with ensemble machine learning models to forecast the price fluctuations of EU Emissions Allowance (EUAs) within
Saptarshi Ganguly +2 more
wiley +1 more source
A Novel Text‐Based Framework for Forecasting Carbon Prices
Journal of Forecasting, EarlyView.ABSTRACT This study proposes a text‐based framework for predicting EU carbon prices. Using weekly data from 2020 to 2024, we construct a multivariate dataset combining financial indicators, commodity prices, Google Trends measures, and news‐based sentiment extracted using FinBERT.
Christian Oliver Ewald, Yaoyu Li
wiley +1 more source
Deconvolution Estimation in Measurement Error Models: The R Package decon [PDF]
Data from many scientific areas often come with measurement error. Density or distribution function estimation from contaminated data and nonparametric regression with errors in variables are two important topics in measurement error models.
Bin Wang, Xiao-Feng Wang
core
Health and Economic Impacts of an Early Labor Induction Policy for High‐BMI Mothers
Health Economics, EarlyView.ABSTRACT We expand the literature on marginal returns to birth interventions by studying a common intervention: early induction of labor for a growing share of pregnancies, high‐Body Mass Index (BMI) women. We exploit Danish guidelines which recommend routine induction at 7 days after the expected due date instead of 10–13 days after for mothers with a
Louis Freget, Maria Koch Gregersen
wiley +1 more source
Nonparametric Estimation of Risk-Neutral Densities [PDF]
This chapter deals with nonparametric estimation of the risk neutral density. We present three different approaches which do not require parametric functional assumptions on the underlying asset price dynamics nor on the distributional form of the risk ...
Wolfgang Karl Härdle +2 more
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Alternative Data for Realised Volatility Forecasting: Limit Order Book and News Stories
International Journal of Finance &Economics, EarlyView.ABSTRACT We examine whether two major alternative data sources, limit order book information and firm‐specific news, provide incremental predictive information for daily realised volatility forecasting within the HAR‐family, using a parsimonious framework to ensure practical implementation and comparability. The framework is designed for practical real‐
Eghbal Rahimikia, Ser‐Huang Poon
wiley +1 more source
Transformation kernel density estimation of actuarial loss functions [PDF]
A transformation kernel density estimator that is suitable for heavy-tailed distributions is discussed. Using a truncated Beta transformation, the choice of the bandwidth parameter becomes straightforward.
Montserrat Guillen (Universitat de Barcelona) +2 more
core
Journal of Applied Econometrics, EarlyView.ABSTRACT While existing work shows COVID‐19 stay‐at‐home (SAH) policies decreased mobility on average, we lack evidence regarding heterogeneity in policy effectiveness across US counties. To uncover potential heterogeneity, we implement a novel two‐stage approach.
James Sears +5 more
wiley +1 more source
The Relative Improvement of Bias Reduction in Density Estimator Using Geometric Extrapolated Kernel
پژوهشهای ریاضی, 2018 One of a nonparametric procedures used to estimate densities is kernel method. In this paper, in order to reduce bias of kernel density estimation, methods such as usual kernel(UK), geometric extrapolation usual kernel(GEUK), a bias reduction kernel(BRK)
Reza Salehi +2 more
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