@misc{10481/97606,
year = {2024},
month = {11},
url = {https://hdl.handle.net/10481/97606},
abstract = {In this work, we approach the forecast problem for a general non-homogeneous diffusion
process over time with a different perspective from the classical one. We study the main characteristic
functions as mean, mode, and α-quantiles conditioned on a future time, not conditioned on the past
(as is normally the case), and we observe the specific formula in some interesting particular cases,
such as Gompertz, logistic, or Bertalanffy diffusion processes, among others. This study aims to
enhance classical inference methods when we need to impute data based on available information,
past or future. We develop a simulation and obtain a dataset that is closer to reality, where there is no
regularity in the number or timing of observations, to extend the traditional inference method. For
such data, we propose using characteristic functions conditioned on the past or the future, depending
on the closest point at which we aim to perform the imputation. The proposed inference procedure
greatly reduces imputation errors in the simulated dataset.},
organization = {PID2021-128261NBI00 (PROESTEAM), financed by MICIN/AEI/10.13039/501100011033 and by ERDF, EU and Junta de Andalucía grant number FQM-147},
organization = {“European Union – Next Generation EU” through MUR-PRIN 2022, project 2022XZSAFN “Anomalous Phenomena on Regular and Irregular Domains: Approximating Complexity for the Applied Sciences”,},
organization = {“European Union – Next Generation EU” through MURPRIN 2022 PNRR, project P2022XSF5H "Stochastic Models in Biomathematics and Applications},
publisher = {MDPI},
keywords = {diffusion processes},
keywords = {Gompertz-lognormal},
keywords = {conditioned on future},
title = {Inference with Non-Homogeneous Lognormal Diffusion Processes Conditioned on Nearest Neighbor},
doi = {10.3390/math12233703},
author = {García Burgos, Ana and Paraggio, Paola and Romero Molina, Desiré and Rico Castro, Nuria},
}