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Cira E. G. Otiniano

Publications and source records attributed to Cira E. G. Otiniano.

3 recordsLinked to original sources

Extremes of solar spectral irradiance in the SORCE/XPS record

Extreme and rare changes in space mission solar irradiance records are scientifically relevant but difficult to quantify because these records are finite, instrument dependent, and affected by observational gaps and time varying measurement quality. We evaluated extreme daily logarithmic changes in the band integrated 0.1-7.0 nm irradiance measured by photodiode 7 of the Solar Radiation and Climate Experiment/X-Ray Photometer System (SORCE/XPS) from 2005 to 2019. After constructing a regular daily series by linear interpolation, we analyzed daily logarithmic changes in irradiance and fitted stationary Generalized Extreme Value models to 60 day block maxima and transformed 15 day block minima. Block lengths were selected using Ljung-Box diagnostics and sample autocorrelation functions. Maximum likelihood estimation was used as the primary inferential method, with probability weighted moments as a sensitivity check. The maximum likelihood GEV shape estimates were 0.1661 for maxima and 0.2928 for transformed minima, with closely aligned estimates under the two fitting methods. These positive point estimates are compatible with Fréchet type tails under the selected block constructions. Annualized return levels provide interpretable summaries of extreme relative increases and reductions, but estimates for long return periods remain strongly dependent on extrapolation beyond the 15 year record. Reported measurement precision and absolute uncertainty were used to qualify the interpretation of the fitted tails and were not propagated through the likelihood. By combining EVT based tail modeling with explicit consideration of measurement precision, absolute uncertainty, interpolation, and mission data gaps, the analysis provides an uncertainty aware astrostatistical baseline for extreme value inference from processed solar mission records.

astro-ph.SR↗

A Bimodal Model for Extremes Data

In extreme values theory, for a sufficiently large block size, the maxima distribution is approximated by the generalized extreme value (GEV) distribution. The GEV distribution is a family of continuous probability distributions, which has wide applicability in several areas including hydrology, engineering, science, ecology and finance. However, the GEV distribution is not suitable to model extreme bimodal data. In this paper, we propose an extension of the GEV distribution that incorporate an additional parameter. The additional parameter introduces bimodality and to vary tail weight, i.e., this proposed extension is more flexible than the GEV distribution. Inference for the proposed distribution were performed under the likelihood paradigm. A Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples with a discussion of the results. Finally, the proposed distribution is applied to environmental data sets, illustrating their capabilities in challenging cases in extreme value theory.

stat.ME↗

On the bimodal Gumbel model with application to environmental data

The Gumbel model is a very popular statistical model due to its wide applicability for instance in the course of certain survival, environmental, financial or reliability studies. In this work, we have introduced a bimodal generalization of the Gumbel distribution that can be an alternative to model bimodal data. We derive the analytical shapes of the corresponding probability density function and the hazard rate function and provide graphical illustrations. Furthermore, We have discussed the properties of this density such as mode, bimodality, moment generating function and moments. Our results were verified using the Markov chain Monte Carlo simulation method. The maximum likelihood method is used for parameters estimation. Finally, we also carry out an application to real data that demonstrates the usefulness of the proposed distribution.

stat.ME↗