Bayes information criterion for Tikhonov regularization with linear constraints: application to spectral data estimation

2003 ◽  
Author(s):  
P. Carvalho ◽  
A. Santos ◽  
A. Dourado ◽  
B. Ribeiro
Keyword(s):  
Spectral Data ◽  
Tikhonov Regularization ◽  
Information Criterion ◽  
Linear Constraints ◽  
Bayes Information Criterion ◽  
Data Estimation

scholarly journals Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect

Risks ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 5
Author(s):  
Karim Barigou ◽  
Stéphane Loisel ◽  
Yahia Salhi
Keyword(s):  
Life Insurance ◽  
Cross Validation ◽  
Cohort Effect ◽  
Information Criterion ◽  
Mortality Forecasting ◽  
Hand Model ◽  
Regularization Techniques ◽  
Bayes Information Criterion ◽  
The One ◽  
Regularized Model

Predicting the evolution of mortality rates plays a central role for life insurance and pension funds. Standard single population models typically suffer from two major drawbacks: on the one hand, they use a large number of parameters compared to the sample size and, on the other hand, model choice is still often based on in-sample criterion, such as the Bayes information criterion (BIC), and therefore not on the ability to predict. In this paper, we develop a model based on a decomposition of the mortality surface into a polynomial basis. Then, we show how regularization techniques and cross-validation can be used to obtain a parsimonious and coherent predictive model for mortality forecasting. We analyze how COVID-19-type effects can affect predictions in our approach and in the classical one. In particular, death rates forecasts tend to be more robust compared to models with a cohort effect, and the regularized model outperforms the so-called P-spline model in terms of prediction and stability.

scholarly journals An Improved GPS-Inferred Seasonal Terrestrial Water Storage Using Terrain-Corrected Vertical Crustal Displacements Constrained by GRACE

2019 ◽  
Vol 11 (12) ◽  
pp. 1433 ◽  
Author(s):  
Hok Sum Fok ◽  
Yongxin Liu
Keyword(s):  
Tikhonov Regularization ◽  
Bayesian Information Criterion ◽  
Variance Component ◽  
Water Storage ◽  
Information Criterion ◽  
Variance Component Estimation ◽  
Terrestrial Water Storage ◽  
Geophysical Model ◽  
Terrestrial Water ◽  
Component Estimation

Based on a geophysical model for elastic loading, the application potential of Global Positioning System (GPS) vertical crustal displacements for inverting terrestrial water storage has been demonstrated using the Tikhonov regularization and the Helmert variance component estimation since 2014. However, the GPS-inferred terrestrial water storage has larger resulting amplitudes than those inferred from satellite gravimetry (i.e., Gravity Recovery and Climate Experiment (GRACE)) and those simulated from hydrological models (e.g., Global Land Data Assimilation System (GLDAS)). We speculate that the enlarged amplitudes should be partly due to irregularly distributed GPS stations and the neglect of the terrain effect. Within southwest China, covering part of southeastern Tibet as a study region, a novel GPS-inferred terrestrial water storage approach is proposed via terrain-corrected GPS and supplementary vertical crustal displacements inferred from GRACE, serving as "virtual GPS stations" for constraining the inversion. Compared to the Tikhonov regularization and Helmert variance component estimation, we employ Akaike’s Bayesian Information Criterion as an inverse method to prove the effectiveness of our solution. Our results indicate that the combined application of the terrain-corrected GPS vertical crustal displacements and supplementary GRACE spatial data constraints improves the inversion accuracy of the GPS-inferred terrestrial water storage from the Helmert variance component estimation, Tikhonov regularization, and Akaike’s Bayesian Information Criterion, by 55%, 33%, and 41%, respectively, when compared to that of the GLDAS-modeled terrestrial water storage. The solution inverted with Akaike’s Bayesian Information Criterion exhibits more stability regardless of the constraint conditions, when compared to those of other inferred solutions. The best Akaike’s Bayesian Information Criterion inverted solution agrees well with the GLDAS-modeled one, with a root-mean-square error (RMSE) of 3.75 cm, equivalent to a 15.6% relative error, when compared to 39.4% obtained in previous studies. The remaining discrepancy might be due to the difference between GPS and GRACE in sensing different surface water storage components, the remaining effect of the water storage changes in rivers and reservoirs, and the internal error in the geophysical model for elastic loading.

Bid and Ask spreads for the cap and floor contracts under the Liouville fractional Vasicek model

2020 ◽  
Vol 39 (1) ◽  
Author(s):  
Farshid Mehrdoust
Keyword(s):  
Interest Rate ◽  
Information Criterion ◽  
Rate Model ◽  
Vasicek Model ◽  
Bayes Information Criterion ◽  
Interest Rate Model ◽  
Newton Raphson

This paper presents bid and ask formulas for cap and floor contracts prices byusing Wang transform under a Liouville fractional Vasicek (LfVasicek) interest rate model. To do this, the parameters of the model are calibrated by using the Newton-Raphson (NR) method. Then the standard and Liouville fractional versions of the Vasicek model are compared by the Bayes information criterion (BIC). Finally, we obtain the bid-ask boundaries for interest rate amount and cap and foor prices.

Benchmarking segmentation results using a Markov model and a Bayes information criterion

2003 ◽  
Author(s):  
Fionn D. Murtagh ◽  
Xiaoyu Qiao ◽  
Danny Crookes ◽  
Paul Walsh ◽  
P. A. M. Basheer ◽  
...  
Keyword(s):  
Markov Model ◽  
Information Criterion ◽  
Bayes Information Criterion

scholarly journals Kernel integration by Graphical LASSO

2020 ◽  
Author(s):  
Sarah Kristine Nørgaard ◽  
Kristoffer Linder-Steinlein ◽  
Anders Ulrik Eliasen ◽  
Jakob Stokholm ◽  
Bo L. Chawez ◽  
...  
Keyword(s):  
Partial Correlation ◽  
Graphical Model ◽  
Symptom Burden ◽  
Information Criterion ◽  
Heterogeneous Data ◽  
Underlying Distribution ◽  
Graphical Lasso ◽  
Bayes Information Criterion ◽  
Diverse Data ◽  
Kernel Integration

Integration of unstructured and very diverse data is often required for a deeper understanding of complex biological systems. In order to uncover communalities between heterogeneous data, the data is often harmonized by constructing a kernel and numerical integration is performed. In this study we propose a method for data integration in the framework of an undirected graphical model, where the nodes represent individual data sources of varying nature in terms of complexity and underlying distribution, and where the edges represent the partial correlation between two blocks of data. We propose a modified GLASSO for estimation of the graph, with a combination of cross-validation and extended Bayes Information Criterion for sparsity tuning. Furthermore, hierarchical clustering on the weighted consensus kernels from a fixed network is used to partitioning the samples into different classes. Simulations show increasing ability to uncover true edges with increasing sample size and signal to noise. Likewise, identification of non existing edges towards disconnected nodes is feasible. The framework is demonstrated for integration of longitudinal symptom burden data from the 2nd and 3rd year of life with 21 diseases precursors as well as the development of asthma and eczema at the age of 6 years from 403 children from the COPSAC2010 mother-child cohort, suggesting that maternal predisposition as well as being born preterm indirectly lead to higher risk of asthma via increased respiratory symptom burden.

scholarly journals Calibration of the Hargreaves–Samani method for the calculation of reference evapotranspiration in different Köppen climate classes

2015 ◽  
Vol 47 (2) ◽  
pp. 521-531 ◽  
Author(s):  
Javier Almorox ◽  
Jürgen Grieser
Keyword(s):  
Reference Evapotranspiration ◽  
Information Criterion ◽  
Absolute Error ◽  
Climatic Data ◽  
The Standard Model ◽  
Bayes Information Criterion ◽  
Maximum Air Temperature ◽  
Monteith Equation

The Penman–Monteith equation (FAO-56) is accepted as the standard model for estimating reference evapotranspiration (ETo). However, the major obstacle to using FAO-56 widely is that it requires numerous climatic data. The Hargreaves–Samani (HS) method is frequently used for the calculation of ETo since it is based on measurements of daily minimum and maximum air temperature alone. Those are commonly recorded at many meteorological stations throughout the world. It is the objective of this paper to evaluate the quality of HS and calibrate the coefficients of this method for different climates as represented by the Köppen classification. Estimated values are compared with Penman–Monteith ETo values in terms of the coefficient of efficiency Ceff as well as the root mean square error, the mean absolute error and the Bayes information criterion. The Penman–Monteith equation for ETo (FAO-56) is based on physics and known to provide best estimates of ETo. The results of our work show that the correlation between long-term monthly means of HS and FAO-56 can be improved significantly by introducing climate-class specific coefficients.

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