Search results for "likelihood"
showing 10 items of 264 documents
Hessian PDF reweighting meets the Bayesian methods
2014
We discuss the Hessian PDF reweighting - a technique intended to estimate the effects that new measurements have on a set of PDFs. The method stems straightforwardly from considering new data in a usual $\chi^2$-fit and it naturally incorporates also non-zero values for the tolerance, $\Delta\chi^2>1$. In comparison to the contemporary Bayesian reweighting techniques, there is no need to generate large ensembles of PDF Monte-Carlo replicas, and the observables need to be evaluated only with the central and the error sets of the original PDFs. In spite of the apparently rather different methodologies, we find that the Hessian and the Bayesian techniques are actually equivalent if the $\Delta…
Empirical Likelihood-Based ANOVA for Trimmed Means
2016
In this paper, we introduce an alternative to Yuen’s test for the comparison of several population trimmed means. This nonparametric ANOVA type test is based on the empirical likelihood (EL) approach and extends the results for one population trimmed mean from Qin and Tsao (2002). The results of our simulation study indicate that for skewed distributions, with and without variance heterogeneity, Yuen’s test performs better than the new EL ANOVA test for trimmed means with respect to control over the probability of a type I error. This finding is in contrast with our simulation results for the comparison of means, where the EL ANOVA test for means performs better than Welch’s heteroscedastic…
Biophysical parameter retrieval with warped Gaussian processes
2015
This paper focuses on biophysical parameter retrieval based on Gaussian Processes (GPs). Very often an arbitrary transformation is applied to the observed variable (e.g. chlorophyll content) to better pose the problem. This standard practice essentially tries to linearize/uniformize the distribution by applying non-linear link functions like the logarithmic, the exponential or the logistic functions. In this paper, we propose to use a GP model that automatically learns the optimal transformation directly from the data. The so-called warped GP regression (WGPR) presented in [1] models output observations as a parametric nonlinear transformation of a GP. The parameters of such prior model are…
Sequential Monte Carlo Methods in Random Intercept Models for Longitudinal Data
2017
Longitudinal modelling is common in the field of Biostatistical research. In some studies, it becomes mandatory to update posterior distributions based on new data in order to perform inferential process on-line. In such situations, the use of posterior distribution as the prior distribution in the new application of the Bayes’ theorem is sensible. However, the analytic form of the posterior distribution is not always available and we only have an approximated sample of it, thus making the process “not-so-easy”. Equivalent inferences could be obtained through a Bayesian inferential process based on the set that integrates the old and new data. Nevertheless, this is not always a real alterna…
Biophysical parameter estimation with adaptive Gaussian Processes
2009
We evaluate Gaussian Processes (GPs) for the estimation of biophysical parameters from acquired multispectral data. The standard GP formulation is used, and all hyperparameters (kernel parameters and noise variance) are optimized by maximizing the marginal likelihood. This gives rise to a fully-adaptive GP to data characteristics, both in terms of signal and noise properties. The good numerical results in the estimation of oceanic chlorophyll concentration and leaf membrane state confirm GPs as adequate, alternative non-parametric methods for biophysical parameter estimation. GPs are also analyzed by scrutinizing the predictive variance, the estimated noise variance, and the relevance of ea…
Structure and function of the vacuolar Ccc1/VIT1 family of iron transporters and its regulation in fungi
2020
Iron is an essential micronutrient for most living beings since it participates as a redox active cofactor in many biological processes including cellular respiration, lipid biosynthesis, DNA replication and repair, and ribosome biogenesis and recycling. However, when present in excess, iron can participate in Fenton reactions and generate reactive oxygen species that damage cells at the level of proteins, lipids and nucleic acids. Organisms have developed different molecular strategies to protect themselves against the harmful effects of high concentrations of iron. In the case of fungi and plants, detoxification mainly occurs by importing cytosolic iron into the vacuole through the Ccc1/V…
From phylogenetics to phylogenomics: the evolutionary relationships of insect endosymbiotic gamma-Proteobacteria as a test case.
2007
The increasing availability of complete genome sequences and the development of new, faster methods for phylogenetic reconstruction allow the exploration of the set of evolutionary trees for each gene in the genome of any species. This has led to the development of new phylogenomic methods. Here, we have compared different phylogenetic and phylogenomic methods in the analysis of the monophyletic origin of insect endosymbionts from the gamma-Proteobacteria, a hotly debated issue with several recent, conflicting reports. We have obtained the phylogenetic tree for each of the 579 identified protein-coding genes in the genome of the primary endosymbiont of carpenter ants, Blochmannia floridanus…
Searches for B0 decays to combinations of charmless isoscalar mesons
2004
We search for B meson decays into two-body combinations of eta, eta', omega, and phi mesons from 89 million B B-bar pairs collected with the BaBar detector at the PEP-II asymmetric-energy e+e- collider at SLAC. We find the branching fraction BF(B0 -> eta omega) = (4.0^{+1.3}_{-1.2} +- 0.4) x 10^-6 with a significance of 4.3 sigma. For all the other decay modes we set the following 90% confidence level upper limits on the branching fractions, in units of 10^-6 : BF(B0 -> eta eta)<2.8, BF(B0 -> eta eta')<4.6, BF(B0 -> eta' eta')<10, BF(B0 -> eta'omega)<2.8, BF(B0 -> eta phi)<1.0, BF(B0 -> eta' phi)<4.5, BF(B0 -> phi phi)<1.5.
Stochastical Real Time Finite State Machine LPC for Planar Manipulator Control System Model estimation
2005
This paper presents a new stochastical real-time LPC (Last Principal Component) algorithm to estimate single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) varying time models from input output data clusters of non stationary black boxes. Each of data clusters is on a time window. An application to estimate the control system model of a planar manipulator is developed. In fact many mathematical models of physical systems are non stationary such as industrial manipulator model. A real time estimation algorithm via stochastical LPC algorithm and an appraiser called "finite state machine" is then described For every data cluster the finite state machine updates the parame…
A new tuning parameter selector in lasso regression
2019
Penalized regression models are popularly used in high-dimensional data analysis to carry out variable selction and model fitting simultaneously. Whereas success has been widely reported in literature, their performance largely depend on the tuning parameter that balances the trade-off between model fitting and sparsity. In this work we introduce a new tuning parameter selction criterion based on the maximization of the signal-to-noise ratio. To prove its effectiveness we applied it to a real data on prostate cancer disease.