Search results for " Likelihood"

Measurement of theCPAsymmetry and Branching Fraction ofB0→ρ0K0

2007

We present a measurement of the branching fraction and time-dependent CP asymmetry of B^0 to rho^0 K^0. The results are obtained from a data sample of 227 10^6 Y4S to BB_ decays collected with the BaBar detector at the PEP2 asymmetric-energy B Factory at SLAC. From a time-dependent maximum likelihood fit yielding 111+/-19 signal events we find B(B^0 to rho^0 K^0)=(4.9+/-0.8+/-0.9) 10^-6, where the first error is statistical and the second systematic. We report the measurement of the CP parameters S=0.20+/-0.52+/-0.24 and C=0.64+/-0.41+/-0.20.

Space-time Point Processes semi-parametric estimation with predictive measure information

2014

In this paper, we provide a method to estimate the space-time intensity of a branching-type point process by mixing nonparametric and parametric approaches. The method accounts simultaneously for the estimation of the different model components, applying a forward predictive likelihood estimation approach to semi-parametric models.

Approximate Bayesian Computation for Forecasting in Hydrological models

2018

Approximate Bayesian Computation (ABC) is a statistical tool for handling parameter inference in a range of challenging statistical problems, mostly characterized by an intractable likelihood function. In this paper, we focus on the application of ABC to hydrological models, not as a tool for parametric inference, but as a mechanism for generating probabilistic forecasts. This mechanism is referred as Approximate Bayesian Forecasting (ABF). The abcd water balance model is applied to a case study on Aipe river basin in Columbia to demonstrate the applicability of ABF. The predictivity of the ABF is compared with the predictivity of the MCMC algorithm. The results show that the ABF method as …

Empirical Likelihood Method for a Location Parameter Using Some Robust Estimators

2022

Pētījumā attīstītas empīriskās ticamības (EL) metodes divu un vairāku neatkarīgu populāciju salīdzināšanai, balstoties uz robustiem lokācijas parametra novērtētājiem. Iegūti jauni asimptotiskie rezultāti par empīriskās ticamības metodēm: 1) divu M-novērtētāju starpībai; 2) divu nošķeltu vidējo vērtību starpībai; 3) uz empīrisko ticamību balstītai ANOVA metodei vairāk kā divu nošķeltu vidējo vērtību salīdzināšanai. Tika izstrādāts simulāciju eksperiments un analizēti datu piemēri, kas parādīja, ka jauniegūtās metodes ir līdzvērtīga alternatīva klasiskās statistikas metodēm gadījumos, kad dati ir normāli sadalīti – tām ir līdzīga jauda un spēja kontrolēt empīrisko pirmā veida kļūdu. Turklāt m…

Uncertainty in urban stormwater quality modelling: The influence of likelihood measure formulation in the GLUE methodology

2009

In the last years, the attention on integrated analysis of sewer networks, wastewater treatment plants and receiving waters has been growing. However, the common lack of data in the urban water-quality field and the incomplete knowledge regarding the interpretation of the main phenomena taking part in integrated urban water systems draw attention to the necessity of evaluating the reliability of model results. Uncertainty analysis can provide useful hints and information regarding the best model approach to be used by assessing its degrees of significance and reliability. Few studies deal with uncertainty assessment in the integrated urban-drainage field. In order to fill this gap, there ha…

Inferring networks from high-dimensional data with mixed variables

2014

We present two methodologies to deal with high-dimensional data with mixed variables, the strongly decomposable graphical model and the regression-type graphical model. The first model is used to infer conditional independence graphs. The latter model is applied to compute the relative importance or contribution of each predictor to the response variables. Recently, penalized likelihood approaches have also been proposed to estimate graph structures. In a simulation study, we compare the performance of the strongly decomposable graphical model and the graphical lasso in terms of graph recovering. Five different graph structures are used to simulate the data: the banded graph, the cluster gr…

IFN-gamma-induced protein 10 is a novel biomarker of rhinovirus-induced asthma exacerbations

2007

BACKGROUND: Rhinovirus-induced acute asthma is the most frequent trigger for asthma exacerbations. OBJECTIVE: We assessed which inflammatory mediators were released from bronchial epithelial cells (BECs) after infection with rhinovirus and then determined whether they were also present in subjects with acute virus-induced asthma, with the aim to identify a biomarker or biomarkers for acute virus-induced asthma. METHODS: BECs were obtained from bronchial brushings of steroid-naive asthmatic subjects and healthy nonatopic control subjects. Cells were infected with rhinovirus 16. Inflammatory mediators were measured by means of flow cytometry with a cytometric bead array. Subjects with acute a…

Penalized logistic regression for small or sparse data: interval estimators revisited

2015

This paper focuses on interval estimation in logistic regression models fitted through the Firth penalized log-likelihood. In this context, many authors have claimed superiority of the Likelihood ratio statistic with respect to the (wrong) Wald statistic via simulation evidence. We re-assess such findings by detailing the inferential tools also including in the comparisons the (right) Wald statistic and other statistics neglected in previous literature. In particular, we assess performances of the CIs estimators by simulation and compare them in a real data set. Differently from previous findings, the Likelihood ratio statistic does not appear to be the best inferential device in Firth pena…

Semi-parametric estimation of the intensity function in space-time point processes

2009

Occlusion-based estimation of independent multinomial random variables using occurrence and sequential information

2017

Abstract This paper deals with the relatively new field of sequence-based estimation in which the goal is to estimate the parameters of a distribution by utilizing both the information in the observations and in their sequence of appearance. Traditionally, the Maximum Likelihood (ML) and Bayesian estimation paradigms work within the model that the data, from which the parameters are to be estimated, is known, and that it is treated as a set rather than as a sequence. The position that we take is that these methods ignore, and thus discard, valuable sequence -based information, and our intention is to obtain ML estimates by “extracting” the information contained in the observations when perc…