Search results for "programming."

A multi-local optimization algorithm

1998

The development of efficient algorithms that provide all the local minima of a function is crucial to solve certain subproblems in many optimization methods. A “multi-local” optimization procedure using inexact line searches is presented, and numerical experiments are also reported. An application of the method to a semi-infinite programming procedure is included.

Spatio-temporal modelling of COVID-19 incident cases using Richards’ curve: An application to the Italian regions

2021

Abstract We introduce an extended generalised logistic growth model for discrete outcomes, in which spatial and temporal dependence are dealt with the specification of a network structure within an Auto-Regressive approach. A major challenge concerns the specification of the network structure, crucial to consistently estimate the canonical parameters of the generalised logistic curve, e.g. peak time and height. We compared a network based on geographic proximity and one built on historical data of transport exchanges between regions. Parameters are estimated under the Bayesian framework, using Stan probabilistic programming language. The proposed approach is motivated by the analysis of bot…

Functional Data Analysis with R and Matlab by RAMSAY, J. O., HOOKER, G., and GRAVES, S.

2010

Cotas inferiores para el QAP-Arbol

1985

The Tree-QAP is a special case of the Quadratic Assignment Problem where the flows not equal zero form a tree. No condition is required for the distance matrix. In this paper we present an integer programming formulation for the Tree-QAP. We use this formulation to construct four Lagrangean relaxations that produce several lower bounds for this problem. To solve one of the relaxed problems we present a Dynamic Programming algorithm which is a generalization of the algorithm of this type that gives a lower bound for the Travelling Salesman Problem. A comparison is given between the lower bounds obtained by each ralaxation for examples with size from 12 to 25.

Mixed Non-Parametric and Parametric Estimation Techniques in R Package etasFLP for Earthquakes’ Description

2017

etasFLP is an R package which fits an epidemic type aftershock sequence (ETAS) model to an earthquake catalog; non-parametric background seismicity can be estimated through a forward predictive likelihood approach, while parametric components of triggered seismicity are estimated through maximum likelihood; estimation steps are alternated until convergence is obtained and for each event the probability of being a background event is estimated. The package includes options which allow its wide use. Methods for plot, summary and profile are defined for the main output class object. The paper provides examples of the package's use with description of the underlying R and Fortran routines.

Combining the intensity and sequencing of the poverty experience:a class of longitudinal poverty indices

2011

Summary Traditional measures of the persistence of poverty do not devote enough attention to the sequence of spells of poverty. We propose a new class of indices which measures the severity of chronic poverty, taking into account the way in which spells of poverty and non-poverty follow one another along individual life courses. All the years spent in poverty concur with the measurement of the persistency of poverty, albeit with a decreasing contribution provided that the distance between two consecutive spells of poverty becomes longer. Moreover, the distance from the poverty line and the poverty persistence probabilities are explicitly taken into account. A macrolevel index, which allows …

Bayesian joint ordinal and survival modeling for breast cancer risk assessment

2016

We propose a joint model to analyze the structure and intensity of the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional-odds cumulative logit model. Time-to-event is modeled through a left-truncated proportionalhazards model, which incorporates information of the longitudinal marker as well as baseline covariates. Both longitudinal and survival processes are connected by means of a common vector of random effects. General inferences are discussed under the Bayesian approach and include the posterior distribution of the probabilities associated to each longitudinal category and the …

Introducing libeemd: a program package for performing the ensemble empirical mode decomposition

2016

The ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN) are adaptive, noise-assisted data analysis methods that improve on the ordinary empirical mode decomposition (EMD). All these methods decompose possibly nonlinear and/or nonstationary time series data into a finite amount of components separated by instantaneous frequencies. This decomposition provides a powerful method to look into the different processes behind a given time series data, and provides a way to separate short time-scale events from a general trend. We present a free software implementation of EMD, EEMD and CEEMDAN and give an overview of the EMD methodology and the algorithms used in the deco…

On 1-Laplacian Elliptic Equations Modeling Magnetic Resonance Image Rician Denoising

2015

Modeling magnitude Magnetic Resonance Images (MRI) rician denoising in a Bayesian or generalized Tikhonov framework using Total Variation (TV) leads naturally to the consideration of nonlinear elliptic equations. These involve the so called $1$-Laplacian operator and special care is needed to properly formulate the problem. The rician statistics of the data are introduced through a singular equation with a reaction term defined in terms of modified first order Bessel functions. An existence theory is provided here together with other qualitative properties of the solutions. Remarkably, each positive global minimum of the associated functional is one of such solutions. Moreover, we directly …

Bayesian survival analysis with BUGS

2020

Survival analysis is one of the most important fields of statistics in medicine and biological sciences. In addition, the computational advances in the last decades have favored the use of Bayesian methods in this context, providing a flexible and powerful alternative to the traditional frequentist approach. The objective of this article is to summarize some of the most popular Bayesian survival models, such as accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data. Moreover, an implementation of each presented model is provided using a BUGS syntax that can be run with JAGS from the R programmin…