1,318 research outputs found
Statistical and Computational Tradeoff in Genetic Algorithm-Based Estimation
When a Genetic Algorithm (GA), or a stochastic algorithm in general, is
employed in a statistical problem, the obtained result is affected by both
variability due to sampling, that refers to the fact that only a sample is
observed, and variability due to the stochastic elements of the algorithm. This
topic can be easily set in a framework of statistical and computational
tradeoff question, crucial in recent problems, for which statisticians must
carefully set statistical and computational part of the analysis, taking
account of some resource or time constraints. In the present work we analyze
estimation problems tackled by GAs, for which variability of estimates can be
decomposed in the two sources of variability, considering some constraints in
the form of cost functions, related to both data acquisition and runtime of the
algorithm. Simulation studies will be presented to discuss the statistical and
computational tradeoff question.Comment: 17 pages, 5 figure
Outliers in dynamic factor models
Dynamic factor models have a wide range of applications in econometrics and
applied economics. The basic motivation resides in their capability of reducing
a large set of time series to only few indicators (factors). If the number of
time series is large compared to the available number of observations then most
information may be conveyed to the factors. This way low dimension models may
be estimated for explaining and forecasting one or more time series of
interest. It is desirable that outlier free time series be available for
estimation. In practice, outlying observations are likely to arise at unknown
dates due, for instance, to external unusual events or gross data entry errors.
Several methods for outlier detection in time series are available. Most
methods, however, apply to univariate time series while even methods designed
for handling the multivariate framework do not include dynamic factor models
explicitly. A method for discovering outliers occurrences in a dynamic factor
model is introduced that is based on linear transforms of the observed data.
Some strategies to separate outliers that add to the model and outliers within
the common component are discussed. Applications to simulated and real data
sets are presented to check the effectiveness of the proposed method.Comment: Published in at http://dx.doi.org/10.1214/07-EJS082 the Electronic
Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Attractor neural networks storing multiple space representations: a model for hippocampal place fields
A recurrent neural network model storing multiple spatial maps, or
``charts'', is analyzed. A network of this type has been suggested as a model
for the origin of place cells in the hippocampus of rodents. The extremely
diluted and fully connected limits are studied, and the storage capacity and
the information capacity are found. The important parameters determining the
performance of the network are the sparsity of the spatial representations and
the degree of connectivity, as found already for the storage of individual
memory patterns in the general theory of auto-associative networks. Such
results suggest a quantitative parallel between theories of hippocampal
function in different animal species, such as primates (episodic memory) and
rodents (memory for space).Comment: 19 RevTeX pages, 8 pes figure
Time-varying Multi-regime Models Fitting by Genetic Algorithms
Many time series exhibit both nonlinearity and nonstationarity. Though both features have often been taken into account separately, few attempts have been proposed to model them simultaneously. We consider threshold models, and present a general model allowing for different regimes both in time and in levels, where regime transitions may happen according to self-exciting, or smoothly varying, or piecewise linear threshold modeling. Since fitting such a model involves the choice of a large number of structural parameters, we propose a procedure based on genetic algorithms, evaluating models by means of a generalized identification criterion. The performance of the proposed procedure is illustrated with a simulation study and applications to some real data.Nonlinear time series; Nonstationary time series; Threshold model
A generalization of periodic autoregressive models for seasonal time series
Many nonstationary time series exhibit changes in the trend and seasonality structure, that may be modeled by splitting the time axis into different regimes. We propose multi-regime models where, inside each regime, the trend is linear and seasonality is explained by a Periodic Autoregressive model. In addition, for achieving parsimony, we allow season grouping, i.e. seasons may consists of one, two, or more consecutive observations. Since the set of possible solutions is very large, the choice of number of regimes, change times and order and structure of the Autoregressive models is obtained by means of a Genetic Algorithm, and the evaluation of each possible solution is left to an identication criterion such as AIC, BIC or MDL. The comparison and performance of the proposed method are illustrated by a real data analysis. The results suggest that the proposed procedure is useful for analyzing complex phenomena with structural breaks, changes in trend and evolving seasonality
Multi-regime models for nonlinear nonstationary time series
Nonlinear nonstationary models for time series are considered, where the series is generated from an autoregressive equation whose coe±cients change both according to time and the delayed values of the series itself, switching between several regimes. The transition from one regime to the next one may be discontinuous (self-exciting threshold model), smooth (smooth transition model) or continuous linear (piecewise linear threshold model). A genetic algorithm for identifying and estimating such models is proposed, and its behavior is evaluated through a simulation study and application to temperature data and a financial index.
I differenti orientamenti giurisprudenziali in materia di accesso al pubblico impiego, fra atteggiamenti di chiusura e approcci più attenti al diritto dell’Unione europea
L'articolo esamina i differenti orientamenti giurisprudenziali in materia di accesso al pubblico impiego. in particolare, viene presa in esame la questione concernente la nomina di cittadini di altri stati membri dell'Unione a direttore dei musei nazionali
Energia nucleare? Sì, per favore
L'opera motiva la necessità del ricorso all'energia elettronucleare
LA DEFINIZIONE DI “CONIUGE” AI SENSI DELLA DIRETTIVA 38/2004: IL CASO COMAN E HAMILTON
Con la pronuncia pregiudiziale nel caso Coman e al. del 5 giugno 2018, la Grande sezione della Corte di giustizia dell’Unione europea ha fornito, per la prima volta, un’interpretazione della nozione di “coniuge”, ai sensi della direttiva 38/2004, relativa al diritto dei cittadini dell’Unione e dei loro familiari di circolare e di soggiornare liberamente nel territorio degli Stati membri. Nel presente lavoro vengono esaminate le principali problematiche di diritto sollevate dalla vicenda in questione e, successivamente, si analizza l’interpretazione che ne ha fornito la Corte, in modo da riflettere sulle prossime evoluzioni della disciplina
Coevolutionary Genetic Algorithms for Establishing Nash Equilibrium in Symmetric Cournot Games
We use co-evolutionary genetic algorithms to model the players' learning process in several Cournot models, and evaluate them in terms of their convergence to the Nash Equilibrium. The \social-learning" versions of the two co-evolutionary algorithms we introduce, establish Nash Equilibrium in those models, in contrast to the \individual learning" versions which, as we see here, do not imply the convergence of the players' strategies to the Nash outcome. When players use \canonical co-evolutionary genetic algorithms" as learning algorithms, the process of the game is an ergodic Markov Chain, and therefore we analyze simulation results using the relevant methodology, to find that in the \social" case, states leading to NE play are highly frequent at the stationary distribution of the chain, in contrast to the \individual learning" case, when NE is not reached at all in our simulations; to ftnd that the expected Hamming distance of the states at the limiting distribution from the \NE state" is significantly smaller in the \social" than in the \individual learning case"; to estimate the expected time that the \social" algorithms need to get to the \NE state" and verify their robustness and finally to show that a large fraction of the games played are indeed at the Nash Equilibrium.Genetic Algorithms, Cournot oligopoly, Evolutionary Game Theory, Nash Equilibrium
- …