1,720,974 research outputs found
Boosting multiplicative model combination
Full text linkIn this article, we define a new boosting-type algorithm for multiplicative model combination using as loss function the Hyvärinen scoring rule. In particular, we focus on density estimation problems and the aim is to define a suitable estimator, using a multiplicative combination of elementary density functions, which correspond to simplified or partially specified probability models for the interest random phenomenon. The boosting algorithm provides a simple sequential procedure for updating the weights of the component density functions, until an optimality criterion is satisfied. An extension of this procedure can be useful for composite likelihood inference, in order to specify the weights of the component likelihood objects and, simultaneously, implement parameter estimation. Finally, three applications are presented. The first one regards prediction and inference for autoregressive models, the second one is the use of model pools for prediction in a time series framework, and the third one is the estimation of the covariance and the precision matrices of a multivariate Gaussian distribution. Empirical results on real-world financial data are presented in challenging contexts, where we have to deal with a large dataset or with sparse matrices and a large number of unknown parameters
A likelihood-based boosting algorithm for factor analysis models with binary data
Full text linkStatistical boosting represents a very effective method for fitting complex models, while performing variable selection and preventing overfitting at the same time. However, the available methods are not directly applicable to factor analysis models for binary data, since any gradient descent method is not able to move from the starting point with zero loadings. The proposed algorithm, exploiting the directions of negative curvature of the log-likelihood function, is able to escape from the regions of local non-convexity. The component-wise approach followed leads to a sparse solution, which has the advantage of facilitating the interpretation without requiring a posterior rotation of the loadings. The method also performs regularization of the estimates, hence reducing their mean square error. To lighten the computational burden of the inferential procedure, a suitable pseudolikelihood, called pairwise likelihood, is exploited. In addition, a group lasso penalty is considered in order to automatically select the number of latent variables included in the model. The good performance of the proposal is illustrated through a simulation study and a real-data example
Pairwise Likelihood Inference for General State Space Models
No full textThis article concerns parameter estimation for general state space models, following a frequentist likelihood-based approach. Since exact methods for computing and maximizing the likelihood function are usually not feasible, approximate solutions, based on Monte Carlo or numerical methods, have to be considered. Here, we concentrate on a different approach based on a simple pseudolikelihood, called “pairwise likelihood.” Its merit is to reduce the computational burden so that it is possible to fit highly structured statistical models, even when the use of standard likelihood methods is not possible. We discuss pairwise likelihood inference for state space models, and we present some touchstone examples concerning autoregressive models with additive observation noise and switching regimes, the local level model and a non-Makovian generalization of the dynamic Tobit model.Composite likelihood, Efficiency, Pairwise likelihood, Pseudolikelihood, Regime switching, State space model, Tobit model,
A note on composite likelihood inference and model selection
Full text linkA composite likelihood consists of a combination of valid likelihood objects, usually related to small subsets of data. The merit of composite likelihood is to reduce the computational complexity so that it is possible to deal with large datasets and very complex models, even when the use of standard likelihood or Bayesian methods is not feasible. In this paper, we aim to suggest an integrated, general approach to inference and model selection using composite likelihood methods. In particular, we introduce an information criterion for model selection based on composite likelihood. We also describe applications to the modelling of time series of counts through dynamic generalised linear models and to the analysis of the well-known Old Faithful geyser dataset. Copyright 2005, Oxford University Press.
Pairwise Likelihood Inference for Ordinal Categorical Time Series.
No full textOrdinal categorical time series may be analyzed as censored observations from a suitable latent stochastic process, which describes the underlying evolution of the system. This approach may be considered as an alternative to Markov chain models or to regression methods for categorical time series data. The problem of parameter estimation is solved through a simple pseudolikelihood, called pairwise likelihood. This inferential methodology is successfully applied to the class of autoregressive ordered probit models. Potential usefulness for inference and model selection within more general classes of models are also emphasized. Illustrations include simulation studies and two simple real data applications
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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