272 research outputs found
La citta di Lodi festeggiante per la promotione al cardinalato dell' em.mo Pietro Vidoni suo vescouo /
No full textErrata, p. [41].T.p. engraving with Vidoni arms signed: Blanc. fec. (i.e. Bianchi?)Publication date taken from date of dedication (11 March 1661).Mode of access: Internet.Binding: modern decorated paper wrappers
Boosting multiplicative model combination
No full textIn 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 parametric family of Massey-type methods: inference, prediction, and sensitivity
No full textWe study the stability of a time-aware version of the popular Massey method, previously introduced by
Franceschet, M., E. Bozzo, and P. Vidoni. 2017. “The Temporalized Massey’s Method.” Journal of Quantitative
Analysis in Sports 13: 37–48, for rating teams in sport competitions. To this end,we embed the tempora lMassey
method in the theory of time-varying averaging algorithms, which are dynamic systems mainly used in control
theory for multi-agent coordination. We also introduce a parametric family of Massey-type methods
and show that the original and time-aware Massey versions are, in some sense, particular instances of it.
Finally, we discuss the key features of this general family of rating procedures, focusing on inferential and predictive
issues and on sensitivity to upsets and modifications of the schedule
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
No full textA 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.
Geometric ergodicity, regularity of the invariant distribution and inference for a threshold bilinear Markov process
No full textIn this paper we consider a first order threshold bilinear Markov process, which can be viewed as an AR model with ARCH-type errors and may be useful for modelling economic or financial time series. We study the main features of this process within a wider family of nonlinear models, where the threshold term is replaced by a smooth approximating function. Under suitable general assumptions, we provide sufficient conditions for the geometric ergodicity of the processes of this class and for the existence of their finite moments of a given order. Furthermore, we state regularity conditions for the invariant measures and we prove that the invariant measures of the smooth models weakly converge to that of the threshold one. The problem of estimating the parameters, including the threshold parameter, is studied and a simple semiparametric procedure based on the theory of optimal estimating functions is proposed
Two Different Numerical Approaches for Supporting Vibration-Based Structural Health Monitoring of Gear Train Systems
No full textPairwise 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
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