1,721,180 research outputs found
Specification Errors in Spatial Models: Impacts on Modeling and Estimation.
No full textA principal motivation for the research reported in this paper is to seek to understand what consequences can result from an erroneous specification of the lattice topology when an autoregressive spatial model is employed for inferential purposes. The first part of the discussion concerns impacts of such a specification error on the resulting dependence structure among data in SAR and CAR models. Although some ideas on this matter can be found in popular monographs about spatial statistics (Cliff and Ord 1981; Griffith 1988; Cressie 1993; Guyon 1995), the problem does not seem to have been attacked directly in the literature, two exceptions being Griffith (1995) and Griffith and Lagona (1997). In this paper an additional step is made in this direction, extensively using the theory of power series for matrices (see, for instance, Cooke 1950) and basic graph theory (see, for instance, Ore 1960). The second part of the discussion concerns impacts of a misspecified lattice topology on the quality of popular estimation procedures for SAR and CAR models. Because estimator quality can be measured by sampling distribution properties, unbiasedness and efficiency are considered here as basic measures of it. For this problem, a useful starting point is furnished by Oksanen (1991), and Cordey and Griffith's (1993) work is extended here. Much of the finite sample mathematical statistics summarized in this paper is numerically demonstrated in Griffith (1995). And, the Jacobian work outlined in Griffith and Sone (1995) is exploited to attain selected analytical results
Segmentation of mortality surfaces by hidden Markov models
No full textGender-specific mortality surfaces are panels of time series of mortality rates that allow
to examine the temporal evolution of male and female mortality across ages. The analysis of these
surfaces is often complicated by time-varying effects that reflect the association of age and gender
with mortality under unobserved time-varying conditions of the population under study. We propose
a hidden Markov model as a simple tool to estimate time-varying effects in mortality surfaces. Under
this model, age and gender effects depend on the evolution of an unobserved (hidden) Markov chain,
which segments each time series of rates according to time-varying latent classes. We describe the
details of an efficient EM algorithm for maximum likelihood estimation of the parameters and suggest a
straightforward parametric bootstrap routine to compute standard errors. These methods are illustrated
on cardiovascular and cancer mortality rates, observed in Italy during the period 1980–2014
Adjacency Selection in Markov Random Fields for High Spatial Resolution Hyperspectral Data
No full textModelli statistici per l’analisi dell'ambiente
No full textI modelli statistici consentono di descrivere la relazione tra variabili
ambientali in diversi ambiti, come ad esempio la valutazione del rischio
e dell’impatto ambientale, la tutela delle risorse naturali, la prevenzione
dell’inquinamento e la previsione di eventi naturali estremi. Il contributo è una
breve introduzione ai modelli statistici lineari e offre un esempio di applicazione
di tali modelli all’analisi del riscaldamento globale
Copula-based segmentation of cylindrical time series
No full textA hidden Markov model is proposed for segmenting cylindrical time series according to a finite number of latent classes, associated with copula-based cylindrical densities. It provides a parsimonious and computationally tractable approach that integrates circular–linear correlation, multimodality and temporal auto-correlation
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