FAST LINEAR ESTIMATION METHODS FOR VECTOR AUTOREGRESSIVE MOVING-AVERAGE MODELS

Abstract. Three linear methods for estimating parameter values of vector auto-regressive moving-average (VARMA) models which are in general at least an order of magnitude faster than maximum likelihood estimation are developed in this paper. Simulation results for different model structures with varying numbers of component series and observations suggest that the accuracy of these procedures is in most cases comparable with maximum likelihood estimation. Procedures for estimating parameter standard error are also discussed and used for identification of nonzero elements in the VARMA polynomial structures. These methods can also be used to establish the order of the VARMA structure. We note, however, that the primary purpose of these estimates is to generate initial estimates for the nonzero parameters in order to reduce subsequent computational time of more efficient estimation procedures such as exact maximum likelihood.     

Comparison of entropy and complexity measures for the assessment of depth of sedation

Entropy and complexity of the electroencephalogram (EEG) have recently been proposed as measures of depth of anesthesia and sedation. Using surrogate data of predefined spectrum and probability distribution we show that the various algorithms used for the calculation of entropy and complexity actually measure different properties of the signal. The tested methods, Shannon entropy (ShEn), spectral entropy, approximate entropy (ApEn), Lempel-Ziv complexity (LZC), and Higuchi fractal dimension (HFD) are then applied to the EEG signal recorded during sedation in the intensive care unit (ICU). It is shown that the applied measures behave in a different manner when compared to clinical depth of sedation score--the Ramsay score. ShEn tends to increase while the other tested measures decrease with deepening sedation. ApEn, LZC, and HFD are highly sensitive to the presence of high-frequency components in the EEG signal.     

The importance of accurate visibility parameterization during atmospheric correction: impact on boreal forest classification

Observation of the Earth's surface from spaceborne platforms is complicated by the various layers of the Earth's atmosphere that reflect, scatter, and attenuate electromagnetic radiation passing through them, thus influencing (upward or downward) the signal strength recorded at the sensor relative to the true quantity of radiance reflected from the observed surfaces. The magnitude and spatial distribution of atmospheric effects is non-stationary and will vary due to numerous factors. While the effect of these factors cannot be eliminated completely, the understanding of radiative transfer physics, atmospheric states, and electromagnetic wave propagation permits much of these effects to be appropriately modelled and minimized. Such corrections for atmospheric effects permit the extraction of more accurate physical properties of surface materials and states from imagery than if atmospheric effects were ignored. Modelling of atmospheric effects with radiative transfer models, however, requires appropriate parameterization. We explore the sensitivity of the important visibility parameter of the popular Atmospheric and Topographic Correction (ATCOR) model for atmospheric correction over boreal forest land cover. Further, we provide a methodology for estimating reasonable values for the visibility parameter in the event that this information is not readily available. Our sensitivity analyses, performed on Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery from northern Québec and Ontario, rely on both incremental adjustments to the visibility parameter to assess the degree of atmospheric effect removal and the cascading effect on land-cover classification. We build confidence around our measures using a spatial bootstrapping analysis within each of the two images we analyse. Our analysis demonstrates that exceeding a magnitude of error of approximately 2 km in estimating a visibility parameter values can decrease classification accuracy by nearly 10%. Our assessments of the spatial structure of the mitigated atmospheric component within our scenes, testing for complete spatial randomness, clustering of like values, or evenness in value distributions are inconclusive, but hint towards more clustered results with greater magnitudes of parameterization error.     

Study of the factors determining the mobility of ions in the polypyrrole films doped with aromatic sulfonate anions

The electrochemical redox behavior of the polypyrrole (PPy) films doped with benzenesulfonate (BS), p-toluenesulfonate (pTS) and naphtalene-1,5-disulfonate (NDS) was investigated by cyclic voltammetry, the chemical composition of the films was determined by XDS, the surface morphology was characterized by AFM, and the interactions between pyrrole oligomers and the anions were modeled with quantum chemical methods. It is the first detailed comparative study of the properties of these interesting systems with two complementing interactions (electrostatic and aromatic stacking). It was shown than these relatively similar aromatic sulfonate anions have rather different behavior in PPy films. The results showed that the redoxactivity of polypyrrole films doped with sulfonate ions increases in the sequence pTS < BS < NDS. The comparison of voltammograms measured in the presence of different anions with PPy films synthesized in the same solution show that the PPy/pTS films have the most densely packed and the PPy/NDS films the least densely packed structure of the three. The mobility of ions in these films is mainly determined by the packing of the films, which in turn partly depends on the interactions between the dopant-anion and polymer chains.     

DETERMINING THE ORDER OF A VECTOR AUTOREGRESSION WHEN THE NUMBER OF COMPONENT SERIES IS LARGE

Abstract. We contrast the performance of several methods used for identifying the order of vector autoregressive (VAR) processes when the number K of component series is large. Through simulation experiments we show that their performance is dependent on K , the number of nonzero elements in the polynomial matrices of the VAR parameters and the permitted upper limit of the order used in testing the autoregressive structure. In addition we introduce a new quite powerful multivariate order determination criterion.     

Type systems equivalent to data-flow analyses for imperative languages

We show that a large class of data-flow analyses for imperative languages are describable as type systems in the following technical sense: possible results of an analysis can be described in a language of types so that a program checks with a type if and only if this type is a supertype of the result of applying the analysis. Type-checking is easy with the help of a certificate that records the “eureka”-bits of a typing derivation. Certificate-assisted type-checking amounts to a form of lightweight analysis à la Rose. For secure information flow, we obtain a type system that is considerably more precise than that of Volpano et al., but not more sophisticated. Importantly, our type systems are compositional.     

A Compositional Natural Semantics and Hoare Logic for Low-Level Languages

The advent of proof-carrying code has generated significant interest in reasoning about low-level languages. It is widely believed that low-level languages with jumps must be difficult to reason about by being inherently non-modular. We argue that this is untrue. We take it seriously that, differently from statements of a high-level language, pieces of low-level code are multiple-entry and multiple-exit. And we define a piece of code to consist of either a single labelled instruction or a finite union of pieces of code. Thus we obtain a compositional natural semantics and a matching Hoare logic for a basic low-level language with jumps. By their simplicity and intuitiveness, these are comparable to the standard natural semantics and Hoare logic of While. The Hoare logic is sound and complete wrt. the semantics and allows for compilation of proofs of the Hoare logic of While.     

A GENERALIZED LEAST-SQUARES APPROACH FOR ESTIMATION OF AUTOREGRESSIVE MOVING-AVERAGE MODELS

Abstract. In this paper we present a generalized least-squares approach for estimating autoregressive moving-average (ARMA) models. Simulation results based on different model structures with varying numbers of observations are used to contrast the performance of our procedure with that of maximum likelihood estimates. Existing software packages can be utilized to derive these estimates.