915 resultados para Linear Models in Temporal Series


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Marnet, Oliver, 'History repeats itself: The failure of rational choice models in corporate governance', Critical Perspectives on Accounting (2005) 18(2) pp.191-210 RAE2008

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http://books.google.com/books?id=plhkPFrJ1QUC&dq=law+and+custom+of+slavery+in+British+India

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This paper uses dynamic impulse response analysis to investigate the interrelationships among stock price volatility, trading volume, and the leverage effect. Dynamic impulse response analysis is a technique for analyzing the multi-step-ahead characteristics of a nonparametric estimate of the one-step conditional density of a strictly stationary process. The technique is the generalization to a nonlinear process of Sims-style impulse response analysis for linear models. In this paper, we refine the technique and apply it to a long panel of daily observations on the price and trading volume of four stocks actively traded on the NYSE: Boeing, Coca-Cola, IBM, and MMM.

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Rates of population increase in early spring and the sizes of overwintering stocks were calculated for the planktonic copepods Pseudocalanus elongatus and Acartia clausi for a set of areas covering the open waters of the north-east Atlantic Ocean and the North Sea for the period 1948 to 1979. For both species, the rates of population increase were higher in the open ocean than in the North Sea and appear to be related to temperature. The overwintering stocks in the North Sea were larger than those in the open ocean and are probably related to phytoplanton concentration. P. elongatus shows higher overwintering stocks and lower rates of population increase than A. clausi, resulting in different levels of persistence in the stocks of the two species. It is suggested that this difference in persistence is responsible for differences between the two species with respect to geographical distribution in summer and different patterns of year-to-year fluctuations in abundance.

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Time-series of annual means of abundance of zooplankton of the north-east Atlantic Ocean and the North Sea, for the period 1948 to 1977, show considerable associations between successive years. The seasonal dynamics of the stocks appear to be consistent with at least a proportion of this being due to inherent persistence from year-to-year. Experiments with a simple model suggest that the observed properties of the time-series cannot be reproduced as a response to simple random forcing. The extent of trends and long wavelength variations can be simulated by introducing fairly extensive persistence into the perturbations, but this underestimates the extent of shorter wavelength variability in the observed time-series. The effect of persistence is to increase the proportion of trend and long wavelength variability in time-series of annual means, but stocks can respond to short wavelength perturbations provided these have a clearly defined frequency.

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Self-compacting concrete (SCC) is generally designed with a relatively higher content of finer, which includes cement, and dosage of superplasticizer than the conventional concrete. The design of the current SCC leads to high compressive strength, which is already used in special applications, where the high cost of materials can be tolerated. Using SCC, which eliminates the need for vibration, leads to increased speed of casting and thus reduces labour requirement, energy consumption, construction time, and cost of equipment. In order to obtain and gain maximum benefit from SCC it has to be used for wider applications. The cost of materials will be decreased by reducing the cement content and using a minimum amount of admixtures. This paper reviews statistical models obtained from a factorial design which was carried out to determine the influence of four key parameters on filling ability, passing ability, segregation and compressive strength. These parameters are important for the successful development of medium strength self-compacting concrete (MS-SCC). The parameters considered in the study were the contents of cement and pulverised fuel ash (PFA), water-to-powder ratio (W/P), and dosage of superplasticizer (SP). The responses of the derived statistical models are slump flow, fluidity loss, rheological parameters, Orimet time, V-funnel time, L-box, JRing combined to Orimet, JRing combined to cone, fresh segregation, and compressive strength at 7, 28 and 90 days. The models are valid for mixes made with 0.38 to 0.72 W/P ratio, 60 to 216 kg/m3 of cement content, 183 to 317 kg/m3 of PFA and 0 to 1% of SP, by mass of powder. The utility of such models to optimize concrete mixes to achieve good balance between filling ability, passing ability, segregation, compressive strength, and cost is discussed. Examples highlighting the usefulness of the models are presented using isoresponse surfaces to demonstrate single and coupled effects of mix parameters on slump flow, loss of fluidity, flow resistance, segregation, JRing combined to Orimet, and compressive strength at 7 and 28 days. Cost analysis is carried out to show trade-offs between cost of materials and specified consistency levels and compressive strength at 7 and 28 days that can be used to identify economic mixes. The paper establishes the usefulness of the mathematical models as a tool to facilitate the test protocol required to optimise medium strength SCC.

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Historical GIS has the potential to re-invigorate our use of statistics from historical censuses and related sources. In particular, areal interpolation can be used to create long-run time-series of spatially detailed data that will enable us to enhance significantly our understanding of geographical change over periods of a century or more. The difficulty with areal interpolation, however, is that the data that it generates are estimates which will inevitably contain some error. This paper describes a technique that allows the automated identification of possible errors at the level of the individual data values.

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Descriptive characterizations of the point, the continuous, and the residual spectra of operators in Banach spaces are put forward. In particular, necessary and sufficient conditions for three disjoint subsets of the complex plane to be the point spectrum, the continuous spectrum, and the residual spectrum of a linear continuous operator in a separable Banach space are obtained.

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The electrochemical reduction of oxygen is reported in four room temperature ionic liquids (RTILs) based on quaternary alkyl -onium cations and heavily fluorinated anions in which the central atom is either nitrogen or phosphorus. Data were collected using cyclic voltammetry and potential step chronoamperometry at gold, platinum, and glassy carbon disk electrodes of micrometer dimension under water-free conditions at a controlled temperature. Analysis via fitting, to appropriate theoretical equations was then carried out to obtain kinetic and thermodynamic information pertaining to the electrochemical processes observed. In the quaternary ammonium electrolytes, reduction of oxygen was found to occur reversibly to give stable superoxide, in an analogous manner to that seen in conventional aprotic solvents such as dimethyl sufoxide and acetonitrile. The most significant difference is in the relative rate of diffusion; the diffusion coefficients of oxygen in the RTILs are an order of magnitude lower than in common organic solvents, and for superoxide these values are reduced by a further factor of 10. In the quaternary phosphonium ionic liquids, however, more complex voltammetry is observed, akin to that expected for the reduction of oxygen in acidified organic media. This is shown to be consistent with the occurrence of a proton abstraction reaction between the electrogenerated superoxide and quaternary alkyl phosphonium cations following the initial electron transfer.

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Many of the challenges faced in health care delivery can be informed through building models. In particular, Discrete Conditional Survival (DCS) models, recently under development, can provide policymakers with a flexible tool to assess time-to-event data. The DCS model is capable of modelling the survival curve based on various underlying distribution types and is capable of clustering or grouping observations (based on other covariate information) external to the distribution fits. The flexibility of the model comes through the choice of data mining techniques that are available in ascertaining the different subsets and also in the choice of distribution types available in modelling these informed subsets. This paper presents an illustrated example of the Discrete Conditional Survival model being deployed to represent ambulance response-times by a fully parameterised model. This model is contrasted against use of a parametric accelerated failure-time model, illustrating the strength and usefulness of Discrete Conditional Survival models.