STATIONARY AND NON-STATIONARY STATE SPACE MODELS

Abstract. The concepts of time invariance, stationarity, non-stationarity and immemorial time are considered for state space models (SSMs). Necessary and sufficient conditions for stationarity are given and connected with standard conditions. Expressions are given for the unconditional mean and covariance matrix of the state of a time-immemorial SSM. Application of the results is made to a variety of theoretical and empirical models and the initialization of the Kalman filter in the non-stationary case and for the ARIMA ( p, d, q ) model.     

测井相识别模型及其在东海油气勘探中的应用

文章提出一种测井相识别模型,该模型用常规测井信息,定量地确定测井相在钻遇地层中的空间位置,建立测井相。配合东海油气勘探工作,用该识别模型对苏堤构造带的钻遇地层进行了测井相分析。     

Batch cultures of Penicillium camembertii on glucose and amino acids—model for growth and ammonium production

BACKGROUND: Three groups of amino acids were previously characterized based on their ability to be assimilated as carbon source by Penicillium camembertii. To describe the diauxic growth recorded on glucose and amino acids from the second group, such as arginine, an unstructured model was previously developed, based on the sequential consumption of both carbon substrates, glucose, followed after its exhaustion, by arginine. The model was modified to describe also the behaviour recorded during growth on other amino acids. RESULTS: The growth model involved the carbon substrate consumption (Verlhust model) and the biomass on carbon substrate yield. Glucose was therefore considered during P. camembertii growth on nitrogen source amino acids (lysine—first group); and amino acid consumption was considered during growth on carbon source amino acids (glutamate—third group), with glucose being dissimilated only for energy supply. The excess nitrogen from amino acids was released as ammonium; the linking of this production to growth was found to increase with the ability of the amino acid to be assimilated as carbon source by P. camembertii. CONCLUSION: The various metabolic behaviours recorded during P. camembertii growth on amino acids, in the presence of a primary carbon source such as glucose, were proved to be satisfactorily described by the model, showing the robustness of the model. Copyright © 2007 Society of Chemical Industry     

Approximating the problem, not the solution: An alternative view of point set matching

This work discusses the issue of approximation in point set matching. In general, one may have two classes of approximations when tackling a matching problem: (1) an algorithmic approximation which consists in using suboptimal procedures to infer the assignment, and (2), a representational approximation which involves a simplified and suboptimal model for the original data. Matching techniques have typically relied on the first approach by retaining the complete model and using suboptimal techniques to solve it. In this paper, we show how a technique based on using exact inference in simple Graphical Models, an instance of the second class, can significantly outperform instances of techniques from the first class. We experimentally compare this method with well-known Spectral and Relaxation methods, which are exemplars of the first class. We have performed experiments with synthetic and real-world data sets which reveal significant performance improvement in a wide operating range.     

Inference for pth‐order random coefficient integer‐valued autoregressive processes

Abstract. A pth‐order random coefficient integer‐valued autoregressive [RCINAR(p)] model is proposed for count data. Stationarity and ergodicity properties are established. Maximum likelihood, conditional least squares, modified quasi‐likelihood and generalized method of moments are used to estimate the model parameters. Asymptotic properties of the estimators are derived. Simulation results on the comparison of the estimators are reported. The models are applied to two real data sets.     

Simulation-based sequential analysis of Markov switching stochastic volatility models

We propose a simulation-based algorithm for inference in stochastic volatility models with possible regime switching in which the regime state is governed by a first-order Markov process. Using auxiliary particle filters we developed a strategy to sequentially learn about states and parameters of the model. The methodology is tested against a synthetic time series and validated with a real financial time series: the IBOVESPA stock index (São Paulo Stock Exchange).     

Does social dominance generate prejudice? Integrating individual and contextual determinants of intergroup cognitions.

Social dominance orientation (SDO) has been proposed as an important variable in the explanation of prejudice. We distinguish between three conceptualizations of SDO: SDO as a personality trait (personality model), SDO as a moderator of the effects of situational variables (Person X Situation model), and SDO as a mediator of the effect of social position on prejudice (group socialization model [GSM]). Four studies (N = 1,657) looking at the relations between social positions, SDO, and prejudice in a natural setting and in a laboratory setting provide strong support for the GSM. In contrast to previous correlational findings, there is evidence of a cause (dominant social position), an effect (prejudice increases), and a mediator (SDO). These results suggest new perspectives on the integration of individual and contextual determinants of prejudice. (PsycINFO Database Record (c) 2010 APA, all rights reserved)