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Search Results: 1 - 10 of 36155 matches for " Multivariate exponential model "
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INFERENCE IN THE MULTIVARIATE EXPONENTIAL MODELS
David D. Hanagal
Journal of Reliability and Statistical Studies , 2009,
Abstract: Block (1975) extended bivariate exponential distributions (BVEDs) of Freund (1961)and Proschan and Sullo (1974) to multivariate case and called them as Generalized Freund-Weinman's multivariate exponential distributions (MVEDs). In this paper, we obtain MLEs of theparameters and large sample test for testing independence and symmetry of k components in thegeneralized Freund-Weinman's MVEDs.
Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach
Kaijian He,Kin Keung Lai,Guocheng Xiang
Energies , 2012, DOI: 10.3390/en5041018
Abstract: In the increasingly globalized economy these days, the major crude oil markets worldwide are seeing higher level of integration, which results in higher level of dependency and transmission of risks among different markets. Thus the risk of the typical multi-asset crude oil portfolio is influenced by dynamic correlation among different assets, which has both normal and transient behaviors. This paper proposes a novel multivariate wavelet denoising based approach for estimating Portfolio Value at Risk (PVaR). The multivariate wavelet analysis is introduced to analyze the multi-scale behaviors of the correlation among different markets and the portfolio volatility behavior in the higher dimensional time scale domain. The heterogeneous data and noise behavior are addressed in the proposed multi-scale denoising based PVaR estimation algorithm, which also incorporatesthe mainstream time series to address other well known data features such as autocorrelation and volatility clustering. Empirical studies suggest that the proposed algorithm outperforms the benchmark ExponentialWeighted Moving Average (EWMA) and DCC-GARCH model, in terms of conventional performance evaluation criteria for the model reliability.
Statistical Models for Forecasting Tourists’ Arrival in Kenya  [PDF]
Albert Orwa Akuno, Michael Oduor Otieno, Charles Wambugu Mwangi, Lawrence Areba Bichanga
Open Journal of Statistics (OJS) , 2015, DOI: 10.4236/ojs.2015.51008
Abstract: In this paper, an attempt has been made to forecast tourists’ arrival using statistical time series modeling techniques—Double Exponential Smoothing and the Auto-Regressive Integrated Moving Average (ARIMA). It is common knowledge that forecasting is very important in making future decisions such as ordering replenishment for an inventory system or increasing the capacity of the available staff in order to meet expected future service delivery. The methodology used is given in Section 2 and the results, discussion and conclusion are given in Section 3. When the forecasts from these models were validated, Double Exponential Smoothing model performed better than the ARIMA model.
Analysis of a Delayed SIR Model with Exponential Birth and Saturated Incidence Rate  [PDF]
Wanwan Wang, Maoxing Liu, Jinqing Zhao
Applied Mathematics (AM) , 2013, DOI: 10.4236/am.2013.410A2006
Abstract:

In this paper, a delayed SIR model with exponential demographic structure and the saturated incidence rate is formulated. The stability of the equilibria is analyzed with delay: the endemic equilibrium is locally stable without delay; and the endemic equilibrium is stable if the delay is under some condition. Moreover the dynamical behaviors from stability to instability will change with an appropriate critical value. At last, some numerical simulations of the model are given to illustrate the main theoretical results.

Predator Population Dynamics Involving Exponential Integral Function When Prey Follows Gompertz Model  [PDF]
Ayele Taye Goshu, Purnachandra Rao Koya
Open Journal of Modelling and Simulation (OJMSi) , 2015, DOI: 10.4236/ojmsi.2015.33008
Abstract: The current study investigates the predator-prey problem with assumptions that interaction of predation has a little or no effect on prey population growth and the prey’s grow rate is time dependent. The prey is assumed to follow the Gompertz growth model and the respective predator growth function is constructed by solving ordinary differential equations. The results show that the predator population model is found to be a function of the well known exponential integral function. The solution is also given in Taylor’s series. Simulation study shows that the predator population size eventually converges either to a finite positive limit or zero or diverges to positive infinity. Under certain conditions, the predator population converges to the asymptotic limit of the prey model. More results are included in the paper.
Modelling Epidemiological Data Using Box-Jenkins Procedure  [PDF]
Stanley Jere, Edwin Moyo
Open Journal of Statistics (OJS) , 2016, DOI: 10.4236/ojs.2016.62025
Abstract: In this paper, the Box-Jenkins modelling procedure is used to determine an ARIMA model and go further to forecasting. We consider data of Malaria cases from Ministry of Health (Kabwe District)-Zambia for the period, 2009 to 2013 for age 1 to under 5 years. The model-building process involves three steps: tentative identification of a model from the ARIMA class, estimation of parameters in the identified model, and diagnostic checks. Results show that an appropriate model is simply an ARIMA (1, 0, 0) due to the fact that, the ACF has an exponential decay and the PACF has a spike at lag 1 which is an indication of the said model. The forecasted Malaria cases for January and February, 2014 are 220 and 265, respectively.
Seasonal Variation of Carbon and Nitrogen Emissions from Turfgrass  [PDF]
Said A. Hamido, Elizabeth A. Guertal, C. Wesley Wood
American Journal of Climate Change (AJCC) , 2016, DOI: 10.4236/ajcc.2016.54033
Abstract: The role of turfgrasses in C and N cycling in the southeastern U.S. has not been well documented. The objectives of this research were to determine the characterization of chemical quality, clipping decomposition rates, and C and N release from warm- and cool-season turfgrasses. The study was conducted for 46 weeks in 2012 in Auburn, AL. Four warm season turfgrasses were used included (bermudagrass [Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt Davy], centipedegrass (Eremochloa ophiuroides (Munro) Hack), St. Augustinegrass (Stenotaphrum secundatum (Walter) Kuntze), zoysiagrass (Zoysia japonica Steud.), and one cool season turfgrass (tall fescue (Festuca arundinacea Schreb)). Litter was placed into nylon bags at an oven dry rate of 3.6 Mg?ha?1. Litter bags were retrieved after 0, 1, 2, 4, 8, 16, 24, 32, and 46 weeks, and analyzed for total C and N. A double exponential decay model was used to describe mass, C, and N loss. Results indicated that tall fescue decomposition occurred rapidly compared to warm season turfgrasses. Litter mass loss measured after 46 weeks was determined to be 61.7%, 73.7%, 72.2%, 86.8%, and 45.4% in bermudagrass, centipedegrass, St. Augustinegrass, tall fescue, and zoysiagrass respectively. Zoysiagrass litter had a higher lignin concentration, while tall fescue had the lowest lignin. Over 46 weeks’ release of C was in the order: zoysiagrass > bermudagrass = centipedegrass = St. Augustinegrass > tall fescue, and release of N was in the order zoysiagrass > centipedegrass > bermudagrass = St. Augustinegrass > tall fescue. Our study concluded that, zosiagrass is a better choice for home lawns.
Exploring the Priced Factors in ICAPM in Japan  [PDF]
Chikashi TSUJI
Modern Economy (ME) , 2011, DOI: 10.4236/me.2011.24078
Abstract: This paper investigates the priced factors in the Intertemporal Capital Asset Pricing Model (ICAPM) in the Tokyo Stock Exchange (TSE) in Japan. Focusing on the time-varying covariance risks derived by the multivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, we find new priced state variables in Japan. That is, our empirical tests reveal that in the TSE, the time-varying covariance between market return and illiquidity measure and that between market return and the log change of the seasonally adjusted industrial production are statistically significantly priced state variables in the ICAPM.
Estimation of Natural Gas Production, Import and Consumption in Brazil Based on Three Mathematical Models  [PDF]
Antonio Carlos Gracias, Sérgio Ricardo Louren?o, Marat Rafikov
Natural Resources (NR) , 2012, DOI: 10.4236/nr.2012.32007
Abstract: A mathematical model capable of providing a forecast of future consumption and import of natural gas is essential for the planning of the Brazilian energy matrix. The aim of this study is to compare three mathematical models, logistic model or model of Verhulst, exponential model or the model of Malthus and the model of von Bertalanffy to analyze the possibilities of these models to describe the evolution of production, import and consumption of natural gas in Brazil, from data provided by the energy balance of the Ministry of Mines and Energy (MME) from 1970 to 2009. A projection of the production and the import of natural gas up to 2017 is made with the models studied in this article and compared with the Brazilian Ten-Year Plan for Expansion of Energy (PDE). At the end of this paper a comparison with the Hubbert model for Brazilian natural gas production is made. These data were adjusted to use the differential equations which describe the models of population growth. All the computer work used in this article: graphics, resolution of differential equations, calculations of linearization and the least squares fitting was prepared in the software MatLab. The results obtained by means of graphs show that the population dynamics models (logistic, exponential and von Bertalanffy) can be applied in modeling the production, import and consumption of natural gas in Brazil.
Semiparametric Estimation of Multivariate GARCH Models  [PDF]
Claudio Morana
Open Journal of Statistics (OJS) , 2015, DOI: 10.4236/ojs.2015.57083
Abstract:

The paper introduces a new simple semiparametric estimator of the conditional variance-covariance and correlation matrix (SP-DCC). While sharing a similar sequential approach to existing dynamic conditional correlation (DCC) methods, SP-DCC has the advantage of not requiring the direct parameterization of the conditional covariance or correlation processes, therefore also avoiding any assumption on their long-run target. In the proposed framework, conditional variances are estimated by univariate GARCH models, for actual and suitably transformed series, in the first step; the latter are then nonlinearly combined in the second step, according to basic properties of the covariance and correlation operator, to yield nonparametric estimates of the various conditional covariances and correlations. Moreover, in contrast to available DCC methods, SP-DCC allows for straightforward estimation also for the non-symultaneous case, i.e. for the estimation of conditional cross-covariances and correlations, displaced at any time horizon of interest. A simple expost procedure to ensure well behaved conditional variance-covariance and correlation matrices, grounded on nonlinear shrinkage, is finally proposed. Due to its sequential implementation and scant computational burden, SP-DCC is very simple to apply and suitable for the modeling of vast sets of conditionally heteroskedastic time series.

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