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Search Results: 1 - 10 of 33211 matches for " empirical model "
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An Empirical Study of Blended Teaching Model in University English Teaching  [PDF]
Shenying Jiang, Danli Li
Creative Education (CE) , 2012, DOI: 10.4236/ce.2012.34076
Abstract: It has been seven years since Education Ministry issued College English Curriculum Trail Requirements. It is of manifest necessity to conduct a survey about the effectiveness application under blended teaching model in common university. This paper adopts consecutive three years’ empirical study of blended teaching model and analyzes the data from both English teachers’ and students’ viewpoint. Then it discovers some problems and proposes corresponding suggestions with the purpose to facilitate the improvement of the English teaching in universities.
Imputed Empirical Likelihood for Varying Coefficient Models with Missing Covariates  [PDF]
Peixin Zhao
Open Journal of Applied Sciences (OJAppS) , 2013, DOI: 10.4236/ojapps.2013.31B1009

The empirical likelihood-based inference for varying coefficient models with missing covariates is investigated. An imputed empirical likelihood ratio function for the coefficient functions is proposed, and it is shown that iis limiting distribution is standard chi-squared. Then the corresponding confidence intervals for the regression coefficients are constructed. Some simulations show that the proposed procedure can attenuate the effect of the missing data, and performs well for the finite sample.

Inference Based on Empirical Likelihood for Varying Coefficient Model with Random Effect  [PDF]
Wanbin Li, Liugen Xue
Open Journal of Statistics (OJS) , 2013, DOI: 10.4236/ojs.2013.36A006

In this article, we develop a statistical inference technique for the unknown coefficient functions in the varying coeffi- cient model with random effect. A residual-adjusted block empirical likelihood (RABEL) method is suggested to inves- tigate the model by taking the within-subject correlation into account. Due to the residual adjustment, the proposed RABEL is asymptotically chi-squared distribution. We illustrate the large sample performance of the proposed method via Monte Carlo simulations and a real data application.

Empirical Likelihood Diagnosis of Modal Linear Regression Models  [PDF]
Shuling Wang, Lin Zheng, Jiangtao Dai
Journal of Applied Mathematics and Physics (JAMP) , 2014, DOI: 10.4236/jamp.2014.210107
Abstract: In this paper, we investigate the empirical likelihood diagnosis of modal linear regression models. The empirical likelihood ratio function based on modal regression estimation method for the regression coefficient is introduced. First, the estimation equation based on empirical likelihood method is established. Then, some diagnostic statistics are proposed. At last, we also examine the performance of proposed method for finite sample sizes through simulation study.
The Development of a Simulated Sediment Dosing Apparatus for Deposition Research in Wastewater Collection Systems  [PDF]
David Campbell, Sean Mushin, Craig Saunders
Journal of Water Resource and Protection (JWARP) , 2018, DOI: 10.4236/jwarp.2018.105026
Abstract: Water conservation initiatives promote installation of water efficient and low-flow appliances in waste water collection systems. This has resulted in lower flow rates in those systems than the intended design loading, causing solid deposition and sedimentation in some areas. A joint UKWIR/EPSRC CASE grant (14440031) has funded the work described in this paper which investigates sedimentation and solid deposition in building drainage system pipes. The purpose of this paper is to detail the design, calibration and operation of a sediment dosing apparatus to simulate sedimentation rates and explore possible solutions to this issue with a full scale laboratory model based on real site data. The methodology adopted is an experimental approach, where tests have been conducted on the sediment dosing apparatus based on calculations and observations to determine an appropriate sediment dosing regime representative of typical systems. Further tests were conducted with the addition of everyday household products to investigate their effects on sedimentation. The results indicated that a suitable dosing rate was approximately 12% weight-to-volume (w/v) of a fine sand with a known particle size distribution, diluted 1:5 in a clean water base flow. It was also shown that the addition of the household products added to the problem of sedimentation within drainage systems. The results give excellent correlation to real site data, with deposition depth and distribution comparable to measured site data to within 10%. The deposition was achieved within three hours, which approximated six weeks deposition in the live site used in the study. This straightforward investigation details the design, construction and testing of a device to cause accelerated sedimentation in a full scale model of a building drainage system. This is the first step in the process of updating research underpinning our understanding of the behaviour of these systems under conditions of low flow rates caused by water conservation, sedimentation, and the use of common household additives. It will be used to improve simulation of water flow and solid transport in sediment-laden systems. Specifically, the results will be used to determine refinements required to a specific drainage simulation model (DRAINET), which currently has an unquantified sedimentation component. This work is part of a larger body of current research funded by two joint EPSRK/UKWIR grants.
Oil Price Forecasting Based on EMD and BP_AdaBoost Neural Network  [PDF]
Huifang Qu, Guoqiang Tang, Qiying Lao
Open Journal of Statistics (OJS) , 2018, DOI: 10.4236/ojs.2018.84043
Abstract: Empirical mode decomposition (EMD) and BP_AdaBoost neural network are used in this paper to model the oil price. Based on the benefits of these two methods, we predict the oil price by using them. To a certain extent, it effectively improves the accuracy of short-term price forecasting. Forecast results of this model are compared with the results of the ARIMA model, BP neural network and EMD-BP combined model. The experimental result shows that the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and Theil inequality (U) of EMD and BP_AdaBoost model are lower than other models, and the combined model has better prediction accuracy.
Optimization of the Conceptual Model of Green-Ampt Using Artificial Neural Network Model (ANN) and WMS to Estimate Infiltration Rate of Soil (Case Study: Kakasharaf Watershed, Khorram Abad, Iran)  [PDF]
Ali Haghizadeh, Leila Soleimani, Hossein Zeinivand
Journal of Water Resource and Protection (JWARP) , 2014, DOI: 10.4236/jwarp.2014.65047

Determination of the infiltration rate in a watershed is not easy and in empirical and theoretical point of view, it is important to access average value of infiltration. Infiltration models has main role in managing water sources. Therefore different types of models with various degrees of complexity were developed to reach this aim. Most of the estimating methods of soil infiltration are expensive and time consuming and these methods estimate infiltration with hypothesis of zero slope. One of the conceptual and physical models for estimating soil infiltration is Green-Ampt model which is similar to Richard model. This model uses slope factor in estimating infiltration and this is the power point of Green-Ampt model. In this research the empirical model of Green-Ampt was optimized with integrating artificial neural network model (ANN) and a model of geographical information system WMS to estimate the infiltration in Kakasharaf watershed. Results of the comparison between the output of this method and real value of infiltration in region (through multiple cylinders) showed that this method can estimate the infiltration rate of Kakasharaf watershed with low error and acceptable accuracy (Nash-Sutcliff performance coefficient 0.821, square error 0.216, correlation coefficient 0.905 and model error 0.024).

Estimativa das perdas de produtividade de gr?os em cultivares de arroz (Oryza sativa) pela interferência do capim-arroz (Echinochloa spp.)
Galon, L.;Agostinetto, D.;Moraes, P.V.D.;Tironi, S.P.;Dal Magro, T.;
Planta Daninha , 2007, DOI: 10.1590/S0100-83582007000400006
Abstract: the objectives of this research were to evaluate the level of barnyardgrass interference in flooded rice cultivars and to compare explicative variables aiming to identify the variable providing better data adjustment to a mathematical model. thus, a field experiment was carried out during the 2005/2006 growing season, with rice cultivated under the conventional system. treatments consisted of six rice cultivars differing in life cycle duration and ten barnyardgrass plant populations. variables were evaluated 28 days after rice emergence. the rectangular hyperbolic model was tested to describe the relationship between rice grain yield loss and explicative variables in plants, dry weight, soil coverage and leaf area. rice grain yield losses due to barnyardgrass interference may be satisfactorily estimated by the hyperbolic model. irga 421, 416, and 417 rice cultivars were the most competitive, attaining suitable data fitting to the model tested for all evaluated variables. the variable barnyardgrass plant population presents better adjustment to the model than do shoot dry matter mass, soil cover, or leaf area.
Modelagem dos efeitos do vento sobre as dimens?es do alcance do jato de um canh?o hidráulico
Oliveira, Henrique F. E. de;Colombo, Alberto;Faria, Lessandro C.;
Revista Brasileira de Engenharia Agrícola e Ambiental , 2009, DOI: 10.1590/S1415-43662009000700002
Abstract: the hypothesis of a linear relationship between wetted area of a sprinkler and the wind velocity which is assumed by the richards & weatherhead semi empirical model for simulation of wind effects on gun type water application distribution was evaluated. distances from the plona-rl250 gun type sprinkler to the upwind, crosswind and downwind edges was determined in 53 field testes, held under different wind conditions and were used to evaluate the suitability of the proposed linear relationship. a linear least square fit for each set of data indicates that, for each 1 m s-1 increase in wind velocity, there is: (i) a 6.3% decrease on the distance to the upwind edge (r2 = 0.737); (ii) a 7.3% decrease on the distance to the crosswind edge (r2 = 0.850); and (iii) a 1.3% increase on downwind edge distance in relation to the no-wind radius of throw. these results indicate that, for the range of wind velocities considered in this study (from 0 up to 5 m s-1) the linear relationship between sprinkler reach and wind velocity can be accepted.
A Statistical Model for Long-Term Forecasts of Strong Sand Dust Storms  [PDF]
Siqi Tan, Moinak Bhaduri, Chih-Hsiang Ho
Journal of Geoscience and Environment Protection (GEP) , 2014, DOI: 10.4236/gep.2014.23003

Historical evidence indicates that dust storms of considerable ferocity often wreak havoc, posing a genuine threat to the climatic and societal equilibrium of a place. A systematic study, with emphasis on the modeling and forecasting aspects, thus, becomes imperative, so that efficient measures can be promptly undertaken to cushion the effect of such an unforeseen calamity. The present work intends to discover a suitable ARIMA model using dust storm data from northern China from March 1954 to April 2002, provided by Zhou and Zhang (2003), thereby extending the idea of empirical recurrence rate (ERR) developed by Ho (2008), to model the temporal trend of such sand dust storms. In particular we show that the ERR time series is endowed with the following characteristics: 1) it is a potent surrogate for a point process, 2) it is capable of taking advantage of the well developed and powerful time series modeling tools and 3) it can generate reliable forecasts, with which we can retrieve the corresponding mean number of strong sand dust storms. A simulation study is conducted prior to the actual fitting, to justify the applicability of the proposed technique.

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