The Experts below are selected from a list of 265350 Experts worldwide ranked by ideXlab platform
Qin Cui - One of the best experts on this subject based on the ideXlab platform.
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A new five-parameter Logistic Model for describing the evolution of energy consumption
Energy Sources Part B-economics Planning and Policy, 2016Co-Authors: Junmeng Cai, Rongrong Liu, Jie Xiong, Qin CuiAbstract:ABSTRACTThe four-parameter Logistic Model is commonly used to describe sigmoidal growth processes. However, the four-parameter Logistic Model is a symmetrical Model. The historical data on energy consumption are not symmetrical. Thus, the four-parameter Logistic Model cannot accurately describe the evolution of energy consumption. In this article, a new five-parameter Logistic Model has been proposed. The new Model is extended from the four-parameter Logistic Model by the addition of an extra parameter. Some properties of the new Model have been analyzed through numerical parametric study. Finally, the new Model has been successfully applied to describe the evolution of natural-gas consumption in China, the evolution of natural-gas consumption in Spain, and the evolution of electricity consumption in Morocco.
Asaduzzaman Shah - One of the best experts on this subject based on the ideXlab platform.
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Stochastic Logistic Model for Fish Growth
Open Journal of Statistics, 2014Co-Authors: Asaduzzaman ShahAbstract:Two extensions of stochastic Logistic Model for fish growth have been examined. The basic features of a Logistic growth rate are deeply influenced by the carrying capacity of the system and the changes are periodical with time. Introduction of a new parameter , enlarges the scope of investing the growthof different fish species. For rapid growth lying between 1 and 2 and for slowly growing.
Samy Suissa - One of the best experts on this subject based on the ideXlab platform.
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The impact of unmeasured baseline effect modification on estimates from an inverse probability of treatment weighted Logistic Model.
European Journal of Epidemiology, 2009Co-Authors: Joseph A. Delaney, Robert W. Platt, Samy SuissaAbstract:We present the results of a Monte Carlo simulation study in which we demonstrate how strong baseline interactions between a confounding variable and a treatment can create an important difference between the marginal effect of exposure on outcome (as estimated by an inverse probability of treatment weighted Logistic Model) and the conditional effect (as estimated by an adjusted Logistic regression Model). The scenarios that we explored included one with a rare outcome and a strong and prevalent effect measure modifier where, across 1,000 simulated data sets, the estimates from an adjusted Logistic regression Model (mean β = 0.475) and an inverse probability of treatment weighted Logistic Model (mean β = 2.144) do not coincide with the known true effect (β = 0.68925) when the effect measure modifier is not accounted for. When the marginal and conditional estimates do not coincide despite a rare outcome this may suggest that there is heterogeneity in the effect of treatment between individuals. Failure to specify effect measure modification in the statistical Model appears to results in systematic differences between the conditional and marginal estimates. When these differences in estimates are observed, testing for or including interactions or non-linear Modeling terms may be advised.
Guangmeng Guo - One of the best experts on this subject based on the ideXlab platform.
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Improvement of fire danger Modelling with geographically weighted Logistic Model
International Journal of Wildland Fire, 2014Co-Authors: Haijun Zhang, Guangmeng GuoAbstract:Global Models dominate historical documents on fire danger Modelling. However, local variations may exist in the relationships between fire presence and fire-influencing factors. In this study, 50 fire danger Models (10 global Logistic Models and 40 geographically weighted Logistic Models, i.e. local Models), were developed to Model daily fire danger in Heilongjiang province in north-east China and cross-validation was performed to evaluate the predictive performance of the various developed Models. In Modelling, multi-temporal spatial sampling and repeated random sub-sampling were applied to obtain 10 groups of training sub-samples and inner testing sub-samples. For each of the 10 groups of training sub-samples, principal component analysis, in which muticollinearity among variables can be removed, was used to create nine principal components that were then employed as covariates to develop one global Logistic Model and four geographically weighted Logistic Models. Compared to global Models, all local Models showed better Model fitting, less spatial autocorrelation of residuals and more desirable Modelling of fire presence. In particular, not only was local spatial variation in fire–environment relationships accounted for in the adaptive Gaussian geographically weighted Logistic Models, but spatial autocorrelation of residuals was significantly reduced to acceptable levels, indicating strong inferential performance.
Mark A Hall - One of the best experts on this subject based on the ideXlab platform.
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speeding up Logistic Model tree induction
European conference on Machine Learning, 2005Co-Authors: Marc Sumner, Eibe Frank, Mark A HallAbstract:Logistic Model Trees have been shown to be very accurate and compact classifiers [8]. Their greatest disadvantage is the computational complexity of inducing the Logistic regression Models in the tree. We address this issue by using the AIC criterion [1] instead of cross-validation to prevent overfitting these Models. In addition, a weight trimming heuristic is used which produces a significant speedup. We compare the training time and accuracy of the new induction process with the original one on various datasets and show that the training time often decreases while the classification accuracy diminishes only slightly.
