stat project with matlab

İptal Edildi İlan edilme: 7 yıl önce Teslim sırasında ödenir
İptal Edildi Teslim sırasında ödenir

My thesis title is "inference methods for the conditional logistic regression model with longitudinal data and their application in environmental pollution." I have been using the methods "Maximum Likelihood Estimate" (MLE) and "generalized estimating equations" (GEE) and used the criteria is QIC, CIC, GPC, RJC, AGPC, BGPC, EAIC, EBIC and DEW for choose the models and determine the working correlation structure (IN, EX, AR-1, ST) . (File attachment) I need to evaluating these criteria and identifying patterns of correlation matrix of the data about environmental pollution, which represents daily measurements based on the time from September 2011 to December 2013. (File attachment). Please prepare the code for programming these criteria in MATLAB. The initially importing of longitudinal data from Excel to MATLAB for further analysis in order to measuring the amount of information loss of the candidate models selected in accordance with the criteria and determine the best candidate model, which is the model used to represent the generating model, with the preferred model being a model that reduces the loss of information by having a smaller difference for (Kullback-Leibler) which is reason in choose the model with the lowest value when the use of information criteria such as AIC. A generating model is the model that is used to represent random variations in observations in terms of explanatory variables. Model selection was used to determine the effects of the explanatory variables in the response variables where the QIC model selection criterion shows the lowest information loss of the explanatory variables relative to Nitrogen Oxides and Carbon dioxide. At the beginning you must treat the data from the multicollinearity by delete a variable with highest Variance inflation factors (VIF) is a measure how much the variance of the estimated regression coefficients are inflated as compared to when the predictor variables are not linearly related. It is used to describe how much multicollinearity exists in a regression analysis. Multicollinearity is problematic because it can increase the variance of the regression coefficients, making them unstable and difficult to interpret. We remove high VIF since it implies that the predictors are highly correlated and will therefore skew the results. Please, I hope you will kindly for see the attached files. In addition, I need to calculate the polynomial matrix, which depends on all the data submitted to show up alongside with the output of the simulation, please make sure that? Please create the simulation, supposed to be the simulation results of the same values in the tables listed in the attached document. (The simulation results of Model Selection Criteria 1) Send me the code for Model Selection Criteria in order to let me implemented in my laptop

Matlab ve Mathematica İstatistiksel Analiz İstatistik

Proje NO: #10086351

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6 teklif Uzak proje Aktif 7 yıl önce

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ahmedbassiouny

I have a bachelor in CS from the American University in Cairo and a minor in Mathematics. I have worked for the past year in Microsoft's Advanced Technology Lab in Cairo (ATLC) as a research assistant in the Computer V Daha Fazla

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Hi! My name is Fernando Téllez. I am a senior year student of electrical engineering at Universidad Simón Bolívar (USB), one of the most prestigious universities in my country (Ranked 34° at the QS University Rankings: Daha Fazla

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rajeshkumararora

Hello I am a PhD and completed a anunber of projects related to matlab and statistics. Please provide details. Thanks,

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pinetree804

Hello, Dear Friend. I am expert in matlab, mathematics, physics,... I have a lot of experiences in many fields. I can help you well. Please contact me. Best regards.

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