Cumulative odds Logistic regression model with applies
Diagnostic and influence analysis for Poisson regression model
The model had higher prediction precision compared with traditional regression model.
Construction and prediction of the main composition regression model of the gross vegetables output of China;
The model is tested and result shows that this prediction model is significantly better than traditional regression model, and it has a good prospect in business application.
This paper gives weighted least squares estimate and the method to choose optimum weighted function for linear regression model.
Method Questionnaire and human capital method were used in calculation of economic burden of disease and the multi linear regression model was explored to analyze the factors.
Aimed at the variable changes of the practical application of gray economic anticipation at the present time and based on the practical investigation and anticipation experience, a new model of gray linear regression model based on gray theory and linear regression theory is presented.
According to the theory model we construct a double logarithm regression model, study and analyze with cross section and time series data , then draw a conclusion that the difference of foreign direct investment causes our district economy difference in a certain degree;
These forecast results were compared with those of regression models.
The probabilistic properties of functional coefficient auto-regression models with regularly varying tailed are discussed.
classical normal linear regression model
On separated estimation for semiparametric regression model under independent case
Based on a descriptive analysis, this paper adopts the 2-logistic regression model for regression statistical analysis.
Edgeworth expansion of random weighting estimation in semi-parametric regression model
A projection pursuit regression model for synthetic earthquake prediction based on particle swarm optimization and Hermite polynomial fitting
After quantitative index analysis of the continuous course score and enrollment types, a regression model is established to forecast after course study score.
(3) According to the results of the regression model, the factors of sociability, communication advantages, anxiety, openness and competency were the powerful predictors for the usage degree of the Internet.
The linear regression model is used to calculate and measure the effect of investment to economic growth in regions of the Bohai, Yangtze River Delta, Pearl River Delta and the Central Region of China, and different degrees of investment dement in the interior of the four regions.
Parameter estimation method of logistic regression models based on particle swarm optimization algorithm
regression model for fertility estimation
Examples on volume equation of multiplicative regression model are illustrated.
Optimized relative linear estimation on the parameters of fuzzy linear regression model;
Let's continue this example of a house price-based regression model, and create some real data to examine.
Test and regression model on critical moisture content of carrot and drying rate during the stage of constant rate by vacuum microwave drying
a priori information in regression models
simple linear regression model
We change the truncated regression model into the semiparametric regression model.
Analysis to once basic linearity regression model by applying the Excel
The nonlinear multiple spline progressive regression model is used in the medium-range prediction of sununer harvest.
The regression model, in which monthly average temperature varied with longitude and altitude, was established by data of longitude, latitude and altitude obtained by using GPS on the spot.
Support vector regression modeling and backstepping control of friction in servo system
strong consistency of wavelet estimation in the semiparametric regression model
A regression model about soil bulk density, moisture content and germinating seed rate was established with the method of quadratic orthogonal rotary regression.
By establishing bivariate regression model, this paper makes an empirical analysis on the correlativity between CHIBOR and price fluctuation in securities market.
general linear regression model
Application of projection pursuit auto regression model in predicting runoff of Yangtze River
After describing the type and the reason of the contracted-land adjustment, the author analyzes the factors to influence the land adjustment by multi-regression model.
time series regression model
Section 2. deals with estimating a regression model that depends on principal component factors instead of explanatory variables.
Some problems on heteroscedasticity in multi-linear regression models;
Through the use of linear model and nonparametric regression model, the trend of the total foreign exchange reserve account for the proportion of GDP is fitted.
In this paper, we discuss the estimate of regression function in nonparametric regression model based on exponential integral martingale difference.
stochastic assumption of linear regression model
The study shows that the precision of the improved model is better than the stepwise regression model.
Linear strong representative of M-estimators in semiparametric regression models;
The regression model also reveals that family modeling and subcultural modeling, which are both important to juvenile delinquency in the social learning theory, have given place to medium violence.
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