Regression Models of the Six Personal Values
Disentangling Topic Models: A Cross cultural Analysis of Personal Values through Words
... the regression models, we can inter- pret the coefficient attached to each attribute as the expected change in the usage of a particular theme as a result of a unit increase in the attribute, hold- ing all ...
10
On pseudo-values for regression analysis in competing risks models
... modeling is not straightforward because in practice, due to right censoring, the event times are often not observable for all patients. To overcome difficulties Andersen et al. (2003) proposed to use pseudo-values ...
15
Copula Regression Models for the Analysis of Correlated Data with Missing Values.
... and proceeds with the analysis using standard methods. Obviously, the data attrition is a concern, because the reduced sample size may result potentially in a substantial loss of estimation efficiency. For remedy, EM ...
127
Values of Asymptotic Efficiency in Linear Regression Models Concepts and Applications
... linear regression models such as normal, extreme- value for largest values and logistic ...the regression coefficients are estimated by ordinary least squares estimator and maximum likelihood ...
7
Optimal designs for estimating the control values in multi-univariate regression models
... © 2010 Elsevier Inc. All rights reserved. 1. Introduction In Chang et al. [1] an example concerning the production of the shadow mask which affects the quality of the screen image in a monitor or TV set is described, ...
12
REGRESSION MODELS
... linear regression can be used to fit a predictive model to an observed data set of y and X ...linear regression analysis can be applied to quantify the strength of the relationship between y and the X j , ...
11
Regression models
... Introduction Censored or truncated response variables occur in a variety of applications. Censored data arise if exact values are only reported in a restricted range. Data may fall outside this range but are ...
9
Analysis of Zero Inflated Over dispersed Count Data Regression Models with Missing Values
... 2.3 Comparisons of different approaches for handling missing data 17 Complete case analysis and the available case analysis only works nicely at the missing completely at random (MCAR) mechanism. Almost all statistical ...
132
Six Essays on Meta-Regression Analysis
... The other problematic issue of the spillover literature is publication bias. While the identified magnitude of bias is not extreme, it is high enough to produce statis- tically significant results where there are ...
287
Extending participatory sensing to personal exposure using microscopic land use regression models
... Keywords: personal exposure; health; policy; black carbon; noise; spatiotemporal models; air pollution; activity ...between personal exposure, external dose, internal dose, and the toxicology ...
17
The Role of Personal Values in Social Entrepreneurship
... the values, their least preference was ...the values concerned with self-control such as freedom and independence depending on their increasingly experiences in education ...other values is an ...
8
Imputation of missing values using quantile regression
... four models under two ...absolute values of the ratios of relative biases between the MI, PFI, NPI-EL and PQRI-EL estimators to that of PQRI-GMM estimator, as shown in Figure ...
193
Addressing multicollinearity in regression models: a ridge regression application
... constant values between zero and ...ridge regression method to address the multicollinearity problem in linear regression by finding a new estimator for the regression coefficients that have ...
21
Addressing multicollinearity in regression models: a ridge regression application
... constant values between zero and ...ridge regression method to address the multicollinearity problem in linear regression by finding a new estimator for the regression coefficients that have ...
21
VARIABLE SELECTION IN REGRESSION MODELS
... Classification models can be defined using Gaussian processes for underlying latent values, which can also be sampled within the Markov ...Bayesian regression and classification models, but ...
12
Dynamic Integration of Regression Models
... Linear Regression is a suitable meta-model so long as the coefficients of regression are constrained to be ...attribute values and the error for each model in predicting its target ...bine ...
10
The consolidation of forecests with regression models
... or values in a ...input values are missing and light green to designate the cells provided for user ...pertinent values, for example the post- holdout testing statistics for the user specified loss ...
292
Diagnostic in Poisson Regression Models
... Poisson regression, deviance, Pearson χ 2 , ...Poisson regression models have received much attention in econometrics and medicine literature as model for describing count data that assume integer ...
9
Intelligible Models for Classification and Regression
... the six regression datasets are shown in Figure ...on regression splines have very low variance, but sometimes at the expense of increased bias, while the best tree-based methods consistently have ...
9
Prediction Intervals for Regression Models
... Example 2.2. For the Buxton (1920) data suppose that the response Y = height and the predictors were a constant, head length, nasal height, bigonal breadth and cephalic index. Five outliers were deleted leaving 82 cases. ...
20