scholarly journals Intellectual Property Protection and Technological Innovation of Chinese GEM Manufacturing Enterprise based on Research of Quantile Regression Model and Dynamic Panel Models

2016 ◽  
Author(s):  
Tianhe Cao
Keyword(s):  
Intellectual Property ◽  
Quantile Regression ◽  
Regression Model ◽  
Technological Innovation ◽  
Intellectual Property Protection ◽  
Dynamic Panel ◽  
Manufacturing Enterprise ◽  
Panel Models ◽  
Quantile Regression Model ◽  
Dynamic Panel Models

scholarly journals Improving estimations in quantile regression model with autoregressive errors

2018 ◽  
Vol 22 (Suppl. 1) ◽  
pp. 97-107 ◽  
Author(s):  
Bahadır Yuzbasi ◽  
Yasin Asar ◽  
Samil Sik ◽  
Ahmet Demiralp
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Simulation Experiment ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
White Noise Process ◽  
Respiratory Mortality ◽  
Explanatory Variables ◽  
Quantile Regression Model ◽  
Regression Approach

An important issue is that the respiratory mortality may be a result of air pollution which can be measured by the following variables: temperature, relative humidity, carbon monoxide, sulfur dioxide, nitrogen dioxide, hydrocarbons, ozone, and particulates. The usual way is to fit a model using the ordinary least squares regression, which has some assumptions, also known as Gauss-Markov assumptions, on the error term showing white noise process of the regression model. However, in many applications, especially for this example, these assumptions are not satisfied. Therefore, in this study, a quantile regression approach is used to model the respiratory mortality using the mentioned explanatory variables. Moreover, improved estimation techniques such as preliminary testing and shrinkage strategies are also obtained when the errors are autoregressive. A Monte Carlo simulation experiment, including the quantile penalty estimators such as lasso, ridge, and elastic net, is designed to evaluate the performances of the proposed techniques. Finally, the theoretical risks of the listed estimators are given.

Using Dynamic Panel Models to Study Age–Related Differences and Time–Related Changes in Personality

2019 ◽  
Vol 33 (3) ◽  
pp. 420-434 ◽  
Author(s):  
Jenny Wagner ◽  
Oliver Lüdtke ◽  
Manuel C. Voelkle
Keyword(s):  
Future Research ◽  
Continuous Variables ◽  
Dynamic Panel ◽  
Personality Psychology ◽  
Adult Lifespan ◽  
Panel Models ◽  
Age Related ◽  
Approaches To Study ◽  
Future Research Directions ◽  
Dynamic Panel Models

Along with an increasing interest in the plasticity and role of personality across the adult lifespan comes the need for a diverse set of innovative statistical approaches to study it. With this paper, we set out to illustrate some of the possibilities and challenges in modelling age–related differences and time–related changes in personality psychology by means of dynamic panel models. To this end, we first distinguish between the study of age–related differences and time–related changes and demonstrate how the treatment of age and time as either discrete or continuous variables implies important modelling choices. Second, we present a selection of four example cases that address the topic of age moderation in diverse matters and with different objectives. Based on our cross–tabulation of age and time as discrete and continuous variables, the first two example cases represent fairly well–established models (cases A and B), whereas the remaining cases are used to illustrate current developments in the field (cases C and D). We close the paper with some final remarks on current limitation and future research directions. © 2019 European Association of Personality Psychology

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