A collection of parametric modal regression models for bounded data

Author(s):  
André F. B. Menezes ◽  
Josmar Mazucheli ◽  
Subrata Chakraborty
2018 ◽  
Vol 19 (6) ◽  
pp. 617-633 ◽  
Author(s):  
Wagner H Bonat ◽  
Ricardo R Petterle ◽  
John Hinde ◽  
Clarice GB Demétrio

We propose a flexible class of regression models for continuous bounded data based on second-moment assumptions. The mean structure is modelled by means of a link function and a linear predictor, while the mean and variance relationship has the form [Formula: see text], where [Formula: see text], [Formula: see text] and [Formula: see text] are the mean, dispersion and power parameters respectively. The models are fitted by using an estimating function approach where the quasi-score and Pearson estimating functions are employed for the estimation of the regression and dispersion parameters respectively. The flexible quasi-beta regression model can automatically adapt to the underlying bounded data distribution by the estimation of the power parameter. Furthermore, the model can easily handle data with exact zeroes and ones in a unified way and has the Bernoulli mean and variance relationship as a limiting case. The computational implementation of the proposed model is fast, relying on a simple Newton scoring algorithm. Simulation studies, using datasets generated from simplex and beta regression models show that the estimating function estimators are unbiased and consistent for the regression coefficients. We illustrate the flexibility of the quasi-beta regression model to deal with bounded data with two examples. We provide an R implementation and the datasets as supplementary materials.


Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 651
Author(s):  
Hao Deng ◽  
Jianghong Chen ◽  
Biqin Song ◽  
Zhibin Pan

Due to their flexibility and interpretability, additive models are powerful tools for high-dimensional mean regression and variable selection. However, the least-squares loss-based mean regression models suffer from sensitivity to non-Gaussian noises, and there is also a need to improve the model’s robustness. This paper considers the estimation and variable selection via modal regression in reproducing kernel Hilbert spaces (RKHSs). Based on the mode-induced metric and two-fold Lasso-type regularizer, we proposed a sparse modal regression algorithm and gave the excess generalization error. The experimental results demonstrated the effectiveness of the proposed model.


Author(s):  
Ricardo R. Petterle ◽  
César A. Taconeli ◽  
José L. P. da Silva ◽  
Guilherme P. da Silva ◽  
Henrique A. Laureano ◽  
...  

2020 ◽  
Vol 21 (2) ◽  
pp. 169-194
Author(s):  
Marta Kajzer-Wietrzny ◽  
Ilmari Ivaska

Empirical Translation Studies have recently extended the scope of research to other forms of constrained and mediated communication, including bilingual communication, editing, and intralingual translation. Despite the diversity of factors accounted for so far, this new strand of research is yet to take the leap into intermodal comparisons. In this paper we look at Lexical Diversity (LD), which under different guises, has been studied both within Translation Studies (TS) and Second Language Acquisition (SLA). LD refers to the rate of word repetition, and vocabulary size and depth, and previous research indicates that translated and non-native language tends to be less lexically diverse. There is, however, no study that would investigate both varieties within a unified methodological framework. The study reported here looks at LD in spoken and written modes of constrained and non-constrained language. In a two-step analysis involving Exploratory Factor Analysis and linear mixed-effects regression models we find interpretations to be least lexically diverse and written non-constrained texts to be most diverse. Speeches delivered impromptu are less diverse than those read out loud and the non-constrained texts are more sensitive to such delivery-related differences than the constrained ones.


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