Partial linear regression of compositional data

Han, Hyebin; Yu, Kyusang

doi:10.1007/s42952-022-00177-1

Cited by 2 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that H may be replaced by any orthogonal matrix which preserves distances (Tsagris et al, 2011). Simplicial-simplicial regression based on the ilr tranformation (Chen et al, 2017, Han and Yu, 2022, Wang et al, 2013 is similar to alr regression and is carried out by transforming both the response and the predictor variables via the alr (2) or the ilr (4) transformations…”

Section: Review Of Simplicial-simplicial Regression Modelsmentioning

confidence: 99%

“…Most published papers regarding the last case scenario involve transformations of both simplicial sides. Hron et al (2012), Wang et al (2013), Chen et al (2017) and Han and Yu (2022) used a logratio transformation for both the response and predictor variables and performed a multivariate linear regression model. Alenazi (2019) transformed the simplicial predictor using the α-transformation (Tsagris et al, 2011) followed by principal component analysis and then employed the Kullback-Leibler divergence regression (or multinomial logit) model (Murteira and Ramalho, 2016).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Constrained least squares simplicial-simplicial regression

Tsagris

2024

Preprint

View full text Add to dashboard Cite

Simplicial-simplicial regression refers to the regression setting where both the responses and predictor variables lie within the simplex space, i.e. they are compositional. For this setting, constrained least squares, where the regression coefficients themselves lie within the simplex, is proposed. The model is transformation-free but the adoption of a power transformation is straightforward, it can treat more than one compositional datasets as predictors and offers the possibility of weights among the simplicial predictors. Among the model’s advantages are its ability to treat zeros in a natural way and a highly computationally efficient algorithm to estimate its coefficients. Resampling based hypothesis testing procedures are employed regarding inference, such as linear independence, and equality of the regression coefficients to some pre-specified values. The strategy behind the formulation new model is implemented to a an existing methodology, that is of the same spirit, showcasing how other similar models can be employed as well. Finally, the performance of the proposed technique and its comparison to the existing methodology takes place using simulation studies and real data examples.

show abstract

Section: Review Of Simplicial-simplicial Regression Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Constrained least squares simplicial-simplicial regression

Tsagris

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In the course of world economic development in the 20-th century, an obvious feature is the rapid rise of the status of the third industry in the whole national economy. Large-scale and efficient logistics, commodity flow and information flow in the third industry link up a large number of production and consumption, so that material production has been greatly developed [31].…”

Section: Introductionmentioning

confidence: 99%

Application of Imputation Method for Compositional Data with Missing Values based on Adaptive LASSO Model: the Composition of Employment Industry in Taiyuan, China

Tian,

Majahar Ali,

Pei Shan

et al. 2023

Mal. J. Fund. Appl. Sci.

View full text Add to dashboard Cite

The tripartite industry classification, which divides all economic activities into three parts, is a classification method to reflect the dynamic process of economic development and the historical trend of the change of resource allocation structure.The fact shows that the proportion of each industry has become an important symbol of the level of national economic development. The proportion of each industry is compositional data,which is a kind of complex multidimensional data used in many fields. All components in the compositional data are non-negative and carry only relative information. In practice, there could be missing values in compositional data. However, general statistical analysis methods cannot be firstly used for compositional data with missing values. The complexity of the missing value of compositional data makes traditional imputation methods no longer suitable. Thus, how to carry out effective statistical inference for compositional data with missing values attracts the attention of many scholars, recently. In this paper, we focus on the imputation problem in compositional data containing missing values, and propose an Adaptive Least Absolute Shrinkage and Selection Operator (ALASSO) imputation method to obtain a complete datasets through variable selection and parameter estimation. Then, the new method is simulated and empirically analyzed, and a comparative study with mean imputation, k-nearest neighbor imputation, and iterative regression imputation is conducted. The results show that the ALASSO imputation method has the highest accuracy for different missing rates, dimensions and correlation coefficients.

show abstract

Partial linear regression of compositional data

Cited by 2 publications

References 27 publications

Constrained least squares simplicial-simplicial regression

Constrained least squares simplicial-simplicial regression

Application of Imputation Method for Compositional Data with Missing Values based on Adaptive LASSO Model: the Composition of Employment Industry in Taiyuan, China

Contact Info

Product

Resources

About