An improved tensor regression model via location smoothing

Stat ◽  
2021 ◽  
Author(s):  
Ya Zhou ◽  
Kejun He
Keyword(s):  
Regression Model ◽  
Tensor Regression

Fast Prediction of Thermal Data Stream for Direct Laser Deposition Processes Using Network-based Tensor Regression

2021 ◽  
pp. 1-39
Author(s):  
Mojtaba Khanzadeh ◽  
Matthew Dantin ◽  
Wenmeng Tian ◽  
Matthew W. Priddy ◽  
Haley Doude ◽  
...  
Keyword(s):  
Regression Model ◽  
Thermal History ◽  
Thermal Image ◽  
Model Parameters ◽  
Tensor Structure ◽  
Direct Laser ◽  
History Of ◽  
Tensor Regression ◽  
Image Streams ◽  
Am Processes

Abstract The objective of this research is to study an effective thermal history prediction method for additive manufacturing (AM) processes using thermal image streams in a layer-wise manner. The need for immaculate integration of in-process sensing and data-driven approaches to monitor process dynamics in AM has been clearly stated in blueprint reports released by various U.S. agencies such as NIST and DoD over the past five years. Reliable physics-based models have been developed to delineate the underlying thermo-mechanical dynamics of AM processes; however, the computational cost is extremely high. We propose a tensor-based surrogate modeling methodology to predict the layer-wise relationship in the thermal history of the AM parts, which is time-efficient compared to available physics-based prediction models. We construct a network-tensor structure for freeform shapes based on thermal image streams obtained in metal-based AM process. Subsequently, we simplify the network-tensor structure by concatenating images to reach layer-wise structure. Succeeding layers are predicted based on antecedent layer using the tensor regression model. Generalized multilinear structure, called the higher-order partial least squares (HOPLS) is used to estimate the tensor regression model parameters. Through proposed method, high-dimensional thermal history of AM components can be predicted accurately in a computationally efficient manner. The proposed thermal history prediction is applied on simulated thermal images from finite element method (FEM) simulations. This shows that the proposed model can be used to enhance their performance alongside simulation-based models.

Multi-view feature selection via sparse tensor regression

2021 ◽  
pp. 2150020
Author(s):  
Haoliang Yuan ◽  
Sio-Long Lo ◽  
Ming Yin ◽  
Yong Liang
Keyword(s):  
Feature Selection ◽  
Computational Complexity ◽  
Regression Model ◽  
Optimization Algorithm ◽  
High Order ◽  
Experimental Results ◽  
Data Sets ◽  
Tensor Structure ◽  
Tensor Regression ◽  
Iterative Optimization Algorithm

In this paper, we propose a sparse tensor regression model for multi-view feature selection. Apart from the most of existing methods, our model adopts a tensor structure to represent multi-view data, which aims to explore their underlying high-order correlations. Based on this tensor structure, our model can effectively select the meaningful feature set for each view. We also develop an iterative optimization algorithm to solve our model, together with analysis about the convergence and computational complexity. Experimental results on several popular multi-view data sets confirm the effectiveness of our model.

Ease of Doing Business and Its Impact on Inward FDI

2018 ◽  
Vol 1 (1) ◽  
pp. 52 ◽  
Author(s):  
Mohamed Tareq Hossain ◽  
Zubair Hassan ◽  
Sumaiya Shafiq ◽  
Abdul Basit
Keyword(s):  
Regression Model ◽  
Causal Relationship ◽  
World Bank ◽  
Least Square ◽  
Doing Business ◽  
Least Square Regression ◽  
Foreign Countries ◽  
Ease Of Doing Business ◽  
The Impact ◽  
Starting A Business

This study investigates the impact of Ease of Doing Business on Inward FDI over the period from 2011 to 2015 across the globe. This study measures ease of doing business using starting a business, getting credit, registering property, paying taxes and enforcing contracts. The research used a sample of 177 countries from 190 countries listed in World Bank. Least square regression model via E-views software used to examine causal relationship. The study found that ease of doing business indicators ‘Enforcing Contracts’ was found to have a positive significant impact on Inward FDI. Nevertheless, ‘Getting Credit’ and ‘Registering Property’ were found to have a negative significant impact on Inward FDI. However, ‘Starting a Business’ and ‘Paying Taxes’ have no significant impact on Inward FDI in the studied timeframe of this research. The findings of the study suggested the ease of doing business enables inward FDI through better contract enforcements, getting credit and registering property. The findings of the research will assist international managers and companies to know the importance of ease of doing business when investing in foreign countries through FDI.

Income Diversification and Bank Efficiency in Vietnam

2017 ◽  
pp. 52-67
Author(s):  
Sang Nguyen Minh
Keyword(s):  
Regression Model ◽  
Commercial Banks ◽  
Banking System ◽  
Operational Efficiency ◽  
Commercial Banking ◽  
Tobit Regression ◽  
Income Diversification ◽  
Positive Effects ◽  
Study Results ◽  
The Impact

This study uses the DEA (Data Envelopment Analysis) method to estimate the technical efficiency index of 34 Vietnamese commercial banks in the period 2007-2015, and then it analyzes the impact of income diversification on the operational efficiency of Vietnamese commercial banks through a censored regression model - the Tobit regression model. Research results indicate that income diversification has positive effects on the operational efficiency of Vietnamese commercial banks in the research period. Based on study results, in this research some recommendations forpolicy are given to enhance the operational efficiency of Vietnam’s commercial banking system.

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