scholarly journals Adversarial orthogonal regression: Two non-linear regressions for causal inference

2021 ◽  
Author(s):  
M. Reza Heydari ◽  
Saber Salehkaleybar ◽  
Kun Zhang
Keyword(s):  
Causal Inference ◽  
Orthogonal Regression ◽  
Non Linear ◽  
Linear Regressions

scholarly journals Prospective study of dynamic whole-body 68Ga-DOTATOC-PET/CT acquisition in patients with well-differentiated neuroendocrine tumors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Philippe Thuillier ◽  
David Bourhis ◽  
Jean Philippe Metges ◽  
Romain Le Pennec ◽  
Karim Amrane ◽  
...  
Keyword(s):  
Neuroendocrine Tumors ◽  
Linear Relation ◽  
Whole Body ◽  
Pet Ct ◽  
Non Linear ◽  
Well Differentiated ◽  
Analog Systems ◽  
Pet System ◽  
Dynamic Acquisition ◽  
Linear Regressions

AbstractTo present the feasibility of a dynamic whole-body (DWB) 68Ga-DOTATOC-PET/CT acquisition in patients with well-differentiated neuroendocrine tumors (WD-NETs). Sixty-one patients who underwent a DWB 68Ga-DOTATOC-PET/CT for a histologically proven/highly suspected WD-NET were prospectively included. The acquisition consisted in single-bed dynamic acquisition centered on the heart, followed by the DWB and static acquisitions. For liver, spleen and tumor (1–5/patient), Ki values (in ml/min/100 ml) were calculated according to Patlak's analysis and tumor-to-liver (TLR-Ki) and tumor-to-spleen ratios (TSR-Ki) were recorded. Ki-based parameters were compared to static parameters (SUVmax/SUVmean, TLR/TSRmean, according to liver/spleen SUVmean), in the whole-cohort and according to the PET system (analog/digital). A correlation analysis between SUVmean/Ki was performed using linear and non-linear regressions. Ki-liver was not influenced by the PET system used, unlike SUVmax/SUVmean. The regression analysis showed a non-linear relation between Ki/SUVmean (R2 = 0.55,0.68 and 0.71 for liver, spleen and tumor uptake, respectively) and a linear relation between TLRmean/TLR-Ki (R2 = 0.75). These results were not affected by the PET system, on the contrary of the relation between TSRmean/TSR-Ki (R2 = 0.94 and 0.73 using linear and non-linear regressions in digital and analog systems, respectively). Our study is the first showing the feasibility of a DWB 68Ga-DOTATOC-PET/CT acquisition in WD-NETs.

scholarly journals Non-linear Causal Inference Using Gaussianity Measures

2019 ◽  
pp. 257-299
Author(s):  
Daniel Hernández-Lobato ◽  
Pablo Morales-Mombiela ◽  
David Lopez-Paz ◽  
Alberto Suárez
Keyword(s):  
Causal Inference ◽  
Non Linear

An Empirical Approach for Prediction of Natural Fiber Reinforced Polypropylene Composite Properties

2014 ◽  
Vol 534 ◽  
pp. 69-73
Author(s):  
Ritu Gupta ◽  
Norrozila Sulaiman ◽  
Mohammed Dalour Hossain Beg ◽  
Arun Gupta
Keyword(s):  
Coupling Agent ◽  
Natural Fiber ◽  
Linear Regression Analysis ◽  
Fiber Reinforced ◽  
Scientific Software ◽  
Fiber Reinforced Plastic ◽  
Reinforced Plastic ◽  
Non Linear ◽  
Linear Regressions ◽  
Composite Properties

In this paper, empirical models are proposed using multiple non linear regressions technique to predict the influence on the Youngs modulus and the tensile strength of the natural fiber reinforced plastic composites (NFRPC). Maleic Anhydride grafted polypropylene (MAPP) has been a proven coupling agent (CA) used to improve the interfacial bonding between the fibers and the plastics material. It is important to include the factor of coupling agent, when making predictions the properties of the composites through the models. For the development of the model, data was collected from various research journals presented in literature. Non linear regression analysis was performed to obtain the empirical model using polymath scientific software. The results were found to be within the acceptable range.

scholarly journals Biomass allocation in natural regeneration of Fagus sylvatica and Picea abies trees in Italian Alps

2014 ◽  
Vol 61 (1) ◽  
pp. 35-46 ◽  
Author(s):  
Fabio Pastorella ◽  
Alessandro Paletto
Keyword(s):  
Leaf Area ◽  
Norway Spruce ◽  
Biomass Allocation ◽  
Growth Stages ◽  
Area Index ◽  
Ground Biomass ◽  
Non Linear ◽  
Below Ground ◽  
The Relationship ◽  
Linear Regressions

Abstract Biomass allocation in seedlings and saplings at different stages of growth is important information for studying the response of species to site conditions. The objectives of the paper are: (a) to analyse the relationship between height and biomass in young Norway spruce and European beech trees, (b) to study the influence of the leaf area on ontogenetic growth stages and biomass sequestration capacity on the regeneration of these two species. 96 seedlings (H < 30 cm) and saplings (31 < H < 130 cm) were collected in different light conditions in a case study in the Alps (Trentino province, Italy). Leaf Area Index and shoot/root ratio were used as indicators of the ecological conditions (e.g. light, soil moisture, nutrient status) able to influence the seedlings and saplings growth. Two non-linear regressions were fitted to analyse the relationship between height and biomass and to develop the aboveground and below-ground allometric equations. Non-linear regressions show that sapling or seedling height is a good predictor of above-ground and below-ground biomass with a R2aj above 0.8 for all equations and a R2aj above 0.9 for above-ground biomass of Norway spruce. The results show that silvicultural practices may influence the regeneration patterns and increase the biomass allocation rate influencing stand density and canopy cover.

scholarly journals Estimating Stochastic Linear Combination of Non-Linear Regressions

2020 ◽  
Vol 34 (04) ◽  
pp. 6137-6144
Author(s):  
Di Wang ◽  
Xiangyu Guo ◽  
Chaowen Guan ◽  
Shi Li ◽  
Jinhui Xu
Keyword(s):  
Machine Learning ◽  
Linear Combination ◽  
Statistical Models ◽  
High Probability ◽  
Estimation Errors ◽  
Zero Bias ◽  
Multi Index ◽  
Non Linear ◽  
Linear Regressions ◽  
Theoretical Results

In this paper we study the problem of estimating stochastic linear combination of non-linear regressions, which has a close connection with many machine learning and statistical models such as non-linear regressions, the Single Index, Multi-index, Varying Coefficient Index Models and Two-layer Neural Networks. Specifically, we first show that with some mild assumptions, if the variate vector x is multivariate Gaussian, then there is an algorithm whose output vectors have ℓ2-norm estimation errors of O(√p/n) with high probability, where p is the dimension of x and n is the number of samples. Then we extend our result to the case where x is sub-Gaussian using the zero-bias transformation, which could be seen as a generalization of the classic Stein's lemma. We also show that with some additional assumptions there is an algorithm whose output vectors have ℓ∞-norm estimation errors of O(1/√p + √p/n) with high probability. Finally, for both Gaussian and sub-Gaussian cases we propose a faster sub-sampling based algorithm and show that when the sub-sample sizes are large enough then the estimation errors will not be sacrificed by too much. Experiments for both cases support our theoretical results. To the best of our knowledge, this is the first work that studies and provides theoretical guarantees for the stochastic linear combination of non-linear regressions model.

scholarly journals ON USING LINEAR QUANTILE REGRESSIONS FOR CAUSAL INFERENCE

2016 ◽  
Vol 33 (3) ◽  
pp. 664-690 ◽  
Author(s):  
Ryutah Kato ◽  
Yuya Sasaki
Keyword(s):  
Causal Inference ◽  
Unobserved Heterogeneity ◽  
Weighted Average ◽  
Quantile Function ◽  
Structural Function ◽  
Slope Parameter ◽  
Quantile Regressions ◽  
Conditional Quantile ◽  
Conditional Quantile Function ◽  
Linear Regressions

We show that the slope parameter of the linear quantile regression measures a weighted average of the local slopes of the conditional quantile function. Extending this result, we also show that the slope parameter measures a weighted average of the partial effects for a general structural function. Our results support the use of linear quantile regressions for causal inference in the presence of nonlinearity and multivariate unobserved heterogeneity. The same conclusion applies to linear regressions.

Sign in / Sign up

Export Citation Format

Share Document