Assessing Robustness of Regularized Regression Models with Applications

2019 ◽  
pp. 401-415 ◽  
Author(s):  
Mayooran Thevaraja ◽  
Azizur Rahman
Keyword(s):  
Regression Models ◽  
Regularized Regression

Unique personality profiles predict when and why sad music is enjoyed

2020 ◽  
pp. 030573562093266
Author(s):  
Matthew E. Sachs ◽  
Antonio Damasio ◽  
Assal Habibi
Keyword(s):  
Regression Models ◽  
Positive Emotions ◽  
Emotional Responses ◽  
Reward System ◽  
Situational Factors ◽  
Regularized Regression ◽  
Personality Profiles ◽  
Sad Music ◽  
The Relationship

The experience of sadness is largely unpleasant, but when expressed through music, it can be pleasurable. Previous research has shown that an attraction to sad music is correlated with personality traits like empathy, Absorption, and rumination. However, the intricacies of the relationship between personality, situational factors, and reasons for engaging with sad music have yet to be fully explored. To address this, participants ( N = 431) reported the situations in which they would listen to sad music and their motivations for doing so. Regularized regression models were employed to assess correlations between personality, situational, and motivational factors. Mediation models were used to determine if emotional responses mediated these associations. People who scored higher on Absorption, the Fantasy component of empathy, and rumination reported enjoying sad music. Absorption and Fantasy were associated with liking sad music because of its ability to regulate/enhance positive emotions. Rumination was associated with liking sad music in tense situations because it both strengthens positive and releases negative emotions. Our results further our understanding of reward responses to negative stimuli by highlighting the role of personality and situational factors. Such findings have implications for the development of interventions for mood disorders, in which music could be used as a tool to regulate emotions and re-engage the reward system.

scholarly journals Statistical Seasonal Prediction Based on Regularized Regression

2017 ◽  
Vol 30 (4) ◽  
pp. 1345-1361 ◽  
Author(s):  
Timothy DelSole ◽  
Arindam Banerjee
Keyword(s):  
Regression Models ◽  
Linear Prediction ◽  
Prediction Models ◽  
Seasonal Prediction ◽  
Statistical Prediction ◽  
Dynamical Model ◽  
Model Output ◽  
Lasso Regression ◽  
Regularized Regression ◽  
Model Data

Abstract This paper proposes a regularized regression procedure for finding a predictive relation between one variable and a field of other variables. The procedure estimates a linear prediction model under the constraint that the regression coefficients have smooth spatial structure. The smoothness constraint is imposed using a novel approach based on the eigenvectors of the Laplace operator over the domain, which results in a constrained optimization problem equivalent to either ridge regression or least absolute shrinkage and selection operator (LASSO) regression, which can be solved by standard numerical software. In addition, this paper explores an unconventional procedure whereby regression models are estimated from dynamical model output and then verified against observations—the reverse of the traditional order. The methodology is illustrated by constructing statistical prediction models of summer Texas-area temperature based on concurrent Pacific sea surface temperature (SST). None of the regularized regression models have statistically significant skill when estimated from observations. In contrast, when estimated from dynamical model output, the regression models have skill with respect to dynamical model data because of the substantially larger sample size available from dynamical model output. In addition, the regression models estimated from dynamical model data can predict observed anomalies with significant skill, even though no observations were used directly to estimate the regression models. The results indicate that dynamical models had no significant skill because they could not accurately predict the SST itself, not because they could not capture realistic SST teleconnections.

scholarly journals Histopathological Landscape of Molecular Genetics and Clinical Determinants in MDS Patients

2020 ◽  
Author(s):  
Oscar Brück ◽  
Susanna Lallukka-Brück ◽  
Helena Hohtari ◽  
Aleksandr Ianevski ◽  
Freja Ebeling ◽  
...  
Keyword(s):  
Regression Models ◽  
Visual Features ◽  
Regularized Regression ◽  
Tissue Composition ◽  
Variant Allele Frequency ◽  
Context Model ◽  
Visual Patterns ◽  
Morphological Findings ◽  
Unsupervised Analysis ◽  
Cytogenetic Aberrations

AbstractIn myelodysplastic syndrome (MDS), bone marrow (BM) histopathology is visually assessed to identify dysplastic cellular morphology, cellularity, and blast excess. Yet, many morphological findings elude the human eye. Here, we extracted visual features of 236 MDS, 87 MDS/MPN, and 10 control BM biopsies with convolutional neural networks. Unsupervised analysis distinguished underlying correlations between tissue composition, leukocyte metrics, and clinical characteristics. We applied morphological features in elastic net-regularized regression models to predict genetic and cytogenetic aberrations, prognosis, and clinical variables. By parallelizing tile, pixel, and leukocyte-level image analysis, we deconvoluted each model to texture and cellular composition to dissect their pathobiological context. Model-based mutation predictions correlated with variant allele frequency and number of affected genes per pathway, demonstrating the models’ ability to identify relevant visual patterns. In summary, this study highlights the potential of deep histopathology in hematology by unveiling the fundamental association of BM morphology with genetic and clinical determinants.

scholarly journals Inverse problem approach to regularized regression models with application to predicting recovery after stroke

2020 ◽  
Vol 62 (8) ◽  
pp. 1926-1938
Author(s):  
Youssef Hbid ◽  
Khaladi Mohamed ◽  
Charles D.A. Wolfe ◽  
Abdel Douiri
Keyword(s):  
Inverse Problem ◽  
Regression Models ◽  
Regularized Regression

scholarly journals Adjusting the Penalized Term for the Regularized Regression Models

2018 ◽  
Vol 13 (2) ◽  
pp. 1609-1630
Author(s):  
Magda Haggag
Keyword(s):  
Regression Models ◽  
Regularized Regression

Regression Models of Foreign Policy Beliefs

1968 ◽  
Author(s):  
David A. Summers ◽  
Thomas R. Stewart
Keyword(s):  
Foreign Policy ◽  
Regression Models ◽  
Policy Beliefs
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