Modeling Hot Deformation of 5005 Aluminum Alloy through Locally Constrained Regression Models with Logarithmic Transformations

This study presents the adoption of locally constrained regression models (LCRMs) with logarithmic transformations in order to model the flow stress behavior of the high-temperature deformation of 5005 aluminum alloy. Hot tensile tests for 5005 aluminum alloy were conducted under the temperatures of 290 °C, 360 °C, 430 °C, and 500 °C, and the strain rates of 0.0003/s, 0.003/s, and 0.03/s. The flow stress behavior was analyzed based on variations in temperature and strain rate. The flow stress during the hot deformation was modeled using the traditional Arrhenius type constitutive equation and the neural network approach. Then, for improved prediction accuracy, the flow stress was modeled using LCRMs. The prediction accuracies of the models were compared by calculating the MAE (Maximum Absolute Error) and RMSE (Root-Mean-Squared Errors) values. The MAE and RMSE of the LCRMs were lower than the errors of the Arrhenius equation and the neural network model. The results show that LCRMs can be useful in modeling the flow stress of 5005 aluminum alloy, and that the developed model can accurately predict the flow stress.

Political and Social Consequences of Qualification Mismatches: A Bounding Approach to Status Inconsistency

Many employees work in jobs that do not match their level of formal education. Status inconsistency theory (SIT) argues that such mismatches result in stress, dissatisfaction, political alienation, and social withdrawal. Status inconsistency may, therefore, pose a threat to social cohesion. However, extant SIT scholarship does not fully appreciate the consequences of an identification problem due to the perfect collinearity among the effects of occupation, education, and their mismatch. I review the literature and show that prior findings depend on implicit theoretical assumptions that are often implausible once spelled out. To overcome this problem, I propose a new approach to the study of mismatches that builds on recent advances in the modeling of age, period, and cohort effects. I demonstrate how a set of relatively weak assumptions that are transparently grounded in sociological theory allows for (partial) identification of mismatch effects. The empirical analysis draws on comparable large-scale survey data from the United Kingdom (UKLHS) and Germany (GSOEP), two countries with a very different institutional organization of education to job matching. Compared with previous research, I use theoretically justified identifying assumptions and provide more rigorous evidence by addressing non-random selection into mismatch. Constrained regression models show mismatch effects on work-related identities, satisfaction, and organizational integration. Contra SIT, my results suggest that the effects of mismatches do not arise from cognitive dissonance but from an expectation formation mechanism. I find only weak evidence that mismatch effects spill over into the political domain. Despite large institutional differences, the results are similar across countries.

Use of Shape Restricted Regression Methods for Fitting Model of Per Capita GDP: A Global Economic Scenario of 2018

The aim of the study is to analyze the pattern of Gross domestic product (GDP) according to Human development index (HDI) for 184 countries of the world. GDP per capita indicates only economic prosperity but not the overall development of the citizens of a country. This research tries to find out the beneath relationship of the financial state and human development of countries using the data of 2018. For demonstrating this analysis several parametric and non-parametric regression methods subject to shape restriction have been used. The study targets to shed light on comparative performance of shape constrained regression with cone projection, polynomial regression, LOESS, Istonic regression with pooled adjacent violators algorithm, Kernel regression, smoothing spline and generalized additive model in convex situation.

Incorporating self-reported health measures in risk equalization through constrained regression

Most health insurance markets with premium-rate restrictions include a risk equalization system to compensate insurers for predictable variation in spending. Recent research has shown, however, that even the most sophisticated risk equalization systems tend to undercompensate (overcompensate) groups of people with poor (good) self-reported health, confronting insurers with incentives for risk selection. Self-reported health measures are generally considered infeasible for use as an explicit ‘risk adjuster’ in risk equalization models. This study examines an alternative way to exploit this information, namely through ‘constrained regression’ (CR). To do so, we use administrative data (N = 17 m) and health survey information (N = 380 k) from the Netherlands. We estimate five CR models and compare these models with the actual Dutch risk equalization model of 2016 which was estimated by ordinary least squares (OLS). In the CR models, the estimated coefficients are restricted, such that the under-/overcompensation for groups based on self-reported general health is reduced by 20, 40, 60, 80, or 100%. Our results show that CR can improve outcomes for groups that are not explicitly flagged by risk adjuster variables, but worsens outcomes for groups that are explicitly flagged by risk adjuster variables. Using a new standardized metric that summarizes under-/overcompensation for both types of groups, we find that the lighter constraints can lead to better outcomes than OLS.