Predictive Regression with p-Lags and Order-q Autoregressive Predictors

Abstract The purpose of the investigation was to obtain the predictive regression models that help correct the calculation of the mechanical properties of single crystal nickel-based superalloys without conducting prior experiments. The paper considers the influence of alloying elements on their tendency to form phases in foundry nickel-based superalloys. Using the elements influence on the phase formation, the coefficient Kc’ of the ratio of alloying elements for this class of alloys was set for the first time. We have revealed the short correlation of the ratio Kc’ with the dimensional misfit of γ and γ’ crystal lattices. Also, a high probability to predict the misfit for multicomponent nickel systems is shown, which significantly affected the strength properties. The regression models of correlation dependencies on the dimensional γ/γ’- misfit were offered to predict the short-term and long-term limits of the strength of alloys. We determined the operating temperature at which the misfit value should decrease to zero. The structure stability should increase because of the structural stresses minimizing. This has a positive effect on strength and plastic properties.

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Development of Tooling Cost Model for High Speed Hard Turning

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.418-420.1482 ◽

2011 ◽

Vol 418-420 ◽

pp. 1482-1485 ◽

Cited By ~ 3

Author(s):

Erry Yulian Triblas Adesta ◽

Muataz Al Hazza ◽

Delvis Agusman ◽

Agus Geter Edy Sutjipto

Keyword(s):

Feed Rate ◽

High Speed ◽

Hard Turning ◽

Cost Model ◽

Cutting Tools ◽

Cutting Speed ◽

Rake Angle ◽

Depth Of Cut ◽

Predictive Regression ◽

Aisi 4340

The current work presents the development of cost model for tooling during high speed hard turning of AISI 4340 hardened steel using regression analysis. A set of experimental data using ceramic cutting tools, composed approximately of Al2O3 (70%) and TiC (30%) on AISI 4340 heat treated to a hardness of 60 HRC was obtained in the following design boundary: cutting speeds (175-325 m/min), feed rate (0.075-0.125 m/rev), negative rake angle (0 to -12) and depth of cut of (0.1-0.15) mm. The output data is used to develop a new model in predicting the tooling cost using in terms of cutting speed, feed rate, depth of cut and rake angle. Box Behnken Design was used in developing the model. Predictive regression model was found to be capable of good predictions the tooling cost within the boundary design.

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Predictive Regression Equations of Flowmetric and Spirometric Peak Expiratory Flow in Healthy Moroccan Adults

International Scholarly Research Notices ◽

10.1155/2017/8985067 ◽

2017 ◽

Vol 2017 ◽

pp. 1-7

Author(s):

Khalid Bouti ◽

Iliass Maouni ◽

Jouda Benamor ◽

Jamal Eddine Bourkadi

Keyword(s):

Peak Flow ◽

Adult Population ◽

Cross Sectional Study ◽

Regression Equations ◽

Anthropometric Parameters ◽

Cross Sectional ◽

Flow Measurements ◽

Predictive Regression ◽

Final Sample ◽

Main Determinants

Introduction. PEF has never been characterized among healthy Moroccan adults. The objective of this study is to describe the values of PEF among healthy Moroccan adults, to study its relationship with anthropometric parameters (gender, age, height, and weight), to compare spirometric and flowmetric PEF, to establish the prediction equations for PEF, and to study the correlation between PEF and FEV1. Methods. Cross-sectional study conducted between May and June 2016. It involved healthy nonsmoking volunteers living in Tetouan, Morocco, gathered through a mobile stand realization of spirometry and peak flow measurements. Results. Our final sample concerned 313 adults (143 men and 170 women). For both men and women, age and height were the main determinants of PEF, and a positive correlation was found between PEF and FEV1. Conclusion. Our study has established the PEF predictive equations in the Moroccan adult population. Our results allow us to conclude that the PEF can be a reliable alternative of FEV1 in centers not equipped with spirometry.

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Properties of nickel-based superalloys of equiaxial crystallization

Innovative Materials and Technologies in Metallurgy and Mechanical Engineering ◽

10.15588/1607-6885-2021-2-3 ◽

2021 ◽

pp. 19-23

Author(s):

O. Glotka ◽

V. Olshanetskii

Keyword(s):

Mechanical Properties ◽

Chemical Composition ◽

Regression Models ◽

Computational Prediction ◽

Integrated Approach ◽

Experimental Data Processing ◽

Predictive Regression ◽

Heat Resistant ◽

First Time ◽

Thermodynamic Processes

Purpose. The aim of the work is to obtain predictive regression models, with the help of which, it is possible to adequately calculate the mechanical properties of nickel-based superalloys of equiaxial crystallization, without carrying out preliminary experiments. Research methods. To find regularities and calculate the latest CALPHAD method was chosen, and modeling of thermodynamic processes of phase crystallization was performed. Results. As a result of experimental data processing, the ratio of alloying elements Kg¢ was proposed for the first time, which can be used to assess the mechanical properties, taking into account the complex effect of the main alloy components. The regularities of the influence of the composition on the properties of heat-resistant nickel alloys of equiaxial crystallization are established. The analysis of the received dependences in comparison with practical results is carried out. The relations well correlated with heat resistance, mismatch and strength of alloys are obtained. Scientific novelty. It is shown that for multicomponent nickel systems it is possible with a high probability to predict a mismatch, which significantly affects the strength characteristics of alloys of this class. The regularities of the influence of the chemical composition on the structure and properties of alloys are established. A promising and effective direction in solving the problem of predicting the main characteristics of heat-resistant materials based on nickel is shown Practical value. On the basis of an integrated approach for multicomponent heat-resistant nickel-based alloys, new regression models have been obtained that make it possible to adequately predict the properties of the chemical composition of the alloy, which made it possible to solve the problem of computational prediction of properties from the chemical composition of the alloy. This allows not only to design new nickel-based alloys, but also to optimize the composition of existing brands.

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Secure Cloud Auditability for Virtual Machines by Adaptive Characterization Using Machine Learning Methods

Operational Research in Engineering Sciences Theory and Applications ◽

10.31181/oresta20402059t ◽

2021 ◽

Vol 4 (3) ◽

pp. 59-75

Author(s):

Shesagiri Taminana ◽

◽

Lalitha Bhaskari ◽

Arwa Mashat ◽

Dragan Pamučar ◽

...

Keyword(s):

Time Complexity ◽

Virtual Machines ◽

Application Development ◽

Predictive Regression ◽

Data Centre ◽

Machine Learning Methods ◽

Highly Sensitive ◽

Increasing Demand ◽

Application Developers ◽

Data Centres

With the Present days increasing demand for the higher performance with the application developers have started considering cloud computing and cloud-based data centres as one of the prime options for hosting the application. Number of parallel research outcomes have for making a data centre secure, the data centre infrastructure must go through the auditing process. During the auditing process, auditors can access VMs, applications and data deployed on the virtual machines. The downside of the data in the VMs can be highly sensitive and during the process of audits, it is highly complex to permits based on the requests and can increase the total time taken to complete the tasks. Henceforth, the demand for the selective and adaptive auditing is the need of the current research. However, these outcomes are criticised for higher time complexity and less accuracy. Thus, this work proposes a predictive method for analysing the characteristics of the VM applications and the characteristics from the auditors and finally granting the access to the virtual machine by building a predictive regression model. The proposed algorithm demonstrates 50% of less time complexity to the other parallel research for making the cloud-based application development industry a safer and faster place.

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Testing for Multiple-Horizon Predictability: Direct Regression Based versus Implication Based

Review of Financial Studies ◽

10.1093/rfs/hhz135 ◽

2019 ◽

Vol 33 (9) ◽

pp. 4403-4443 ◽

Cited By ~ 2

Author(s):

Ke-Li Xu

Keyword(s):

Equity Premium ◽

Predictive Ability ◽

Finite Sample ◽

Simulation Experiments ◽

Asset Return ◽

Predictive Regression ◽

Oxford University ◽

Short Run ◽

Direct Regression ◽

Published Paper

Abstract Research in finance and macroeconomics has routinely employed multiple horizons to test asset return predictability. In a simple predictive regression model, we find the popular scaled test can have zero power when the predictor is not sufficiently persistent. A new test based on implication of the short-run model is suggested and is shown to be uniformly more powerful than the scaled test. The new test can accommodate multiple predictors. Compared with various other widely used tests, simulation experiments demonstrate remarkable finite-sample performance. We reexamine the predictive ability of various popular predictors for aggregate equity premium. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

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A divide-and-conquer method for sparse risk prediction and evaluation

Biostatistics ◽

10.1093/biostatistics/kxaa031 ◽

2020 ◽

Author(s):

Chuan Hong ◽

Yan Wang ◽

Tianxi Cai

Keyword(s):

Regression Model ◽

Risk Prediction ◽

Prediction Models ◽

Large Data ◽

Disease Diagnosis ◽

Divide And Conquer ◽

Classification Model ◽

Statistical Efficiency ◽

Inference Procedure ◽

Predictive Regression

Summary Divide-and-conquer (DAC) is a commonly used strategy to overcome the challenges of extraordinarily large data, by first breaking the dataset into series of data blocks, then combining results from individual data blocks to obtain a final estimation. Various DAC algorithms have been proposed to fit a sparse predictive regression model in the $L_1$ regularization setting. However, many existing DAC algorithms remain computationally intensive when sample size and number of candidate predictors are both large. In addition, no existing DAC procedures provide inference for quantifying the accuracy of risk prediction models. In this article, we propose a screening and one-step linearization infused DAC (SOLID) algorithm to fit sparse logistic regression to massive datasets, by integrating the DAC strategy with a screening step and sequences of linearization. This enables us to maximize the likelihood with only selected covariates and perform penalized estimation via a fast approximation to the likelihood. To assess the accuracy of a predictive regression model, we develop a modified cross-validation (MCV) that utilizes the side products of the SOLID, substantially reducing the computational burden. Compared with existing DAC methods, the MCV procedure is the first to make inference on accuracy. Extensive simulation studies suggest that the proposed SOLID and MCV procedures substantially outperform the existing methods with respect to computational speed and achieve similar statistical efficiency as the full sample-based estimator. We also demonstrate that the proposed inference procedure provides valid interval estimators. We apply the proposed SOLID procedure to develop and validate a classification model for disease diagnosis using narrative clinical notes based on electronic medical record data from Partners HealthCare.

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