The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform

Thijs J. H. Vlugt - One of the best experts on this subject based on the ideXlab platform.

  • Multiple Linear Regression and Thermodynamic Fluctuations are Equivalent for Computing Thermodynamic Derivatives from Molecular Simulation
    Fluid Phase Equilibria, 2020
    Co-Authors: Ahmadreza Rahbari, Tyler R. Josephson, Yangzesheng Sun, Othonas A. Moultos, David Dubbeldam, J. Ilja Siepmann, Thijs J. H. Vlugt
    Abstract:

    Abstract Partial molar properties are of fundamental importance for understanding properties of non-ideal mixtures. Josephson and co-workers (Mol. Phys. 2019, 117, 3589–3602) used least squares Multiple Linear Regression to obtain partial molar properties in open constant-pressure ensembles. Assuming composition-independent partial molar properties for the narrow composition range encountered throughout simulation trajectories, we rigorously prove the equivalence of two approaches for computing thermodynamic derivatives in open ensembles of an n-component system: (1) Multiple Linear Regression, and (2) thermodynamic fluctuations. Multiple Linear Regression provides a conceptually simple and computationally efficient way of computing thermodynamic derivatives for multicomponent systems. We show that in the reaction ensemble, the reaction enthalpy can be computed directly by simple Multiple Linear Regression of the enthalpy as a function of the number of reactant molecules. Non-Linear Regression and a Gaussian process model taking into account the compositional dependence of partial molar properties further support that Multiple Linear Regression captures the correct physics.

Sajjad Haider Bhatti - One of the best experts on this subject based on the ideXlab platform.

  • Reservoir Inflow Prediction by Ensembling Wavelet and Bootstrap Techniques to Multiple Linear Regression Model
    Water Resources Management, 2019
    Co-Authors: Adnan Bashir, Muhammad Ahmed Shehzad, Muhammad Ishaq Asif Rehmani, Ijaz Hussain, Sajjad Haider Bhatti
    Abstract:

    In this study, a new hybrid model, bootstrap Multiple Linear Regression (BMLR) is suggested to investigate the potential of bootstrap resampling technique for daily reservoir inflow prediction. The proposed model compares with three other models: Multiple Linear Regression (MLR), wavelet Multiple Linear Regression (WMLR) and wavelet bootstrap Multiple Linear Regression (WBMLR). River stage data of monsoon season (1st July 2010 to 30 September 2010) from three gauging stations of Chenab river basin are used. In wavelet transformation, input vectors are decomposed using discrete wavelet transformation (DWT) into discrete wavelet components (DWCs). Then suitable DWCs are used to provide input to MLR model to develop WMLR model. Bootstrap technique coupled with MLR model to build up BMLR model. While WBMLR model is the conjunction of suitable DWCs and bootstrap technique to MLR model. Performance indices namely root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe coefficient of efficiency (NSC), and persistence index (CP) are used in study to evaluate the performance of model. Results showed that hybrid model BMLR produce significantly better results on performance indices than other models MLR, WMLR and WBMLR.

Edward J. Wegman - One of the best experts on this subject based on the ideXlab platform.

  • Parallelizing Multiple Linear Regression for speed and redundancy: an empirical study ∗
    Journal of Statistical Computation and Simulation, 1991
    Co-Authors: Xu Mingxian, John J. H. Miller, Edward J. Wegman
    Abstract:

    The purpose of this paper is to present a parallel implementation of Multiple Linear Regression. We discuss the Multiple Linear Regression model. Traditionally parallelism has been used for either speed up or redundancy (hence reliability). With stochastic data, by clever parsing and algorithm development, it is possible to achieve both speed and reliability enhancement. We demonstrate this with Multiple Linear Regression.

  • Parallelizing Multiple Linear Regression for Speed and Redundancy: An Empirical Study
    Proceedings of the Fifth Distributed Memory Computing Conference 1990., 1
    Co-Authors: John J. H. Miller, Edward J. Wegman
    Abstract:

    The purpose of this paper is to present a parallel implementation of Multiple Linear Regression. We discuss the Multiple Linear Regression model. Traditionally parallelism has been used for either speed-up or redundancy (hence reliability). With stochastic data, by clever parsing and algorithm development, it is possible to achieve both speed and reliability enhancement. We demonstrate this with Multiple Linear Regression. Other examples include kernel estimation and bootstrapping.

Liuwei - One of the best experts on this subject based on the ideXlab platform.

Amirhossein Amiri - One of the best experts on this subject based on the ideXlab platform.

  • Diagnosis Aids in Multivariate Multiple Linear Regression Profiles Monitoring
    Communications in Statistics - Theory and Methods, 2014
    Co-Authors: Amirhossein Amiri, Abbas Saghaei, Mohammad Reza Mohseni, Yaser Zerehsaz
    Abstract:

    Diagnosis aids in addition to detecting the out-of-control state is an important issue in multivariate Multiple Linear Regression profiles monitoring; because a large number of parameters and profiles in this structure are involved. In this paper, we specifically concentrate on identification of profile(s) and parameter(s) which have changed during the process in multivariate Multiple Linear Regression profiles structure in Phase II. We demonstrate the effectiveness of our proposed approaches through Monte Carlo simulations and a real case study in terms of accuracy percent.

  • A parameters reduction method for monitoring Multiple Linear Regression profiles
    The International Journal of Advanced Manufacturing Technology, 2011
    Co-Authors: Amirhossein Amiri, M. Eyvazian, Changliang Zou, Rassoul Noorossana
    Abstract:

    In certain applications of statistical process control, it is possible to model quality of a product or process using a Multiple Linear Regression profile. Some methods exist in the literature which could be used for monitoring Multiple Linear Regression profiles. However, the performance of most of these methods deteriorates as the number of Regression parameters increases. In this paper, we specifically concentrate on phase II monitoring of Multiple Linear Regression profiles and propose a new dimension reduction method to overcome the dimensionality problem of some of the existing methods. The robustness, effectiveness, and limitations of the proposed method are also discussed. Simulation results show that in term of average run length criterion, the proposed method outperforms the traditional methods and has comparable performance with another dimension reduction method in the literature.

  • phase ii monitoring of multivariate Multiple Linear Regression profiles
    Quality and Reliability Engineering International, 2011
    Co-Authors: M. Eyvazian, Abbas Saghaei, Rassoul Noorossana, Amirhossein Amiri
    Abstract:

    In certain cases, the quality of a process or a product can be effectively characterized by two or more Multiple Linear Regression profiles in which response variables are correlated. This structure can be modeled as multivariate Multiple Linear Regression profiles. When Linear profiles are monitored separately, then correlation between response variables is ignored and misleading results could be expected. To overcome this problem, the use of methods that consider the multivariate structure between response variables is inevitable. In this paper, we propose four methods to monitor this structure in Phase II. The performance of the methods is compared through simulation studies in terms of the average run length criterion. Furthermore, a method based on likelihood ratio approach is developed to determine the location of shifts and a numerical simulation is used to evaluate the performance of the proposed method. Finally, the use of the methods is illustrated by a numerical example. Copyright © 2010 John Wiley & Sons, Ltd.