**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**8471

# Search results for: generalized linear model

##### 8471 The New Relative Efficiency Based on the Least Eigenvalue in Generalized Linear Model

**Authors:**
Chao Yuan,
Bao Guang Tian

**Abstract:**

**Keywords:**
Generalized linear model,
generalized relative coefficient,
least eigenvalue,
relative efficiency.

##### 8470 A Comparison of the Sum of Squares in Linear and Partial Linear Regression Models

**Authors:**
Dursun Aydın

**Abstract:**

**Keywords:**
Partial Linear Regression Model,
Linear RegressionModel,
Residuals,
Deviance,
Smoothing Spline.

##### 8469 Convergence Analysis of the Generalized Alternating Two-Stage Method

**Authors:**
Guangbin Wang,
Liangliang Li,
Fuping Tan

**Abstract:**

In this paper, we give the generalized alternating twostage method in which the inner iterations are accomplished by a generalized alternating method. And we present convergence results of the method for solving nonsingular linear systems when the coefficient matrix of the linear system is a monotone matrix or an H-matrix.

**Keywords:**
Generalized alternating two-stage method,
linear system,
convergence.

##### 8468 2D Numerical Analysis of Sao Paulo Tunnel

**Authors:**
A.H. Akhaveissy

**Abstract:**

**Keywords:**
Non-associated flow rule,
Generalized plasticity,
tunnel excavation,
Excavation method.

##### 8467 Application of a Generalized Additive Model to Reveal the Relations between the Density of Zooplankton with Other Variables in the West Daya Bay, China

**Authors:**
Weiwen Li,
Hao Huang,
Chengmao You,
Jianji Liao,
Lei Wang,
Lina An

**Abstract:**

Zooplankton are a central issue in the ecology which makes a great contribution to maintaining the balance of an ecosystem. It is critical in promoting the material cycle and energy flow within the ecosystems. A generalized additive model (GAM) was applied to analyze the relationships between the density (individuals per m³) of zooplankton and other variables in West Daya Bay. All data used in this analysis (the survey month, survey station (longitude and latitude), the depth of the water column, the superficial concentration of chlorophyll a, the benthonic concentration of chlorophyll a, the number of zooplankton species and the number of zooplankton species) were collected through monthly scientific surveys during January to December 2016. GLM model (generalized linear model) was used to choose the significant variables’ impact on the density of zooplankton, and the GAM was employed to analyze the relationship between the density of zooplankton and the significant variables. The results showed that the density of zooplankton increased with an increase of the benthonic concentration of chlorophyll a, but decreased with a decrease in the depth of the water column. Both high numbers of zooplankton species and the overall total number of zooplankton individuals led to a higher density of zooplankton.

**Keywords:**
Density,
generalized linear model,
generalized additive model,
the West Daya Bay,
zooplankton.

##### 8466 Solving Linear Matrix Equations by Matrix Decompositions

**Authors:**
Yongxin Yuan,
Kezheng Zuo

**Abstract:**

In this paper, a system of linear matrix equations is considered. A new necessary and sufficient condition for the consistency of the equations is derived by means of the generalized singular-value decomposition, and the explicit representation of the general solution is provided.

**Keywords:**
Matrix equation,
Generalized inverse,
Generalized
singular-value decomposition.

##### 8465 Risk Factors for Defective Autoparts Products Using Bayesian Method in Poisson Generalized Linear Mixed Model

**Authors:**
Pitsanu Tongkhow,
Pichet Jiraprasertwong

**Abstract:**

This research investigates risk factors for defective products in autoparts factories. Under a Bayesian framework, a generalized linear mixed model (GLMM) in which the dependent variable, the number of defective products, has a Poisson distribution is adopted. Its performance is compared with the Poisson GLM under a Bayesian framework. The factors considered are production process, machines, and workers. The products coded RT50 are observed. The study found that the Poisson GLMM is more appropriate than the Poisson GLM. For the production Process factor, the highest risk of producing defective products is Process 1, for the Machine factor, the highest risk is Machine 5, and for the Worker factor, the highest risk is Worker 6.

**Keywords:**
Defective autoparts products,
Bayesian framework,
Generalized linear mixed model (GLMM),
Risk factors.

##### 8464 A Note on the Convergence of the Generalized AOR Iterative Method for Linear Systems

**Authors:**
Zhong-xi Gao,
Hou-biao Li

**Abstract:**

Recently, some convergent results of the generalized AOR iterative (GAOR) method for solving linear systems with strictly diagonally dominant matrices are presented in [Darvishi, M.T., Hessari, P.: On convergence of the generalized AOR method for linear systems with diagonally dominant cofficient matrices. Appl. Math. Comput. 176, 128-133 (2006)] and [Tian, G.X., Huang, T.Z., Cui, S.Y.: Convergence of generalized AOR iterative method for linear systems with strictly diagonally dominant cofficient matrices. J. Comp. Appl. Math. 213, 240-247 (2008)]. In this paper, we give the convergence of the GAOR method for linear systems with strictly doubly diagonally dominant matrix, which improves these corresponding results.

**Keywords:**
Diagonally dominant matrix,
GAOR method,
Linear
system,
Convergence

##### 8463 Relationship between Sums of Squares in Linear Regression and Semi-parametric Regression

**Authors:**
Dursun Aydın,
Bilgin Senel

**Abstract:**

**Keywords:**
Semi-parametric regression,
Penalized LeastSquares,
Residuals,
Deviance,
Smoothing Spline.

##### 8462 Density Estimation using Generalized Linear Model and a Linear Combination of Gaussians

**Authors:**
Aly Farag,
Ayman El-Baz,
Refaat Mohamed

**Abstract:**

In this paper we present a novel approach for density estimation. The proposed approach is based on using the logistic regression model to get initial density estimation for the given empirical density. The empirical data does not exactly follow the logistic regression model, so, there will be a deviation between the empirical density and the density estimated using logistic regression model. This deviation may be positive and/or negative. In this paper we use a linear combination of Gaussian (LCG) with positive and negative components as a model for this deviation. Also, we will use the expectation maximization (EM) algorithm to estimate the parameters of LCG. Experiments on real images demonstrate the accuracy of our approach.

**Keywords:**
Logistic regression model,
Expectationmaximization,
Segmentation.

##### 8461 Development of Admire Longitudinal Quasi-Linear Model by using State Transformation Approach

**Authors:**
Jianqiao. Yu,
Jianbo. Wang,
Xinzhen. He

**Abstract:**

This paper presents a longitudinal quasi-linear model for the ADMIRE model. The ADMIRE model is a nonlinear model of aircraft flying in the condition of high angle of attack. So it can-t be considered to be a linear system approximately. In this paper, for getting the longitudinal quasi-linear model of the ADMIRE, a state transformation based on differentiable functions of the nonscheduling states and control inputs is performed, with the goal of removing any nonlinear terms not dependent on the scheduling parameter. Since it needn-t linear approximation and can obtain the exact transformations of the nonlinear states, the above-mentioned approach is thought to be appropriate to establish the mathematical model of ADMIRE. To verify this conclusion, simulation experiments are done. And the result shows that this quasi-linear model is accurate enough.

**Keywords:**
quasi-linear model,
simulation,
state transformation approach,
the ADMIRE model.

##### 8460 Exponential Stability of Uncertain Takagi-Sugeno Fuzzy Hopfield Neural Networks with Time Delays

**Abstract:**

In this paper, based on linear matrix inequality (LMI), by using Lyapunov functional theory, the exponential stability criterion is obtained for a class of uncertain Takagi-Sugeno fuzzy Hopfield neural networks (TSFHNNs) with time delays. Here we choose a generalized Lyapunov functional and introduce a parameterized model transformation with free weighting matrices to it, these techniques lead to generalized and less conservative stability condition that guarantee the wide stability region. Finally, an example is given to illustrate our results by using MATLAB LMI toolbox.

**Keywords:**
Hopfield neural network,
linear matrix inequality,
exponential stability,
time delay,
T-S fuzzy model.

##### 8459 Estimating Regression Effects in Com Poisson Generalized Linear Model

**Authors:**
Vandna Jowaheer,
Naushad A. Mamode Khan

**Abstract:**

Com Poisson distribution is capable of modeling the count responses irrespective of their mean variance relation and the parameters of this distribution when fitted to a simple cross sectional data can be efficiently estimated using maximum likelihood (ML) method. In the regression setup, however, ML estimation of the parameters of the Com Poisson based generalized linear model is computationally intensive. In this paper, we propose to use quasilikelihood (QL) approach to estimate the effect of the covariates on the Com Poisson counts and investigate the performance of this method with respect to the ML method. QL estimates are consistent and almost as efficient as ML estimates. The simulation studies show that the efficiency loss in the estimation of all the parameters using QL approach as compared to ML approach is quite negligible, whereas QL approach is lesser involving than ML approach.

**Keywords:**
Com Poisson,
Cross-sectional,
Maximum
Likelihood,
Quasi likelihood

##### 8458 Modelling Extreme Temperature in Malaysia Using Generalized Extreme Value Distribution

**Authors:**
Husna Hasan,
Norfatin Salam,
Mohd Bakri Adam

**Abstract:**

**Keywords:**
Extreme temperature,
extreme value,
return level.

##### 8457 Forecasting Electricity Spot Price with Generalized Long Memory Modeling: Wavelet and Neural Network

**Authors:**
Souhir Ben Amor,
Heni Boubaker,
Lotfi Belkacem

**Abstract:**

**Keywords:**
k-factor,
GARMA,
LLWNN,
G-GARCH,
electricity price,
forecasting.

##### 8456 Implementation of Generalized Plasticity in Load-Deformation Behavior of Foundation with Emphasis on Localization Problem

**Authors:**
A. H. Akhaveissy

**Abstract:**

**Keywords:**
Localization phenomena,
Generalized plasticity,
Non-associated Flow Rule

##### 8455 Complex-Valued Neural Network in Signal Processing: A Study on the Effectiveness of Complex Valued Generalized Mean Neuron Model

**Authors:**
Anupama Pande,
Ashok Kumar Thakur,
Swapnoneel Roy

**Abstract:**

**Keywords:**
Complex valued neural network,
Generalized Meanneuron model,
Signal processing.

##### 8454 Laplace Transformation on Ordered Linear Space of Generalized Functions

**Authors:**
K. V. Geetha,
N. R. Mangalambal

**Abstract:**

**Keywords:**
Laplace transformable generalized function,
positive cone,
topology of bounded convergence

##### 8453 Weyl Type Theorem and the Fuglede Property

**Authors:**
M. H. M. Rashid

**Abstract:**

**Keywords:**
Fuglede Property,
Weyl’s theorem,
generalized
derivation,
Aluthge Transformation.

##### 8452 Preconditioned Generalized Accelerated Overrelaxation Methods for Solving Certain Nonsingular Linear System

**Authors:**
Deyu Sun,
Guangbin Wang

**Abstract:**

In this paper, we present preconditioned generalized accelerated overrelaxation (GAOR) methods for solving certain nonsingular linear system. We compare the spectral radii of the iteration matrices of the preconditioned and the original methods. The comparison results show that the preconditioned GAOR methods converge faster than the GAOR method whenever the GAOR method is convergent. Finally, we give two numerical examples to confirm our theoretical results.

**Keywords:**
Preconditioned,
GAOR method,
linear system,
convergence,
comparison.

##### 8451 On Fourier Type Integral Transform for a Class of Generalized Quotients

**Authors:**
A. S. Issa,
S. K. Q. AL-Omari

**Abstract:**

**Keywords:**
Fourier type integral,
Fourier integral,
generalized
quotient,
Boehmian,
distribution.

##### 8450 Detecting Earnings Management via Statistical and Neural Network Techniques

**Authors:**
Mohammad Namazi,
Mohammad Sadeghzadeh Maharluie

**Abstract:**

**Keywords:**
Earnings management,
generalized regression neural
networks,
linear regression,
multi-layer perceptron,
Tehran stock
exchange.

##### 8449 A Comparison of Marginal and Joint Generalized Quasi-likelihood Estimating Equations Based On the Com-Poisson GLM: Application to Car Breakdowns Data

**Authors:**
N. Mamode Khan,
V. Jowaheer

**Abstract:**

In this paper, we apply and compare two generalized estimating equation approaches to the analysis of car breakdowns data in Mauritius. Number of breakdowns experienced by a machinery is a highly under-dispersed count random variable and its value can be attributed to the factors related to the mechanical input and output of that machinery. Analyzing such under-dispersed count observation as a function of the explanatory factors has been a challenging problem. In this paper, we aim at estimating the effects of various factors on the number of breakdowns experienced by a passenger car based on a study performed in Mauritius over a year. We remark that the number of passenger car breakdowns is highly under-dispersed. These data are therefore modelled and analyzed using Com-Poisson regression model. We use the two types of quasi-likelihood estimation approaches to estimate the parameters of the model: marginal and joint generalized quasi-likelihood estimating equation approaches. Under-dispersion parameter is estimated to be around 2.14 justifying the appropriateness of Com-Poisson distribution in modelling underdispersed count responses recorded in this study.

**Keywords:**
Breakdowns,
under-dispersion,
com-poisson,
generalized linear model,
marginal quasi-likelihood estimation,
joint quasi-likelihood estimation.

##### 8448 Validity Domains of Beams Behavioural Models: Efficiency and Reduction with Artificial Neural Networks

**Authors:**
Keny Ordaz-Hernandez,
Xavier Fischer,
Fouad Bennis

**Abstract:**

In a particular case of behavioural model reduction by ANNs, a validity domain shortening has been found. In mechanics, as in other domains, the notion of validity domain allows the engineer to choose a valid model for a particular analysis or simulation. In the study of mechanical behaviour for a cantilever beam (using linear and non-linear models), Multi-Layer Perceptron (MLP) Backpropagation (BP) networks have been applied as model reduction technique. This reduced model is constructed to be more efficient than the non-reduced model. Within a less extended domain, the ANN reduced model estimates correctly the non-linear response, with a lower computational cost. It has been found that the neural network model is not able to approximate the linear behaviour while it does approximate the non-linear behaviour very well. The details of the case are provided with an example of the cantilever beam behaviour modelling.

**Keywords:**
artificial neural network,
validity domain,
cantileverbeam,
non-linear behaviour,
model reduction.

##### 8447 Investigation on Machine Tools Energy Consumptions

**Authors:**
Shiva Abdoli,
Daniel T. Semere

**Abstract:**

Several researches have been conducted to study consumption of energy in cutting process. Most of these researches are focusing to measure the consumption and propose consumption reduction methods. In this work, the relation between the cutting parameters and the consumption is investigated in order to establish a generalized energy consumption model that can be used for process and production planning in real production lines. Using the generalized model, the process planning will be carried out by taking into account the energy as a function of the selected process parameters. Similarly, the generalized model can be used in production planning to select the right operational parameters like batch sizes, routing, buffer size, etc. in a production line. The description and derivation of the model as well as a case study are given in this paper to illustrate the applicability and validity of the model.

**Keywords:**
Process parameters,
cutting process,
energy
efficiency,
Material Removal Rate (MRR).

##### 8446 Note to the Global GMRES for Solving the Matrix Equation AXB = F

**Authors:**
Fatemeh Panjeh Ali Beik

**Abstract:**

In the present work, we propose a new projection method for solving the matrix equation AXB = F. For implementing our new method, generalized forms of block Krylov subspace and global Arnoldi process are presented. The new method can be considered as an extended form of the well-known global generalized minimum residual (Gl-GMRES) method for solving multiple linear systems and it will be called as the extended Gl-GMRES (EGl- GMRES). Some new theoretical results have been established for proposed method by employing Schur complement. Finally, some numerical results are given to illustrate the efficiency of our new method.

**Keywords:**
Matrix equation,
Iterative method,
linear systems,
block Krylov subspace method,
global generalized minimum residual (Gl-GMRES).

##### 8445 Dynamic Model of a Buck Converter with a Sliding Mode Control

**Authors:**
S. Chonsatidjamroen ,
K-N. Areerak,
K-L. Areerak

**Abstract:**

**Keywords:**
Generalized state-space averaging method,
buck
converter,
sliding mode control,
modeling,
simulation.

##### 8444 The Relative Efficiency of Parameter Estimation in Linear Weighted Regression

**Authors:**
Baoguang Tian,
Nan Chen

**Abstract:**

A new relative efficiency in linear model in reference is instructed into the linear weighted regression, and its upper and lower bound are proposed. In the linear weighted regression model, for the best linear unbiased estimation of mean matrix respect to the least-squares estimation, two new relative efficiencies are given, and their upper and lower bounds are also studied.

**Keywords:**
Linear weighted regression,
Relative efficiency,
Mean matrix,
Trace.

##### 8443 Adomian’s Decomposition Method to Generalized Magneto-Thermoelasticity

**Authors:**
Hamdy M. Youssef,
Eman A. Al-Lehaibi

**Abstract:**

Due to many applications and problems in the fields of plasma physics, geophysics, and other many topics, the interaction between the strain field and the magnetic field has to be considered. Adomian introduced the decomposition method for solving linear and nonlinear functional equations. This method leads to accurate, computable, approximately convergent solutions of linear and nonlinear partial and ordinary differential equations even the equations with variable coefficients. This paper is dealing with a mathematical model of generalized thermoelasticity of a half-space conducting medium. A magnetic field with constant intensity acts normal to the bounding plane has been assumed. Adomian’s decomposition method has been used to solve the model when the bounding plane is taken to be traction free and thermally loaded by harmonic heating. The numerical results for the temperature increment, the stress, the strain, the displacement, the induced magnetic, and the electric fields have been represented in figures. The magnetic field, the relaxation time, and the angular thermal load have significant effects on all the studied fields.

**Keywords:**
Adomian’s Decomposition Method,
magneto-thermoelasticity,
finite conductivity,
iteration method,
thermal load.

##### 8442 Some Results on New Preconditioned Generalized Mixed-Type Splitting Iterative Methods

**Authors:**
Guangbin Wang,
Fuping Tan,
Deyu Sun

**Abstract:**

In this paper, we present new preconditioned generalized mixed-type splitting (GMTS) methods for solving weighted linear least square problems. We compare the spectral radii of the iteration matrices of the preconditioned and the original methods. The comparison results show that the preconditioned GMTS methods converge faster than the GMTS method whenever the GMTS method is convergent. Finally, we give a numerical example to confirm our theoretical results.

**Keywords:**
Preconditioned,
GMTS method,
linear system,
convergence,
comparison.