Statistical Inference in Generalized Linear Mixed Models by Joint Modelling Mean and Covariance of Non-Normal Random Effects

Generalized linear mixed models (GLMMs) are typically constructed by incorporating random effects into the linear predictor. The random effects are usually assumed to be normally distributed with mean zero and variance-covariance identity matrix. In this paper, we propose to release random effects to non-normal distributions and discuss how to model the mean and covariance structures in GLMMs simultaneously. Parameter estimation is solved by using Quasi-Monte Carlo (QMC) method through iterative Newton-Raphson (NR) algorithm very well in terms of accuracy and stabilization, which is demonstrated by real binary salamander mating data analysis and simulation studies.

Critical Exercise Price for American Floating Strike Lookback Option in a Mixed Jump-Diffusion Model

This paper studies the critical exercise price of American floating strike lookback options under the mixed jump-diffusion model. By using It formula and Wick-It-Skorohod integral, a new market pricing model established under the environment of mixed jumpdiffusion fractional Brownian motion. The fundamental solutions of stochastic parabolic partial differential equations are estimated under the condition of Merton assumptions. The explicit integral representation of early exercise premium and the critical exercise price are also given, then the American floating strike lookback options factorization formula is obtained, the results is generalized the classical Black-Scholes market pricing model.

Nonlinear mixed-effects height to crown base and crown length dynamic models using the branch mortality technique for a Korean larch( Larix olgensis ) plantations in northeast China

Korean larch(Larix olgensis)is one of the main tree species for aff orestation and timber production in northeast China.However,its timber quality and growth ability are largely infl uenced by crown size,structure and shape.The majority of crown models are static models based on tree size and stand characteristics from temporary sample plots,but crown dynamic models has seldom been constructed.Therefore,this study aimed to develop height to crown base(HCB)and crown length(CL)dynamic models using the branch mortality technique for a Korean larch plantation.The nonlinear mixed-eff ects model with random eff ects,variance functions and correlation structures,was used to build HCB and CL dynamic models.The data were obtained from 95 sample trees of 19 plots in Meng JiaGang forest farm in Northeast China.The results showed that HCB progressively increases as tree age,tree height growth(HT growth)and diameter at breast height growth(DBH growth).The CL was increased with tree age in 20 years ago,and subsequently stabilized.HT growth,DBH growth stand basal area(BAS)and crown competition factor(CCF)signifi cantly infl uenced HCB and CL.The HCB was positively correlated with BAS,HT growth and DBH growth,but negatively correlated with CCF.The CL was positively correlated with BAS and CCF,but negatively correlated with DBH growth.Model fi tting and validation confi rmed that the mixed-eff ects model considering the stand and tree level random eff ects was accurate and reliable for predicting the HCB and CL dynamics.However,the models involving adding variance functions and time series correlation structure could not completely remove heterogeneity and autocorrelation,and the fi tting precision of the models was reduced.Therefore,from the point of view of application,we should take care to avoid setting up over-complex models.The HCB and CL dynamic models in our study may also be incorporated into stand growth and yield model systems in China.

The Sensitivity of Model Results to Specification of Network-Based Level of Service Attributes: An Application of a Mixed Logit Model to Trave Mode Choice

The need for travel demand models is growing worldwide. Obtaining reasonably accurate level of service (LOS) attributes of different travel modes such as travel time and cost representing the performance of transportation system is not a trivial task, especially in growing cities of developing countries. This study investigates the sensitivity of results of a travel mode choice model to different specifications of network-based LOS attributes using a mixed logit model. The study also looks at the possibilities of correcting some of the inaccuracies in network-based LOS attributes. Further, the study also explores the effects of different specifications of LOS data on implied values of time and aggregation forecasting. The findings indicate that the implied values of time are very sensitive to specification of data and model implying that utmost care must be taken if the purpose of the model is to estimate values of time. Models estimated on all specifications of LOS-data perform well in prediction, likely suggesting that the extra expense on developing a more detailed and accurate network models so as to derive more precise LOS attributes is unnecessary for impact analyses of some policies.

Sequencing of Mixed Model Assembly Lines Based on Improved Shuffled Frog Leaping Algorithm

Shuffled frog leaping algorithm( SFLA) was used to solve multi-objective sequencing problem of mixed model assembly line( MMAL). Local convergence can be avoided and optimal solution can be obtained to a certain extent. However,the multi-objective sequencing problem of MMAL is an non-deterministic polynomial hard( NP-hard) problem and the shortcomings are slow convergence rate and low precision. To solve the shortcomings for optimization objectives of minimizing total utility time and keeping average consumption rate of parts, a chaos differential evolution SFLA( CDESFLA) is proposed in this study. Because SFLA is easy to fall into local optimum,the evolution operator of differential evolution algorithms is introduced in SFLA as a local search strategy,and differential mutation operator is introduced in chaotic sequence to prevent premature convergence. The examples show that the proposed CDESFLA is better for convergence accuracy than SFLA,genetic algorithm( GA) and particle swarm optimization( PSO)

Path Selection of Multimodal Transport Based on Multi-Objective Mixed Integer Programming

Based on “One Belt and One Road”, this paper studies the path selection of multimodal transport by using the method of multi-objective mixed integer programming. Therefore, this paper studies the factors of transportation time, transportation cost and transportation safety performance, and establishes a mathematical model. In addition, the method of multi-objective mixed integer programming is used to comprehensively consider the different emphasis and differences of customers on cargo transportation. Then we use planning tools of Microsoft Excel to solve path selection and to determine whether the chosen path is economical and reliable. Finally, a relatively complex road network is built as an example to verify the accuracy of this planning method.

Optimal Redundancy Allocation in Hierarchical Series-Parallel Systems Using Mixed Integer Programming

Reliability optimization plays an important role in design, operation and management of the industrial systems. System reliability can be easily enhanced by improving the reliability of unreliable components and/or by using redundant configuration with subsystems/components in parallel. Redundancy Allocation Problem (RAP) was studied in this research. A mixed integer programming model was proposed to solve the problem, which considers simultaneously two objectives under several resource constraints. The model is only for the hierarchical series-parallel systems in which the elements of any subset of subsystems or components are connected in series or parallel and constitute a larger subsystem or total system. At the end of the study, the performance of the proposed approach was evaluated by a numerical example.

Numerical study on turbulent mixed convection in a vertical plane channel using hybrid RANS/LES and LES models

Two Delayed-Detached Eddy Simulation(DDES) models, and a Large-Eddy Simulation(LES) model are used to investigate the turbulent flows and mixed convection between a hot plate and a cold plate via the software FLUENT. The two DDES models include Production-limited DDES(PL-DDES) and Improved DDES(IDDES) models.The Wall-Adapting Local Eddy-Viscosity(WALE) model is the used LES model. The numerical computations are performed at Reynolds number Reb= 4494 and different Richardson numbers Ri = 0.025, 0.048, 0.1. The comparing data is from the Direct Numerical Simulation(DNS) at Reb= 4494 and Ri = 0.048. The comparison reveals that the two DDES models have better performance in predicting time-averaged parameters than the WALE model in the aiding flow. The best predicted time-averaged results are obtained by the PL-DDES model in the opposing flow. Furthermore, the results of different Ri obtained by the PL-DDES model agree well with the DNS data.

Reduction and Analysis of a Max-Plus Linear System to a Constraint Satisfaction Problem for Mixed Integer Programming

This research develops a solution method for project scheduling represented by a max-plus-linear (MPL) form. Max-plus-linear representation is an approach to model and analyze a class of discrete-event systems, in which the behavior of a target system is represented by linear equations in max-plus algebra. Several types of MPL equations can be reduced to a constraint satisfaction problem (CSP) for mixed integer programming. The resulting formulation is flexible and easy-to-use for project scheduling;for example, we can obtain the earliest output times, latest task-starting times, and latest input times using an MPL form. We also develop a key method for identifying critical tasks under the framework of CSP. The developed methods are validated through a numerical example.

Mathematical Modeling and Experimental Validation of Mixed Metal Oxide Thin Film Deposition by Spray Pyrolysis

The deposition of metal oxide films using Spray Pyrolysis Technique (SPT) is investigated through mathematical and physical modeling. A comprehensive model is developed in the processes including atomization, spray, evaporation, chemical reaction and deposition. The predicted results including particle size and film thickness are compared with the experimental data obtained in a complementary study. The predicted film thickness is in a good agreement with the measurements when the temperature is high enough for the chemical reaction to proceed. The model also adequately predicts the size distribution when the nanocrystals are well-structured at controlled temperature and concentration.

CLUSTERING POPULATIONS BY MIXED LINEAR MODELS

Two mixed linear models are proposed for grouping populations by a dissimilarity coefficent which has two parameters for squared difference of marginal mean and variance component of interaction.Cluster trees can be constructed by the mixed linear model approaches for experimental data with sampling errors within populations or with some missing values.Unweighted pair-group method ( UPGM ) is suggested as fusion method. Sampling variances of estimated dissimilarity coefficient can be obtained by the jackknife procedure.A one-tail t-test is applicable for detecting significance of dissimilarity of populaions within specific group.Unbiasedness and efficiency for estimation of dissimilarity coefficients are proved by Monte Carolo simulations.Worked example from cotton yield data is given for demonstration of the use of these cluster methods.

Mixed D-vine copula-based conditional quantile model for stochastic monthly streamflow simulation

Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.

Surface Mixed Layer Profile of Physical and Biogeochemical Variables in the Subpolar North-West and -East Atlantic Ocean: A Data-Model Comparison Study

This paper presents a study of physical and biogeochemical variables using numerical model and mixed layer oceanographic data from a 2 - 3 year?in situmeasurements in the Northwestern and Northeastern sites of the Atlantic Ocean. Model outputs are presented and indicated that very good estimates may be obtained. The outputs showed considerable agreement in reproducing seasonal distributions of?pCO2,?pCO2-T,?pCO2-nonT, mixed layer temperature, and chlorophyll-a?in both winter and summer, and therefore provide useful physical and theoretical understanding of their biogeochemistry. The model?pCO2indicated a distinct temporal variability with seasonal changes coinciding with the change in sea surface temperature. It also provides an agreement that there is a strong seasonal cycle of mixed layer parameters filliped by nonthermal and physical factors. As an outgrowth of this work, the?pCO2?model outputs affirm the North Atlantic Ocean capacity as an important oceanographic sink for anthropogenic carbon dioxide.

COMPLETE CONVERGENCE OF ERROR VARIANCE ESITIMATES UNDER Ф－MIXING ERROR IN LINEAR MODELS

In this paper,we consider the estimates d of error variance d2=Var(ei) in the linear models Yi=x' iβ+ei(i= 1, 2, ... ). We study the complete convergence of dm2-o2 when the error {ei }is a sequence of identically distributed p-mixing variables. And we also obtain the better convergence rates when {ei} is not identically distribution

Three-dimensional numerical modeling of gravity anomalies based on Poisson equation in spacewavenumber mixed domain

In gravity-anomaly-based prospecting, the computational and memory requirements for practical numerical modeling are potentially enormous. Achieving an efficient and precise inversion for gravity anomaly imaging over large-scale and complex terrain requires additional methods. To this end, we have proposed a new topography-capable By performing a two-dimensional Fourier transform in the horizontal directions, threedimensional partial differential equations in the spatial domain were transformed into a group of independent, one-dimensional differential equations engaged with different wave numbers. These independent differential equations are highly parallel across different wave numbers. differential equations with different wave numbers, and the efficiency of solving fixedbandwidth linear equations was further improved by a chasing method. In a synthetic test, a prism model was used to verify the accuracy and reliability of the proposed algorithm by comparing the numerical solution with the analytical solution. We studied the computational precision and efficiency with and without topography using different Fourier transform methods. The results showed that the Guass-FFT method has higher numerical precision, while the standard FFT method is superior, in terms of computation time, for inversion and quantitative interpretation under complicated terrain.

A Survey of Model Predictive Control Methods for Traffic Signal Control

Enhancing traffic efficiency and alleviating(even circumventing) traffic congestion with advanced traffic signal control(TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control(MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations,and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.

Genetic Algorithm for Concurrent Balancing of Mixed-Model Assembly Lines with Original Task Times of Models

The growing global competition compels manufacturing organizations to engage themselves in all productivity improvement activities. In this direction, the consideration of mixed-model assembly line balancing problem and implementing in industries plays a major role in improving organizational productivity. In this paper, the mixed model assembly line balancing problem with deterministic task times is considered. The authors made an attempt to develop a genetic algorithm for realistic design of the mixed-model assembly line balancing problem. The design is made using the originnal task times of the models, which is a realistic approach. Then, it is compared with the generally perceived design of the mixed-model assembly line balancing problem.

THEORETICAL BASIS AND GENERAL OPTIMAL FORMULATIONS OF ISOPARAMETRIC GENERALIZED HYBRID/MIXED ELEMENT MODEL FOR IMPROVED STRESS ANALYSIS

By the modified three-field Hu-Washizu principle, this paper establishes a theoretical founda- tion and general convenient formulations to generate convergent stable generalized hybrid/mixed cle- ment (GH/ME) model which is invariant with respect to coordinate, insensitive to geometric distortion and suitable for improved stress computation. In the two proposed formulations, the stress equilibrium and orthogonality constraints are imposed through incompatible displacement and internal strain modes respectively. The proposed model by the general formulations in this paper is characterized by including as- sumed stress/strain, assumed stress, variable-node, singular, compatible and incompatible GH/ME models. When using regular meshes or the constant values of the isoparametric Jacobian Det in the assumed strain in- terpolation, the incompatible GH/ME model degenerates to the hybrid/mixed element model. Both general and concrete guidelines for the optimal selection of element shape functions are suggested. By means of the GH/ME theory in this paper, a family of new GH/ME can be and have been easily constructed. The software can also be developed conveniently because all the standard subroutines for the corresponding isoparametric displacement elements can be utilized directly.

Research on the Agricultural Remote Sensing Image Enhancement Technology Based on the Mixed Entropy Model

Uncertainty is the most important factor affecting the quality of the remote sensing image classification.Aiming at the characteristics ofboth the random and the fuzzy uncertainties in the process of the remote sensing image classification,a method based on the mixed entropy model is proposed to measure these two uncertainties comprehensively,and a multi-scale evaluation index is established.Based on the analysis of the basic principles of the mixed entropy model,a method of using the statistical data of the feature space and the fuzzy classifier to establish the information entropy,the fuzzy entropy and the mixed entropy is proposed.At the same time,on the scale of the pixel and the category,the index of the mixed entropy of the pixel and the mixed entropy of the category are established to evaluate the uncertainty of the classification.

Short-Term Scheduling of Combined Cycle Units Using Mixed Integer Linear Programming Solution

Combined cycle plants (CCs) are broadly used all over the world. The inclusion of CCs into the optimal resource scheduling causes difficulties because they can be operated in different operating configuration modes based on the number of combustion and steam turbines. In this paper a model CCs based on a mixed integer linear programming approach to be included into an optimal short term resource optimization problem is presented. The proposed method allows modeling of CCs in different modes of operation taking into account the non convex operating costs for the different combined cycle mode of operation.