Simultaneous Modeling Approach Research Articles

Multivariate Analysis of a Sequence of Paired Data Matrices: Succesive and Simultaneous Approaches

The relationship between two data matrices has been studied in the interbattery factor analysis. When two data matrices are partitioned in rows, the relationship between two data matrices has been studied in the STATICO method. The main advantage of this method is the optimality of the compromise of co-structures. It is well known that the weighting coefficients of the compromise may be contrary sign in some cases and make it uninterpretable. Thus, many multivariate data analysis methods have been developed, particularly those designed to tackle the fundamental issue: the description of the relationships between two data matrices. This can be studied by successive modeling approaches as well as by a simultaneous modeling approach. These methods are based on co-inertia and can be reduced to finding the maximum, minimum, or other critical values of a ratio of quadratic forms. However, all these methods are successive. In this paper, we propose two algorithms. The first one called sDO-CCSWA (successive Double-Common Component and Specific Weight Analysis) maximizes the sum of squared covariances, by first finding the best pair-component solution, and repeating that process in the respective residual spaces. The sDO-CCSWA is a new monotonically convergent algorithm obtained by searching for a fixed point of the stationary equations. The second approach is a simultaneous algorithm (DO-CCSWA) which maximizes the sum of squared covariances.

Linking categorical models for prediction of pleasantness score using individual predictions of sweetness and creaminess: An advancement of categorical modeling

The aim of this work was to develop and evaluate approaches of linked categorical models using individual predictions of probability. A model was developed using data from a study which assessed the perception of sweetness, creaminess, and pleasantness in dairy solutions containing variable concentrations of sugar and fat. Ordered categorical models were used to predict the individual sweetness and creaminess scores and these individual predictions were used as covariates in the model of pleasantness response. The model using individual predictions was compared to a previously developed model using the amount of fat and sugar as covariates driving pleasantness score. The model using the individual prediction of odds of sweetness and creaminess had a lower variability of pleasantness than the model using the content of sugar and fat in the test solutions, which indicates that the individual odds explain part of the variability in pleasantness. Additionally, simultaneous and sequential modeling approaches were compared for the linked categorical model. Parameter estimation was similar, but precision was better with sequential modeling approaches compared to the simultaneous modeling approach. The previous model characterizing the pleasantness response was improved by using individual predictions of sweetness and creaminess rather than the amount of fat and sugar in the solution. The application of this approach provides an advancement within categorical modeling showing how categorical models can be linked to enable the utilization of individual prediction. This approach is aligned with biology of taste sensory which is reflective of the individual perception of sweetness and creaminess, rather than the amount of fat and sugar in the solution.

English

English

Candecomp/Parafac with zero constraints at arbitrary positions in a loading matrix

When one interprets Candecomp/Parafac (CP) solutions for analyzing three-way data, small loadings are often ignored, that is, considered to be zero. Rather than just considering them zero, it seems better to actually model such values as zero. This can be done by successive modeling approaches as well as by a simultaneous modeling approach. This paper offers algorithms for three such approaches, and compares them on the basis of empirical data and a simulation study. The conclusion of the latter was that, under realistic circumstances, all approaches recovered the underlying structure well, when the number of values to constrain to zero was given. Whereas the simultaneous modeling approach seemed to perform slightly better, differences were very small and not substantial. Given that the simultaneous approach is far more time consuming than the successive approaches, the present study suggests that for practical purposes successive approaches for modeling zeros in the CP model seem to be indicated.

Simultaneous vegetation classification and mapping at large spatial scales

AbstractAimMultivariate mixture models offer the ability to streamline the typically multi‐stage process of vegetation classification and mapping into a single, simultaneous analytical step. Our aim is to demonstrate the model's utility over large and diverse areas, with comparison to existing classification and mapping conventions.LocationNew South Wales, Australia.MethodsWe demonstrate a statistical model that uses both multivariate species data and environmental covariate data to simultaneously classify observations into groups and predict those groups out into environmental and geographical space. We used two large data sets to demonstrate the method: an ~810,000 km2 area with 4,715 sites and 488 species and a ~220,000 km2 area with 5,183 sites and 446 species. A range of topographic, terrain, climate and soil predictors were used as environmental variables, including future projected climate variables. Models can be fit with the R package “RCPmod.”ResultsThe method results in probabilistic memberships to vegetation assemblages, for both the classification and map. There was general agreement between our approach and existing vegetation classification conventions, but we explore some notable differences. We were able to examine the environmental gradients that define the predicted vegetation distributions, as well as make predictions about how the distribution of species assemblages might shift as a result of climate change.Main conclusionsOur approach tightens the link between description of biodiversity patterns and their depiction in space, by considering both biotic and spatially explicit abiotic information simultaneously. The method allows uncertainty to be quantified objectively across a consistent set of groups for both vegetation classification and mapping, which is rarely the case in traditional multi‐stage approaches. A simultaneous modelling approach increases capacity to make predictions into varying spatial, temporal and environmental dimensions, providing new ecological insights and increasing capacity for evidence‐based decision making.

MODELLING OF DAILY ACTIVITY SCHEDULE OF WORKERS USING UNSUPERVISED MACHINE LEARNING TECHNIQUE

Travel demand models are used to replicate the real world travel demand and to predict the future travel demand. A behavioural oriented approach in travel demand analysis is provided by activity based travel demand modelling and it provides a better understanding of the travel behaviour of an individual. A sequential modelling approach using econometric models is commonly used in activity based travel modelling. In this method, error obtained in one model will be carried forward to the second model and so on. Hence chances of accumulation of error are more at the final stage, when the models are used for prediction. Hence an attempt is made in this study to replace the sequential econometric modelling approach with simultaneous modelling approach using an unsupervised machine learning technique. Three stage Neural Network modelling used in this study replaces sixteen stage econometric models. The predictive accuracy of all the output parameters was compared in both the modelling approaches. Results shows that Artificial Neural Network (ANN) results outperform econometric models. The decrease in percentage error ranges from 2.22% to 27.17%.

Validation of a coupled heat, vapour and liquid moisture transport model for porous materials implemented in CFD

Moisture-related damage is an important issue when looking at the performance of building envelopes. In order to accurately predict the moisture behaviour of building components, building designers can resort to Heat, Air and Moisture (HAM) models. In this paper a newly developed heat and mass transfer model that is implemented in a 3D finite volume solver, Fluent®, is presented. This allows a simultaneous modelling approach of both the convective conditions surrounding a porous material and the heat and moisture transport in the porous material governed by diffusion. Unlike most HAM models that often confine to constant convective transport coefficients it is now possible to better predict these convective boundary conditions. An important application of the model is the convective drying of porous building materials. Especially during the first drying stage, the drying rate is determined by the convective boundary conditions. The model was validated against a convective drying experiment from literature, in which a saturated ceramic brick sample is dried by flowing dry air over one side of the sample surface. Temperature and relative humidity measurements at different depths in the sample, moisture distribution profiles and mass loss measurements were compared with simulation results. An overall good agreement between the coupled model and the experiments was found, however, the model predicted the constant drying rate period better than the falling rate period. This was improved by adjusting the material properties. The adjustment of the material properties was supported by neutron radiography measurements.

Detecting trends in diameter growth of Norway spruce on long-term forest research plots using linear mixed-effects models

The objective of the study was to introduce a simultaneous approach to extracting growth trends from diameter increment series by combining tree- and stand-level information in the framework of mixed-effects modeling. The model results are compared with those of the sequential modeling approach according to Yue et al. Can J For Res 41:1577–1589, (2011). Comprehensive data from periodically repeated tree and stand measurements on 113 Norway spruce long-term forest research stands distributed throughout southwestern Germany as well as annually resolved diameter growth data from stem analysis of 581 sample trees are used for model parameterization. Results provide clear evidence for distinct temporal variation due to environment-related effects after accounting for tree- and stand-level effects on diameter growth of Norway spruce over the past 136 years, with especially high growth levels during the last four decades. Most remarkable is the considerable increase in diameter growth rates following the 1947–1952 growth depression. From the early 1960s onwards, growth rates fluctuated on an elevated level until most recent years. Model comparison reveals that results of the sequential and simultaneous modeling approaches are similar with respect to the course of the time-specific environmental effects on growth. We conclude that the proposed simultaneous modeling approach has the advantages of extracting growth trends at a higher level of precision and being the more parsimonious modeling option.

A survey on the relationship between ownership structure, debt policy and dividend policy in Tunisian stock exchange : Three stage least square simultaneous model approach

This research tests the efficiency of the debt policy, dividend policy and ownership structure as mechanism of resolution of agency conflicts between shareholders and managers due to the problem of overinvestment, in the limitation of the problem of the free cash flow. By estimating three stage least square simultaneous model and on the basis of a sample of 35 non-financial Tunisian listed companies selected for the period 2000–2009, our results are in favor of the theory of free cash flows of Jensen (1986) that stipulates that the debt policy represents the principal governance mechanism that can limit the risk of free cash flow. However, our empirical results do not confirm our hypothesis implies that the solution to reduce the level of free cash flow in the Tunisian firms with low growth opportunities is the use of policy dividends. It therefore appears that in the case of our sample, managers must settle their debts to creditors. They should thus allocate free cash flow to profitable projects. Thus, the debt reduces agency costs of free cash flow and present as a control mechanism which substitute dividend policy. Also, it is found that managerial ownership lowers the level of agency costs of free cash flow. However, the ownership concentration increases the risk of the free cash flow. Finally, regarding the impact of ownership structure on the payout ratio, our results support the idea that more risk aversion of the majority shareholders and their intention to expropriate minority shareholders through the extraction private benefits are the cause of a low dividend.

Evaluating the effects of land use and strategies for parking and transit supply on mode choice of downtown commuters

Metropolitan regions around the world are looking for sustainable strategies to battle energy consumption and emissions. These strategies include land use policies and the desire to decrease the externalities from transportation and therefore increase the share of public transit use. The goal of this research is to better understand the relationships between Land-use (LU), public transit accessibility (PT), parking policies, and mode choice for suburban Montreal commuters. In this regard we evaluate the potential impact of land-use, transit accessibility and parking policies on the mode choice of the households dwelling in commuter rail line catchments in the region of Montreal, Canada. The approach adopted here is to consider mode choice as a joint decision with neighborhood type choice, based on neighborhood typologies derived using a cluster analysis characterized by land use variables. Also, the effect of parking fees on mode choice is investigated and quantified. The method used in this study is a simultaneous modeling approach to account for the endogeneity of mode and household location choice. The model system is made up of two models – a neighborhood type choice model and a mode choice model. The results demonstrate the robustness of the models and methodology implemented thus providing important innovations for transportation and land-use planning.

Forecasting high-speed rail ridership using a simultaneous modeling approach

The newly launched, June 2009, US High-Speed Intercity Passenger Rail Program has rekindled a renewed interest in forecasting high-speed rail (HSR) ridership. The first step to the concerted effort by the federal, state, rail, and other related agencies to develop a nationwide HSR network is the development of credible approaches to forecast the ridership. This article presents a nested logit/simultaneous choice model to improve the demand forecast in the context of intercity travel. In addition to incorporating the interrelationship between trip generation and mode choice decisions, the simultaneous model also provides a platform for the same utility function flowing between both the decision-making processes. Using American Travel Survey data, supplemented by various mode parameters, the proposed model improves the forecast accuracy and confirms the significant impact of travel costs on both mode choice and trip generation. Furthermore, the cross elasticity of mode choice and trip generation related to travel costs and other modal characteristics may shed some light on transportation policies in the area of intercity travel, especially in anticipation of HSR development.

Interactions between free cash flow, debt policy and structure of governance: Three stage least square simultaneous model approach: evidence from the Tunisian stock exchange

This research tests the efficiency of the ownership structure and the debt policy as mechanism of resolution of agency conflicts between shareholders and managers due to the problem of overinvestment, in the limitation of the problem of the free cash flow, by estimating three stage least square simultaneous model and on the basis of a sample of 35 non-financial Tunisian listed companies selected for the period 1999–2008. Our results are in favour of the theory of free cash flows of Jensen (1986) that stipulates that the debt policy represents the principal governance mechanism that can limit the risk of free cash flow. However, the ownership concentration and managerial ownership increase the risk of the free cash flow.

The Formation and Outcomes of Cohabiting and Marital Partnerships in Early Adulthood: The Role of Previous Partnership Experience

SummaryWe estimate a joint model of the formation and dissolution of cohabiting and marital unions among British women who were born in 1970. The focus of the analysis is the effect of previous cohabitation and marriage on subsequent partnership transitions. We use a multilevel simultaneous equations event history model to allow for residual correlation between the hazards of moving from an unpartnered state into cohabitation or marriage, converting a cohabiting union into marriage and dissolution of either form of union. A simultaneous modelling approach allows for the joint determination of these transitions, which may otherwise bias estimates of the effects of previous partnership outcomes on later transitions.

A novel simultaneous modeling approach to estimate linearity of pharmacokinetic parameters, including saturation of intestinal efflux, in the rat

Introduction Various independent methods exist for the estimation of linearity of pharmacokinetic parameters in vivo. A novel simultaneous modeling approach has been developed in the rat that in combination allows estimation of the rate and extent of duodenal absorption, hepatic first pass extraction and saturation of potential dose-dependent duodenal bioavailability (saturation of intestinal efflux). Methods Simultaneous modeling of plasma concentrations of a Pfizer compound in the rat were conducted using NONMEM™ after (1) accelerated intravenous, intraduodenal, and intraportal infusions over 5 h and (2) 5-min intravenous infusions of 0.28 and 1.4 mg doses. Results The data was best described by a two-compartment linear pharmacokinetic model with good agreement between observed and model predicted plasma concentrations following the various routes of administration. Clearance was estimated to be linear up to plasma concentrations of 1200 ng/ml. The estimated rate constants (± asymptotic errors) for intraduodenal absorption (KA), movement of drug from plasma to tissue (K23) and movement of drug from tissue to plasma (K32) were 0.645 ± 0.107, 18.0 ± 2.98, and 2.02 ± 0.209 h − 1 , respectively. The rate constants for drug elimination from the central compartment (K20) after 5-min intravenous infusion or accelerated infusion were 3.24 ± 0.6 or 6.26 ± 1.64 h − 1 . The estimated maximal extent of first pass extraction was 17%. The model (including increasing duodenal bioavailability as the amount in the duodenum increases, from a minimum of 5% to a maximum estimated intraportal bioavailability value— 83%) indicated a saturable intestinal efflux process. Discussion This novel study design and the proposed method for data analysis provides a robust and efficient means for assessing the linearity of multiple pharmacokinetic processes while accounting for the multi-compartmental distribution characteristics of the test compound.

Stochastic Cost-Effectiveness Analysis: A Simultaneous Marginal-Effect Approach

Objective The purpose of this study is to develop a cost-effectiveness methodology in the context of a simultaneous modeling framework that provides consistent point and interval estimates. Methods A simultaneous model of cost and effectiveness functions was developed to measure the incremental cost-effectiveness ratio for competing medical interventions. A feasible nonlinear least-squares method was suggested to estimate the simultaneous model. Using a series of hypothetical data, a simulation analysis was performed to show the superior performance of the proposed model, relative to the average-effect model, a widely used approach to cost-effectiveness estimation. Results The traditional average-effect approach has two shortcomings. First, it assumes two strong conditions: truly random distributions of all the significant nontreatment variables (both observed and unobserved) across study groups, and the independence of cost and effectiveness variables. Second, it does not give the confidence interval, an important measure to assess the stochastic nature and robustness of point estimates. In contrast, the simultaneous modeling approach provides marginal-effect estimates, imposing no restrictions on the random distributions of the individual characteristics across study groups. Furthermore, it takes into account the simultaneity of cost and effectiveness functions being estimated. The simulation analysis showed that the simultaneous modeling approach is significantly more unbiased and efficient in predicting the true cost-effectiveness ratio. Conclusion The simultaneous modeling approach is superior to the average-effect approach in the estimation of incremental cost-effectiveness ratios using data with significant nontreatment confounding factors. The advantages of the simultaneous modeling approach are particularly appealing for evaluative studies dealing with large-scale retrospective data at the patient level.

A Simultaneous Model Approach to the Determinants of Male Earnings Differentials in Turkey for 1968

Income distribution is a topical issue in all countries regardless of their economic and social systems or levels of development. In recent years, relationship between income distribution and process of development has come under increasing scrutiny. Parallel to this development, increasing attention has been given by economists and statisticians to empirical analysis of distribution of income. The purpose of this paper is to examine causes of annual earnings differentials among male household heads outside agriculture in Turkey for 1968.1,2 The primary data source is a survey conducted by Department of Population Studies of Hacettepe University of Turkey in 1968.3 In most of studies that deal with causes of inequality, key explanatory variables have been education and experience (Becker and Chiswick, 1966; Mincer, 1974). Others have introduced geographic location, ability, occupation, and physical capital factors in addition to human capital factors (Fishlow, 1974; Griliches and Mason, 1972). As Richard D. Morgenstern notes, however, the notion that socio-economic background is important in determining an individual's income has been played down in literature concurrent with rise in popularity of human capital explanation of earnings differences.4 Only recently, socioeconomic background factors and intergenerational transfers of inequality have been gaining their deserved importance in income distribution literature.5 The second shortcoming of single equation models of income distribution has been their lack of attention to interactions between explanatory variables. Ribich and Murphy point out that while single equation models might suffice as a reasohable equation for predicting earnings, relative importance of independent variables would be obscured by statistical tests that focused on this equation alone.6 In this paper, distribution of earnings among male heads of households is examined within frameworks of recursive and simultaneous equation models. The models developed have three endogenous variables, namely, education, occupation and earnings. The exogenous variables include availability and quality of schooling, experience, geographical location and socio-economic background factors. The causal model of income determination employed in this study is given in figure 1.