A Bayesian approach to estimating the regression coefficients of a multinomial logit model

A Bayesian approach to estimation of the regression coefficients of a multinominal logit model with ordinal scale response categories is presented. A Monte Carlo method is used to construct the posterior distribution of the link function. The link function is treated as an arbitrary scalar function. Then the Gauss-Markov theorem is used to determine a function of the link which produces a random vector of coefficients. The posterior distribution of the random vector of coefficients is used to estimate the regression coefficients. The method described is referred to as a Bayesian generalized least square (BGLS) analysis. Two cases involving multinominal logit models are described. Case I involves a cumulative logit model and Case II involves a proportional-odds model. All inferences about the coefficients for both cases are described in terms of the posterior distribution of the regression coefficients. The results from the BGLS method are compared to maximum likelihood estimates of the regression coefficients. The BGLS method avoids the nonlinear problems encountered when estimating the regression coefficients of a generalized linear model. The method is not complex or computationally intensive. The BGLS method offers several advantages over Bayesian approaches. ^

Application of the general linear model to assess the effect of missing data on bone marrow mononuclear cell therapy with infusion timing and follow-up MRI

With most clinical trials, missing data presents a statistical problem in evaluating a treatment's efficacy. There are many methods commonly used to assess missing data; however, these methods leave room for bias to enter the study. This thesis was a secondary analysis on data taken from TIME, a phase 2 randomized clinical trial conducted to evaluate the safety and effect of the administration timing of bone marrow mononuclear cells (BMMNC) for subjects with acute myocardial infarction (AMI).^ We evaluated the effect of missing data by comparing the variance inflation factor (VIF) of the effect of therapy between all subjects and only subjects with complete data. Through the general linear model, an unbiased solution was made for the VIF of the treatment's efficacy using the weighted least squares method to incorporate missing data. Two groups were identified from the TIME data: 1) all subjects and 2) subjects with complete data (baseline and follow-up measurements). After the general solution was found for the VIF, it was migrated Excel 2010 to evaluate data from TIME. The resulting numerical value from the two groups was compared to assess the effect of missing data.^ The VIF values from the TIME study were considerably less in the group with missing data. By design, we varied the correlation factor in order to evaluate the VIFs of both groups. As the correlation factor increased, the VIF values increased at a faster rate in the group with only complete data. Furthermore, while varying the correlation factor, the number of subjects with missing data was also varied to see how missing data affects the VIF. When subjects with only baseline data was increased, we saw a significant rate increase in VIF values in the group with only complete data while the group with missing data saw a steady and consistent increase in the VIF. The same was seen when we varied the group with follow-up only data. This essentially showed that the VIFs steadily increased when missing data is not ignored. When missing data is ignored as with our comparison group, the VIF values sharply increase as correlation increases.^

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^

Model results in NetCDF format of North African vegetation-precipitation in early and mid-Holocene climate simulated by CCSM3-DGVM//

The present study analyses the sign, strength, and working mechanism of the vegetation-precipitation feedback over North Africa in middle (6 ka BP) and early Holocene (9 ka BP) simulations using the comprehensive coupled climate-vegetation model CCSM3-DGVM (Community Climate System Model version 3 and a dynamic global vegetation model). The coupled model simulates enhanced summer rainfall and a northward migration of the West African monsoon trough along with an expansion of the vegetation cover for the early and middle Holocene compared to the pre-industrial period. It is shown that dynamic vegetation enhances the orbitally triggered summer precipitation anomaly by approximately 20% in the Sahara-Sahel region (10-25° N, 20° W-30° E) in both the early and mid-Holocene experiments compared to their fixed-vegetation counterparts. The primary vegetation-rainfall feedback identified here operates through surface latent heat flux anomalies by canopy evaporation and transpiration and their effect on the mid-tropospheric African easterly jet, whereas the effects of vegetation changes on surface albedo and local water recycling play a negligible role. Even though CCSM3-DGVM simulates a positive vegetation-precipitation feedback in the North African region, this feedback is not strong enough to produce multiple equilibrium climate-ecosystem states on a regional scale.

The emission reduction effect and economic impact of an energy tax vs. a carbon tax in China : a dynamic CGE model analysis

Chinese government commits to reach its peak carbon emissions before 2030, which requires China to implement new policies. Using a CGE model, this study conducts simulation studies on the functions of an energy tax and a carbon tax and analyzes their effects on macro-economic indices. The Chinese economy is affected at an acceptable level by the two taxes. GDP will lose less than 0.8% with a carbon tax of 100, 50, or 10 RMB/ton CO2 or 5% of the delivery price of an energy tax. Thus, the loss of real disposable personal income is smaller. Compared with implementing a single tax, a combined carbon and energy tax induces more emission reductions with relatively smaller economic costs. With these taxes, the domestic competitiveness of energy intensive industries is improved. Additionally, we found that the sooner such taxes are launched, the smaller the economic costs and the more significant the achieved emission reductions.

Extreme flood abatement in large dams with fixed-crest spillways

This study characterises the abatement effect of large dams with fixed-crest spillways under extreme design flood conditions. In contrast to previous studies using specific hydrographs for flow into the reservoir and simplifications to obtain analytical solutions, an automated tool was designed for calculations based on a Monte Carlo simulation environment, which integrates models that represent the different physical processes in watersheds with areas of 150?2000 km2. The tool was applied to 21 sites that were uniformly distributed throughout continental Spain, with 105 fixed-crest dam configurations. This tool allowed a set of hydrographs to be obtained as an approximation for the hydrological forcing of a dam and the characterisation of the response of the dam to this forcing. For all cases studied, we obtained a strong linear correlation between the peak flow entering the reservoir and the peak flow discharged by the dam, and a simple general procedure was proposed to characterise the peak-flow attenuation behaviour of the reservoir. Additionally, two dimensionless coefficients were defined to relate the variables governing both the generation of the flood and its abatement in the reservoir. Using these coefficients, a model was defined to allow for the estimation of the flood abatement effect of a reservoir based on the available information. This model should be useful in the hydrological design of spillways and the evaluation of the hydrological safety of dams. Finally, the proposed procedure and model were evaluated and representative applications were presented

Agent-based model for the effect of curing temperature on cement hydration

The agent-based model presented here, comprises an algorithm that computes the degree of hydration, the water consumption and the layer thickness of C-S-H gel as functions of time for different temperatures and different w/c ratios. The results are in agreement with reported experimental studies, demonstrating the applicability of the model. As the available experimental results regarding elevated curing temperature are scarce, the model could be recalibrated in the future. Combining the agent-based computational model with TGA analysis, a semiempirical method is achieved to be used for better understanding the microstructure development in ordinary cement pastes and to predict the influence of temperature on the hydration process.

A general framework to testing the effect of transport policy measures to achieve a modal shift: a sequental hybrid model

In order to achieve to minimize car-based trips, transport planners have been particularly interested in understanding the factors that explain modal choices. In the transport modelling literature there has been an increasing awareness that socioeconomic attributes and quantitative variables are not sufficient to characterize travelers and forecast their travel behavior. Recent studies have also recognized that users? social interactions and land use patterns influence travel behavior, especially when changes to transport systems are introduced, but links between international and Spanish perspectives are rarely deal. In this paper, factorial and path analyses through a Multiple-Indicator Multiple-Cause (MIMIC) model are used to understand and describe the relationship between the different psychological and environmental constructs with social influence and socioeconomic variables. The MIMIC model generates Latent Variables (LVs) to be incorporated sequentially into Discrete Choice Models (DCM) where the levels of service and cost attributes of travel modes are also included directly to measure the effect of the transport policies that have been introduced in Madrid during the last three years in the context of the economic crisis. The data used for this paper are collected from a two panel smartphone-based survey (n=255 and 190 respondents, respectively) of Madrid.

Factors driving the carbon mineralization priming effect in a sandy loam soil amended with different types of biochar

The effect of biochar on the soil carbon mineral- ization priming effect depends on the characteristics of the raw materials, production method and pyrolysis conditions. The goal of the present study is to evaluate the impact of three different types of biochar on physicochemical properties and CO2 emissions of a sandy loam soil. For this purpose, soil was amended with three different biochars (BI, BII and BIII) at a rate of 8 wt % and soil CO2 emissions were measured for 45 days. BI is produced from a mixed wood sieving from wood chip production, BII from a mixture of paper sludge and wheat husks and BIII from sewage sludge. Cumulative CO2 emissions of biochars, soil and amended soil were well fit to a simple first-order kinetic model with correlation coef- ficients (r 2 ) greater than 0.97. Results show a negative prim- ing effect in the soil after addition of BI and a positive prim- ing effect in the case of soil amended with BII and BIII. These results can be related to different biochar properties such as carbon content, carbon aromaticity, volatile matter, fixed carbon, easily oxidized organic carbon or metal and phenolic substance content in addition to surface biochar properties. Three biochars increased the values of soil field capacity and wilting point, while effects over pH and cation exchange capacity were not observed.

Influencia del viento en los ensayos de rebase en modelo físico a escala reducida = Wind effect on wave overtopping in physical model test

El artículo que se presenta a continuación recoge la ampliación de una investigación previa sobre los rebases en los espaldones de los diques verticales y en talud. Para ello se han realizado una serie de ensayos en modelo físico a escala reducida sobre la sección vertical del Dique de Levante de Málaga, cuyo objeto principal fue analizar el efecto del viento en el rebase. Los ensayos se han realizado generando oleaje con y sin viento, comparando los resultados obtenidos en cada una de las dos situaciones y se han llevado a cabo en el Canal de Oleaje y Viento de Gran Escala existente en el Laboratorio de Experimentación Marítima del Centro de Estudios de Puertos y Costas del CEDEX. The purpose of the research work as summarised in this article, resulting from diverse work carried out at the CEDEX, is to make an analysis of the influence of wind effects on the wave overtopping of vertical sea-walls. The results obtained in the Málaga´s Levante breakwater tests are presented here. The test was carried out in large sized facilities where waves and wind are generated.

Model of the aerodynamic behavior of a pararotor

Asimple semi-empirical model for the aerodynamic behavior of a low-aspect ratio pararotor in autorotation at low Reynolds numbers is presented. The paper is split into three sections: Sec. II deals with the theoretical model derivation, Sec. III deals with the wind-tunnel measurements needed for tuning the theoretical model, and Sec. IV deals with the tuning between the theoretical model and the experimental data. The study is focused on the effect of both the blade pitch angle and the blade roughness and also on the stream velocity, on the rotation velocity, and on the drag of a model. Flow pattern visualizations have also been performed. The value of the free aerodynamic parameters of the semi-empirical model that produces the best fit with the experimental results agrees with the expected ones for the blades at the test conditions. Finally, the model is able to describe the behavior of a pararotor in autorotation that rotates fixed to a shaft, validated for a range of blade pitch angles. The movement of the device is found to be governed by a reduced set of dimensionless parameters.

Conformational influences of glycosylation of a peptide: A possible model for the effect of glycosylation on the rate of protein folding

Improved strategies for synthesis make it possible to expand the range of glycopeptides available for detailed conformational studies. The glycopeptide 1 was synthesized using a new solid phase synthesis of carbohydrates and a convergent coupling to peptide followed by deprotection. Its conformational properties were subjected to NMR analysis and compared with a control peptide 2 prepared by conventional solid phase methods. Whereas peptide 2 fails to manifest any appreciable secondary structure, the glycopeptide 1 does show considerable conformational bias suggestive of an equilibrium between an ordered and a random state. The implications of this ordering effect for the larger issue of protein folding are considered.

Inhibitory effect of 9-(2-phosphonylmethoxyethyl)adenine on visna virus infection in lambs: a model for in vivo testing of candidate anti-human immunodeficiency virus drugs.

The acyclic nucleoside phosphonate analog 9-(2-phosphonylmethoxyethyl)adenine (PMEA) was recently found to be effective as an inhibitor of visna virus replication and cytopathic effect in sheep choroid plexus cultures. To study whether PMEA also affects visna virus infection in sheep, two groups of four lambs each were inoculated intracerebrally with 10(6.3) TCID50 of visna virus strain KV1772 and treated subcutaneously three times a week with PMEA at 10 and 25 mg/kg, respectively. The treatment was begun on the day of virus inoculation and continued for 6 weeks. A group of four lambs were infected in the same way but were not treated. The lambs were bled weekly or biweekly and the leukocytes were tested for virus. At 7 weeks after infection, the animals were sacrificed, and cerebrospinal fluid (CSF) and samples of tissue from various areas of the brain and from lungs, spleen, and lymph nodes were collected for isolation of virus and for histopathologic examination. The PMEA treatment had a striking effect on visna virus infection, which was similar for both doses of the drug. Thus, the frequency of virus isolations was much lower in PMEA-treated than in untreated lambs. The difference was particularly pronounced in the blood, CSF, and brain tissue. Furthermore, CSF cell counts were much lower and inflammatory lesions in the brain were much less severe in the treated lambs than in the untreated controls. The results indicate that PMEA inhibits the propagation and spread of visna virus in infected lambs and prevents brain lesions, at least during early infection. The drug caused no noticeable side effects during the 6 weeks of treatment.

Industry-specific effect of CSR initiatives: hotels and airlines

Purpose – This study aims to examine the relationships between a firm's corporate social responsibility (CSR) activities and its performance and risk. The authors hypothesize that industry-level effects are highly determinant of the sign and magnitude of these relationships to establish a ranking of industries to identify the position of the most prominent tourism-related industries: hotels and airlines. Based on the cybernetic model of decision making and the heuristics thereof, shareholders base their investment decisions derived from CSR announcements on the idea that the industries behave differently; their fixed costs being a relevant factor. Design/methodology/approach – The authors estimate the industry-specific effects of CSR initiatives on firms' performance and risk using a sample of 583 announcements from the Spanish Stock Market. Findings – The results show that while CSR announcements have a positive effect on performance when the authors do not account for industry-specific factors, once the authors incorporate these factors into the analysis, the authors find that firm performance and risk vary quite substantially as a function of the industry to which the firm belongs. Interestingly, while the hotel industry presents an average behavior (standing at 9th position in returns, 15th in terms of risk, and 8th according to the ratio returns/volatility), the airline industry presents the worst situation of all industries: last in performance and last in risk. Practical implications – The results help managers assess their decisions and allocate CSR resources optimally. Originality/value – This article is the first attempt to empirically test and comprehensively detect the different relationships between CSR and firm performance across industries.