A dynamic random effects multinomial logit model of household car ownership

DEFF Research Database (Denmark)

Bue Bjørner, Thomas; Leth-Petersen, Søren

2007-01-01

Using a large household panel we estimate demand for car ownership by means of a dynamic multinomial model with correlated random effects. Results suggest that the persistence in car ownership observed in the data should be attributed to both true state dependence and to unobserved heterogeneity...... (random effects). It also appears that random effects related to single and multiple car ownership are correlated, suggesting that the IIA assumption employed in simple multinomial models of car ownership is invalid. Relatively small elasticities with respect to income and car costs are estimated...

Tennis Elbow Diagnosis Using Equivalent Uniform Voltage to Fit the Logistic and the Probit Diseased Probability Models

Directory of Open Access Journals (Sweden)

Tsair-Fwu Lee

2015-01-01

Full Text Available To develop the logistic and the probit models to analyse electromyographic (EMG equivalent uniform voltage- (EUV- response for the tenderness of tennis elbow. In total, 78 hands from 39 subjects were enrolled. In this study, surface EMG (sEMG signal is obtained by an innovative device with electrodes over forearm region. The analytical endpoint was defined as Visual Analog Score (VAS 3+ tenderness of tennis elbow. The logistic and the probit diseased probability (DP models were established for the VAS score and EMG absolute voltage-time histograms (AVTH. TV50 is the threshold equivalent uniform voltage predicting a 50% risk of disease. Twenty-one out of 78 samples (27% developed VAS 3+ tenderness of tennis elbow reported by the subject and confirmed by the physician. The fitted DP parameters were TV50 = 153.0 mV (CI: 136.3–169.7 mV, γ50 = 0.84 (CI: 0.78–0.90 and TV50 = 155.6 mV (CI: 138.9–172.4 mV, m = 0.54 (CI: 0.49–0.59 for logistic and probit models, respectively. When the EUV ≥ 153 mV, the DP of the patient is greater than 50% and vice versa. The logistic and the probit models are valuable tools to predict the DP of VAS 3+ tenderness of tennis elbow.

Tennis Elbow Diagnosis Using Equivalent Uniform Voltage to Fit the Logistic and the Probit Diseased Probability Models

Science.gov (United States)

Lin, Wei-Chun; Lin, Shu-Yuan; Wu, Li-Fu; Guo, Shih-Sian; Huang, Hsiang-Jui; Chao, Pei-Ju

2015-01-01

To develop the logistic and the probit models to analyse electromyographic (EMG) equivalent uniform voltage- (EUV-) response for the tenderness of tennis elbow. In total, 78 hands from 39 subjects were enrolled. In this study, surface EMG (sEMG) signal is obtained by an innovative device with electrodes over forearm region. The analytical endpoint was defined as Visual Analog Score (VAS) 3+ tenderness of tennis elbow. The logistic and the probit diseased probability (DP) models were established for the VAS score and EMG absolute voltage-time histograms (AVTH). TV50 is the threshold equivalent uniform voltage predicting a 50% risk of disease. Twenty-one out of 78 samples (27%) developed VAS 3+ tenderness of tennis elbow reported by the subject and confirmed by the physician. The fitted DP parameters were TV50 = 153.0 mV (CI: 136.3–169.7 mV), γ 50 = 0.84 (CI: 0.78–0.90) and TV50 = 155.6 mV (CI: 138.9–172.4 mV), m = 0.54 (CI: 0.49–0.59) for logistic and probit models, respectively. When the EUV ≥ 153 mV, the DP of the patient is greater than 50% and vice versa. The logistic and the probit models are valuable tools to predict the DP of VAS 3+ tenderness of tennis elbow. PMID:26380281

Another Look at the Method of Y-Standardization in Logit and Probit Models

DEFF Research Database (Denmark)

Karlson, Kristian Bernt

2015-01-01

This paper takes another look at the derivation of the method of Y-standardization used in sociological analysis involving comparisons of coefficients across logit or probit models. It shows that the method can be derived under less restrictive assumptions than hitherto suggested. Rather than...

Comparison of multinomial and binomial proportion methods for analysis of multinomial count data.

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Galyean, M L; Wester, D B

2010-10-01

Simulation methods were used to generate 1,000 experiments, each with 3 treatments and 10 experimental units/treatment, in completely randomized (CRD) and randomized complete block designs. Data were counts in 3 ordered or 4 nominal categories from multinomial distributions. For the 3-category analyses, category probabilities were 0.6, 0.3, and 0.1, respectively, for 2 of the treatments, and 0.5, 0.35, and 0.15 for the third treatment. In the 4-category analysis (CRD only), probabilities were 0.3, 0.3, 0.2, and 0.2 for treatments 1 and 2 vs. 0.4, 0.4, 0.1, and 0.1 for treatment 3. The 3-category data were analyzed with generalized linear mixed models as an ordered multinomial distribution with a cumulative logit link or by regrouping the data (e.g., counts in 1 category/sum of counts in all categories), followed by analysis of single categories as binomial proportions. Similarly, the 4-category data were analyzed as a nominal multinomial distribution with a glogit link or by grouping data as binomial proportions. For the 3-category CRD analyses, empirically determined type I error rates based on pair-wise comparisons (F- and Wald chi(2) tests) did not differ between multinomial and individual binomial category analyses with 10 (P = 0.38 to 0.60) or 50 (P = 0.19 to 0.67) sampling units/experimental unit. When analyzed as binomial proportions, power estimates varied among categories, with analysis of the category with the greatest counts yielding power similar to the multinomial analysis. Agreement between methods (percentage of experiments with the same results for the overall test for treatment effects) varied considerably among categories analyzed and sampling unit scenarios for the 3-category CRD analyses. Power (F-test) was 24.3, 49.1, 66.9, 83.5, 86.8, and 99.7% for 10, 20, 30, 40, 50, and 100 sampling units/experimental unit for the 3-category multinomial CRD analyses. Results with randomized complete block design simulations were similar to those with the CRD

Evaluating the performance of simple estimators for probit models with two dummy endogenous regressors

DEFF Research Database (Denmark)

Holm, Anders; Nielsen, Jacob Arendt

2013-01-01

This study considers the small sample performance of approximate but simple two-stage estimators for probit models with two endogenous binary covariates. Monte Carlo simulations showthat all the considered estimators, including the simulated maximum-likelihood (SML) estimation, of the trivariate ...

Retrieving unobserved consideration sets from household panel data

NARCIS (Netherlands)

J.E.M. van Nierop; R. Paap (Richard); B. Bronnenberg; Ph.H.B.F. Franses (Philip Hans); M. Wedel (Michel)

2005-01-01

textabstractWe propose a new model to describe consideration, consisting of a multivariate probit model component for consideration and a multinomial probit model component for choice, given consideration. The approach allows one to analyze stated consideration set data, revealed consideration set

Logit and probit model in toll sensitivity analysis of Solo-Ngawi, Kartasura-Palang Joglo segment based on Willingness to Pay (WTP)

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Handayani, Dewi; Cahyaning Putri, Hera; Mahmudah, AMH

2017-12-01

Solo-Ngawi toll road project is part of the mega project of the Trans Java toll road development initiated by the government and is still under construction until now. PT Solo Ngawi Jaya (SNJ) as the Solo-Ngawi toll management company needs to determine the toll fare that is in accordance with the business plan. The determination of appropriate toll rates will affect progress in regional economic sustainability and decrease the traffic congestion. These policy instruments is crucial for achieving environmentally sustainable transport. Therefore, the objective of this research is to find out how the toll fare sensitivity of Solo-Ngawi toll road based on Willingness To Pay (WTP). Primary data was obtained by distributing stated preference questionnaires to four wheeled vehicle users in Kartasura-Palang Joglo artery road segment. Further data obtained will be analysed with logit and probit model. Based on the analysis, it is found that the effect of fare change on the amount of WTP on the binomial logit model is more sensitive than the probit model on the same travel conditions. The range of tariff change against values of WTP on the binomial logit model is 20% greater than the range of values in the probit model . On the other hand, the probability results of the binomial logit model and the binary probit have no significant difference (less than 1%).

and Multinomial Logistic Regression

African Journals Online (AJOL)

This work presented the results of an experimental comparison of two models: Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) for classifying students based on their academic performance. The predictive accuracy for each model was measured by their average Classification Correct Rate (CCR).

The MDI Method as a Generalization of Logit, Probit and Hendry Analyses in Marketing.

Science.gov (United States)

1980-04-01

model involves nothing more than fitting a normal distribution function ( Hanushek and Jackson (1977)). For a given value of x, the probit model...preference shifts within the soft drink category. --For applications of probit models relevant for marketing, see Hausman and Wise (1978) and Hanushek and...Marketing Research" JMR XIV, Feb. (1977). Hanushek , E.A., and J.E. Jackson, Statistical Methods for Social Scientists. Academic Press, New York (1977

Interpreting Marginal Effects in the Multinomial Logit Model

DEFF Research Database (Denmark)

Wulff, Jesper

2014-01-01

with a substantial increase in the probability of entering a foreign market using a joint venture, while increases in the unpredictability in the host country environment are associated with a lower probability of wholly owned subsidiaries and a higher probability of exporting entries....... that have entered foreign markets. Through the application of a multinomial logit model, careful analysis of the marginal effects is performed through graphical representations, marginal effects at the mean, average marginal effects and elasticities. I show that increasing cultural distance is associated......This paper presents the challenges when researchers interpret results about relationships between variables from discrete choice models with multiple outcomes. The recommended approach is demonstrated by testing predictions from transaction cost theory on a sample of 246 Scandinavian firms...

PROBIT MODEL ANALYSIS OF SMALLHOLDER’S FARMERS DECISION TO USE AGROCHEMICAL INPUTS IN GWAGWALADA AND KUJE AREA COUNCILS OF FEDERAL CAPITAL TERRITORY, ABUJA, NIGERIA

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Omotayo Olugbenga Alabi

2014-01-01

Full Text Available This study examined Probit model analysis of smallholder’s farmers decision to use agrochemical inputs in Gwagwalada and Kuje Area Councils of Federal Capital Territory, Abuja, Nigeria. Primary data were used for this study. Data were obtained using structured questionnaire. The questionnaires were administered to sixty smallholder’s farmers sampled using a two-stage sampling technique. Data obtained were analyzed using descriptive statistics and Probit model. Eight estimators, age; farm-size; education–level; extension services; access to credit; off-farm income; experiences in farming; in the Probit model were found statistically significant. Results show that the probability of using agrochemical inputs increases with age; farm-size; family-size; education-level; extension services; experiences in farming but decreases where they have off-farm income and access to credits. Mc Fadden Pseudo-R 2 gives 0.6866 and Probit model correctly classified 93%. This study concluded that capacity of agricultural extension agents needs to be improved in the study area to educate farmers to invest in agrochemicals and improved agricultural technologies. Also, Government needs to improve on good road networks and appropriate policies to regulate standard, use, safety needs and environment of use of agrochemicals in the study area.

Insights into the latent multinomial model through mark-resight data on female grizzly bears with cubs-of-the-year

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Higgs, Megan D.; Link, William; White, Gary C.; Haroldson, Mark A.; Bjornlie, Daniel D.

2013-01-01

Mark-resight designs for estimation of population abundance are common and attractive to researchers. However, inference from such designs is very limited when faced with sparse data, either from a low number of marked animals, a low probability of detection, or both. In the Greater Yellowstone Ecosystem, yearly mark-resight data are collected for female grizzly bears with cubs-of-the-year (FCOY), and inference suffers from both limitations. To overcome difficulties due to sparseness, we assume homogeneity in sighting probabilities over 16 years of bi-annual aerial surveys. We model counts of marked and unmarked animals as multinomial random variables, using the capture frequencies of marked animals for inference about the latent multinomial frequencies for unmarked animals. We discuss undesirable behavior of the commonly used discrete uniform prior distribution on the population size parameter and provide OpenBUGS code for fitting such models. The application provides valuable insights into subtleties of implementing Bayesian inference for latent multinomial models. We tie the discussion to our application, though the insights are broadly useful for applications of the latent multinomial model.

A Comparison of Alternative Specifications of the College Attendance Equation with an Extension to two-stage Selectivity-Correction Models.

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Hilmer, Michael J.

2001-01-01

Estimates a college-attendance equation for a common set of students (from the High School and Beyond Survey) using three popular econometric specifications: the multinomial logit, the ordered probit, and the bivariate probit. Estimated marginal effects do not differ significantly across the three specifications. Choice of specification may not…

Modeling Information Content Via Dirichlet-Multinomial Regression Analysis.

Science.gov (United States)

Ferrari, Alberto

2017-01-01

Shannon entropy is being increasingly used in biomedical research as an index of complexity and information content in sequences of symbols, e.g. languages, amino acid sequences, DNA methylation patterns and animal vocalizations. Yet, distributional properties of information entropy as a random variable have seldom been the object of study, leading to researchers mainly using linear models or simulation-based analytical approach to assess differences in information content, when entropy is measured repeatedly in different experimental conditions. Here a method to perform inference on entropy in such conditions is proposed. Building on results coming from studies in the field of Bayesian entropy estimation, a symmetric Dirichlet-multinomial regression model, able to deal efficiently with the issue of mean entropy estimation, is formulated. Through a simulation study the model is shown to outperform linear modeling in a vast range of scenarios and to have promising statistical properties. As a practical example, the method is applied to a data set coming from a real experiment on animal communication.

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

Science.gov (United States)

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

DETERMINANTS OF SOVEREIGN RATING: FACTOR BASED ORDERED PROBIT MODELS FOR PANEL DATA ANALYSIS MODELING FRAMEWORK

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Dilek Teker

2013-01-01

Full Text Available The aim of this research is to compose a new rating methodology and provide credit notches to 23 countries which of 13 are developed and 10 are emerging. There are various literature that explains the determinants of credit ratings. Following the literature, we select 11 variables for our model which of 5 are eliminated by the factor analysis. We use specific dummies to investigate the structural breaks in time and cross section such as pre crises, post crises, BRIC membership, EU membership, OPEC membership, shipbuilder country and platinum reserved country. Then we run an ordered probit model and give credit notches to the countries. We use FITCH ratings as benchmark. Thus, at the end we compare the notches of FITCH with the ones we derive out of our estimated model.

A Spatial Probit Econometric Model of Land Change: The Case of Infrastructure Development in Western Amazonia, Peru

Science.gov (United States)

Arima, E. Y.

2016-01-01

Tropical forests are now at the center stage of climate mitigation policies worldwide given their roles as sources of carbon emissions resulting from deforestation and forest degradation. Although the international community has created mechanisms such as REDD+ to reduce those emissions, developing tropical countries continue to invest in infrastructure development in an effort to spur economic growth. Construction of roads in particular is known to be an important driver of deforestation. This article simulates the impact of road construction on deforestation in Western Amazonia, Peru, and quantifies the amount of carbon emissions associated with projected deforestation. To accomplish this objective, the article adopts a Bayesian probit land change model in which spatial dependencies are defined between regions or groups of pixels instead of between individual pixels, thereby reducing computational requirements. It also compares and contrasts the patterns of deforestation predicted by both spatial and non-spatial probit models. The spatial model replicates complex patterns of deforestation whereas the non-spatial model fails to do so. In terms of policy, both models suggest that road construction will increase deforestation by a modest amount, between 200–300 km2. This translates into aboveground carbon emissions of 1.36 and 1.85 x 106 tons. However, recent introduction of palm oil in the region serves as a cautionary example that the models may be underestimating the impact of roads. PMID:27010739

A Spatial Probit Econometric Model of Land Change: The Case of Infrastructure Development in Western Amazonia, Peru.

Directory of Open Access Journals (Sweden)

E Y Arima

Full Text Available Tropical forests are now at the center stage of climate mitigation policies worldwide given their roles as sources of carbon emissions resulting from deforestation and forest degradation. Although the international community has created mechanisms such as REDD+ to reduce those emissions, developing tropical countries continue to invest in infrastructure development in an effort to spur economic growth. Construction of roads in particular is known to be an important driver of deforestation. This article simulates the impact of road construction on deforestation in Western Amazonia, Peru, and quantifies the amount of carbon emissions associated with projected deforestation. To accomplish this objective, the article adopts a Bayesian probit land change model in which spatial dependencies are defined between regions or groups of pixels instead of between individual pixels, thereby reducing computational requirements. It also compares and contrasts the patterns of deforestation predicted by both spatial and non-spatial probit models. The spatial model replicates complex patterns of deforestation whereas the non-spatial model fails to do so. In terms of policy, both models suggest that road construction will increase deforestation by a modest amount, between 200-300 km2. This translates into aboveground carbon emissions of 1.36 and 1.85 x 106 tons. However, recent introduction of palm oil in the region serves as a cautionary example that the models may be underestimating the impact of roads.

A Spatial Probit Econometric Model of Land Change: The Case of Infrastructure Development in Western Amazonia, Peru.

Science.gov (United States)

Arima, E Y

2016-01-01

Tropical forests are now at the center stage of climate mitigation policies worldwide given their roles as sources of carbon emissions resulting from deforestation and forest degradation. Although the international community has created mechanisms such as REDD+ to reduce those emissions, developing tropical countries continue to invest in infrastructure development in an effort to spur economic growth. Construction of roads in particular is known to be an important driver of deforestation. This article simulates the impact of road construction on deforestation in Western Amazonia, Peru, and quantifies the amount of carbon emissions associated with projected deforestation. To accomplish this objective, the article adopts a Bayesian probit land change model in which spatial dependencies are defined between regions or groups of pixels instead of between individual pixels, thereby reducing computational requirements. It also compares and contrasts the patterns of deforestation predicted by both spatial and non-spatial probit models. The spatial model replicates complex patterns of deforestation whereas the non-spatial model fails to do so. In terms of policy, both models suggest that road construction will increase deforestation by a modest amount, between 200-300 km2. This translates into aboveground carbon emissions of 1.36 and 1.85 x 106 tons. However, recent introduction of palm oil in the region serves as a cautionary example that the models may be underestimating the impact of roads.

Extended probit mortality model for zooplankton against transient change of PCO(2).

Science.gov (United States)

Sato, Toru; Watanabe, Yuji; Toyota, Koji; Ishizaka, Joji

2005-09-01

The direct injection of CO(2) in the deep ocean is a promising way to mitigate global warming. One of the uncertainties in this method, however, is its impact on marine organisms in the near field. Since the concentration of CO(2), which organisms experience in the ocean, changes with time, it is required to develop a biological impact model for the organisms against the unsteady change of CO(2) concentration. In general, the LC(50) concept is widely applied for testing a toxic agent for the acute mortality. Here, we regard the probit-transformed mortality as a linear function not only of the concentration of CO(2) but also of exposure time. A simple mathematical transform of the function gives a damage-accumulation mortality model for zooplankton. In this article, this model was validated by the mortality test of Metamphiascopsis hirsutus against the transient change of CO(2) concentration.

Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models.

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Yue, Chen; Chen, Shaojie; Sair, Haris I; Airan, Raag; Caffo, Brian S

2015-09-01

Data reproducibility is a critical issue in all scientific experiments. In this manuscript, the problem of quantifying the reproducibility of graphical measurements is considered. The image intra-class correlation coefficient (I2C2) is generalized and the graphical intra-class correlation coefficient (GICC) is proposed for such purpose. The concept for GICC is based on multivariate probit-linear mixed effect models. A Markov Chain Monte Carlo EM (mcm-cEM) algorithm is used for estimating the GICC. Simulation results with varied settings are demonstrated and our method is applied to the KIRBY21 test-retest dataset.

Multinomial N-mixture models improve the applicability of electrofishing for developing population estimates of stream-dwelling Smallmouth Bass

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Mollenhauer, Robert; Brewer, Shannon K.

2017-01-01

Failure to account for variable detection across survey conditions constrains progressive stream ecology and can lead to erroneous stream fish management and conservation decisions. In addition to variable detection’s confounding long-term stream fish population trends, reliable abundance estimates across a wide range of survey conditions are fundamental to establishing species–environment relationships. Despite major advancements in accounting for variable detection when surveying animal populations, these approaches remain largely ignored by stream fish scientists, and CPUE remains the most common metric used by researchers and managers. One notable advancement for addressing the challenges of variable detection is the multinomial N-mixture model. Multinomial N-mixture models use a flexible hierarchical framework to model the detection process across sites as a function of covariates; they also accommodate common fisheries survey methods, such as removal and capture–recapture. Effective monitoring of stream-dwelling Smallmouth Bass Micropterus dolomieu populations has long been challenging; therefore, our objective was to examine the use of multinomial N-mixture models to improve the applicability of electrofishing for estimating absolute abundance. We sampled Smallmouth Bass populations by using tow-barge electrofishing across a range of environmental conditions in streams of the Ozark Highlands ecoregion. Using an information-theoretic approach, we identified effort, water clarity, wetted channel width, and water depth as covariates that were related to variable Smallmouth Bass electrofishing detection. Smallmouth Bass abundance estimates derived from our top model consistently agreed with baseline estimates obtained via snorkel surveys. Additionally, confidence intervals from the multinomial N-mixture models were consistently more precise than those of unbiased Petersen capture–recapture estimates due to the dependency among data sets in the

Multinomial Response Models, for Modeling and Determining Important Factors in Different Contraceptive Methods in Women

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E Haji Nejad

2001-06-01

Full Text Available Difference aspects of multinomial statistical modelings and its classifications has been studied so far. In these type of problems Y is the qualitative random variable with T possible states which are considered as classifications. The goal is prediction of Y based on a random Vector X ? IR^m. Many methods for analyzing these problems were considered. One of the modern and general method of classification is Classification and Regression Trees (CART. Another method is recursive partitioning techniques which has a strange relationship with nonparametric regression. Classical discriminant analysis is a standard method for analyzing these type of data. Flexible discriminant analysis method which is a combination of nonparametric regression and discriminant analysis and classification using spline that includes least square regression and additive cubic splines. Neural network is an advanced statistical method for analyzing these types of data. In this paper properties of multinomial logistics regression were investigated and this method was used for modeling effective factors in selecting contraceptive methods in Ghom province for married women age 15-49. The response variable has a tetranomial distibution. The levels of this variable are: nothing, pills, traditional and a collection of other contraceptive methods. A collection of significant independent variables were: place, age of women, education, history of pregnancy and family size. Menstruation age and age at marriage were not statistically significant.

Implicit moral evaluations: A multinomial modeling approach.

Science.gov (United States)

Cameron, C Daryl; Payne, B Keith; Sinnott-Armstrong, Walter; Scheffer, Julian A; Inzlicht, Michael

2017-01-01

Implicit moral evaluations-i.e., immediate, unintentional assessments of the wrongness of actions or persons-play a central role in supporting moral behavior in everyday life. Yet little research has employed methods that rigorously measure individual differences in implicit moral evaluations. In five experiments, we develop a new sequential priming measure-the Moral Categorization Task-and a multinomial model that decomposes judgment on this task into multiple component processes. These include implicit moral evaluations of moral transgression primes (Unintentional Judgment), accurate moral judgments about target actions (Intentional Judgment), and a directional tendency to judge actions as morally wrong (Response Bias). Speeded response deadlines reduced Intentional Judgment but not Unintentional Judgment (Experiment 1). Unintentional Judgment was stronger toward moral transgression primes than non-moral negative primes (Experiments 2-4). Intentional Judgment was associated with increased error-related negativity, a neurophysiological indicator of behavioral control (Experiment 4). Finally, people who voted for an anti-gay marriage amendment had stronger Unintentional Judgment toward gay marriage primes (Experiment 5). Across Experiments 1-4, implicit moral evaluations converged with moral personality: Unintentional Judgment about wrong primes, but not negative primes, was negatively associated with psychopathic tendencies and positively associated with moral identity and guilt proneness. Theoretical and practical applications of formal modeling for moral psychology are discussed. Copyright © 2016 Elsevier B.V. All rights reserved.

Fuzzy multinomial logistic regression analysis: A multi-objective programming approach

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Abdalla, Hesham A.; El-Sayed, Amany A.; Hamed, Ramadan

2017-05-01

Parameter estimation for multinomial logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets, Maximum Likelihood (ML) estimation is a satisfactory approach. Unfortunately, ML can fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. In this study, a new approach based on fuzzy concepts is proposed to estimate parameters of the multinomial logistic regression. The study assumes that the parameters of multinomial logistic regression are fuzzy. Based on the extension principle stated by Zadeh and Bárdossy's proposition, a multi-objective programming approach is suggested to estimate these fuzzy parameters. A simulation study is used to evaluate the performance of the new approach versus Maximum likelihood (ML) approach. Results show that the new proposed model outperforms ML in cases of small datasets.

Prospective memory after moderate-to-severe traumatic brain injury: a multinomial modeling approach.

Science.gov (United States)

Pavawalla, Shital P; Schmitter-Edgecombe, Maureen; Smith, Rebekah E

2012-01-01

Prospective memory (PM), which can be understood as the processes involved in realizing a delayed intention, is consistently found to be impaired after a traumatic brain injury (TBI). Although PM can be empirically dissociated from retrospective memory, it inherently involves both a prospective component (i.e., remembering that an action needs to be carried out) and retrospective components (i.e., remembering what action needs to be executed and when). This study utilized a multinomial processing tree model to disentangle the prospective (that) and retrospective recognition (when) components underlying PM after moderate-to-severe TBI. Seventeen participants with moderate to severe TBI and 17 age- and education-matched control participants completed an event-based PM task that was embedded within an ongoing computer-based color-matching task. The multinomial processing tree modeling approach revealed a significant group difference in the prospective component, indicating that the control participants allocated greater preparatory attentional resources to the PM task compared to the TBI participants. Participants in the TBI group were also found to be significantly more impaired than controls in the when aspect of the retrospective component. These findings indicated that the TBI participants had greater difficulty allocating the necessary preparatory attentional resources to the PM task and greater difficulty discriminating between PM targets and nontargets during task execution, despite demonstrating intact posttest recall and/or recognition of the PM tasks and targets.

PENERAPAN REGRESI PROBIT BIVARIAT UNTUK MENDUGA FAKTOR-FAKTOR YANG MEMENGARUHI KELULUSAN MAHASISWA (Studi Kasus: Mahasiswa Fakultas MIPA Unversitas Udayana

Directory of Open Access Journals (Sweden)

NI GUSTI KETUT TRISNA PRADNYANTARI

2015-06-01

Full Text Available The aim of this research to estimate the factors that affect students graduation using bivariate probit regression. Bivariate probit regression is a statistical method that involves two response variables which are qualitative and the independent variables are qualitative, quantitative, or a combination of both. In bivariate probit regression model, the result obtained is the probability of the response variable. The result of this research are the factors that affect significantly for students graduation based on study period are majors, sex, and duration of the thesis, while the factors that significantly for students graduation based on GPA are the entry system, duration of the thesis and the number of parents’ dependents.

The empathy impulse: A multinomial model of intentional and unintentional empathy for pain.

Science.gov (United States)

Cameron, C Daryl; Spring, Victoria L; Todd, Andrew R

2017-04-01

Empathy for pain is often described as automatic. Here, we used implicit measurement and multinomial modeling to formally quantify unintentional empathy for pain: empathy that occurs despite intentions to the contrary. We developed the pain identification task (PIT), a sequential priming task wherein participants judge the painfulness of target experiences while trying to avoid the influence of prime experiences. Using multinomial modeling, we distinguished 3 component processes underlying PIT performance: empathy toward target stimuli (Intentional Empathy), empathy toward prime stimuli (Unintentional Empathy), and bias to judge target stimuli as painful (Response Bias). In Experiment 1, imposing a fast (vs. slow) response deadline uniquely reduced Intentional Empathy. In Experiment 2, inducing imagine-self (vs. imagine-other) perspective-taking uniquely increased Unintentional Empathy. In Experiment 3, Intentional and Unintentional Empathy were stronger toward targets with typical (vs. atypical) pain outcomes, suggesting that outcome information matters and that effects on the PIT are not reducible to affective priming. Typicality of pain outcomes more weakly affected task performance when target stimuli were merely categorized rather than judged for painfulness, suggesting that effects on the latter are not reducible to semantic priming. In Experiment 4, Unintentional Empathy was stronger for participants who engaged in costly donation to cancer charities, but this parameter was also high for those who donated to an objectively worse but socially more popular charity, suggesting that overly high empathy may facilitate maladaptive altruism. Theoretical and practical applications of our modeling approach for understanding variation in empathy are discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Widen NomoGram for multinomial logistic regression: an application to staging liver fibrosis in chronic hepatitis C patients.

Science.gov (United States)

Ardoino, Ilaria; Lanzoni, Monica; Marano, Giuseppe; Boracchi, Patrizia; Sagrini, Elisabetta; Gianstefani, Alice; Piscaglia, Fabio; Biganzoli, Elia M

2017-04-01

The interpretation of regression models results can often benefit from the generation of nomograms, 'user friendly' graphical devices especially useful for assisting the decision-making processes. However, in the case of multinomial regression models, whenever categorical responses with more than two classes are involved, nomograms cannot be drawn in the conventional way. Such a difficulty in managing and interpreting the outcome could often result in a limitation of the use of multinomial regression in decision-making support. In the present paper, we illustrate the derivation of a non-conventional nomogram for multinomial regression models, intended to overcome this issue. Although it may appear less straightforward at first sight, the proposed methodology allows an easy interpretation of the results of multinomial regression models and makes them more accessible for clinicians and general practitioners too. Development of prediction model based on multinomial logistic regression and of the pertinent graphical tool is illustrated by means of an example involving the prediction of the extent of liver fibrosis in hepatitis C patients by routinely available markers.

Naive Bayesian classifiers for multinomial features: a theoretical analysis

CSIR Research Space (South Africa)

Van Dyk, E

2007-11-01

Full Text Available The authors investigate the use of naive Bayesian classifiers for multinomial feature spaces and derive error estimates for these classifiers. The error analysis is done by developing a mathematical model to estimate the probability density...

Application of Multinomial Probit Model in Analyzing Factors Affecting the Occupation of Graduated Students from the University of Agricultural Applied-Science

Directory of Open Access Journals (Sweden)

H. Mohammadi

2016-03-01

Full Text Available Introduction:Scientificand practicaltrainingwith an emphasis onoperation andapplication of what is taught and having an empiricalapproachto education isa more suitable approach for creating jobs. Preparation of educational needs of the agricultural sector by scientificand practicaltraining and providingemploymentin agreement with education and skills is one of the most important programs in order to achieve the objectives of comprehensive development of the country. An imbalance seems to exist between the processes and materials in university courses and the skills and abilities needed by the labor market and this is the most importantreason for the failureof the university graduatesin finding employment. This studyhas beendone for understandingthe type of jobof agricultural graduatesof training center of Jihad-e-Keshavarzi in Mashhad and the factor saffecting their employment. Materials and Methods: This study is an applied research and the statistical population is 167 and includes all the students who had earned a Bachelor’s degree who had come to receive their graduation certificates in 2011. The dependent variable is type of job which includes five categories of employment in the public sector related to education, employ men unrelated to the government, employment related tothe privatesector andthe unemployed who were seeking workin the private sector. Independent variables includegender,quotainuniversityadmissions, the level of interestin thefield of study,satisfaction withthe discipline, evaluationand trainingof graduatesofvocational skillsacquired incollegegraduates'assessmentof thework culturein the societyand evaluation oflack ofcapitalas a factor preventingemployment in the academicfield. Information was collected through questionnaires and the multiple probit mode lwas used. Results and discussion: The results ofthe survey showthatjobsof graduates are divided intofour categoriesincluding:Related to the field of study and

A probit- log- skew-normal mixture model for repeated measures data with excess zeros, with application to a cohort study of paediatric respiratory symptoms

Directory of Open Access Journals (Sweden)

Johnston Neil W

2010-06-01

Full Text Available Abstract Background A zero-inflated continuous outcome is characterized by occurrence of "excess" zeros that more than a single distribution can explain, with the positive observations forming a skewed distribution. Mixture models are employed for regression analysis of zero-inflated data. Moreover, for repeated measures zero-inflated data the clustering structure should also be modeled for an adequate analysis. Methods Diary of Asthma and Viral Infections Study (DAVIS was a one year (2004 cohort study conducted at McMaster University to monitor viral infection and respiratory symptoms in children aged 5-11 years with and without asthma. Respiratory symptoms were recorded daily using either an Internet or paper-based diary. Changes in symptoms were assessed by study staff and led to collection of nasal fluid specimens for virological testing. The study objectives included investigating the response of respiratory symptoms to respiratory viral infection in children with and without asthma over a one year period. Due to sparse data daily respiratory symptom scores were aggregated into weekly average scores. More than 70% of the weekly average scores were zero, with the positive scores forming a skewed distribution. We propose a random effects probit/log-skew-normal mixture model to analyze the DAVIS data. The model parameters were estimated using a maximum marginal likelihood approach. A simulation study was conducted to assess the performance of the proposed mixture model if the underlying distribution of the positive response is different from log-skew normal. Results Viral infection status was highly significant in both probit and log-skew normal model components respectively. The probability of being symptom free was much lower for the week a child was viral positive relative to the week she/he was viral negative. The severity of the symptoms was also greater for the week a child was viral positive. The probability of being symptom free was

The individual tolerance concept is not the sole explanation for the probit dose-effect model

Energy Technology Data Exchange (ETDEWEB)

Newman, M.C.; McCloskey, J.T.

2000-02-01

Predominant methods for analyzing dose- or concentration-effect data (i.e., probit analysis) are based on the concept of individual tolerance or individual effective dose (IED, the smallest characteristic dose needed to kill an individual). An alternative explanation (stochasticity hypothesis) is that individuals do not have unique tolerances: death results from stochastic processes occurring similarly in all individuals. These opposing hypotheses were tested with two types of experiments. First, time to stupefaction (TTS) was measured for zebra fish (Brachydanio rerio) exposed to benzocaine. The same 40 fish were exposed during five trials to test if the same order for TTS was maintained among trials. The IED hypothesis was supported with a minor stochastic component being present. Second, eastern mosquitofish (Gambusia holbrooki) were exposed to sublethal or lethal NaCl concentrations until a large portion of the lethally exposed fish died. After sufficient time for recovery, fish sublethally exposed and fish surviving lethal exposure were exposed simultaneously to lethal NaCl concentrations. No statistically significant effect was found of previous exposure on survival time but a large stochastic component to the survival dynamics was obvious. Repetition of this second type of test with pentachlorophenol also provided no support for the IED hypothesis. The authors conclude that neither hypothesis alone was the sole or dominant explanation for the lognormal (probit) model. Determination of the correct explanation (IED or stochastic) or the relative contributions of each is crucial to predicting consequences to populations after repeated or chronic exposures to any particular toxicant.

Probit vs. semi-nonparametric estimation: examining the role of disability on institutional entry for older adults.

Science.gov (United States)

Sharma, Andy

2017-06-01

The purpose of this study was to showcase an advanced methodological approach to model disability and institutional entry. Both of these are important areas to investigate given the on-going aging of the United States population. By 2020, approximately 15% of the population will be 65 years and older. Many of these older adults will experience disability and require formal care. A probit analysis was employed to determine which disabilities were associated with admission into an institution (i.e. long-term care). Since this framework imposes strong distributional assumptions, misspecification leads to inconsistent estimators. To overcome such a short-coming, this analysis extended the probit framework by employing an advanced semi-nonparamertic maximum likelihood estimation utilizing Hermite polynomial expansions. Specification tests show semi-nonparametric estimation is preferred over probit. In terms of the estimates, semi-nonparametric ratios equal 42 for cognitive difficulty, 64 for independent living, and 111 for self-care disability while probit yields much smaller estimates of 19, 30, and 44, respectively. Public health professionals can use these results to better understand why certain interventions have not shown promise. Equally important, healthcare workers can use this research to evaluate which type of treatment plans may delay institutionalization and improve the quality of life for older adults. Implications for rehabilitation With on-going global aging, understanding the association between disability and institutional entry is important in devising successful rehabilitation interventions. Semi-nonparametric is preferred to probit and shows ambulatory and cognitive impairments present high risk for institutional entry (long-term care). Informal caregiving and home-based care require further examination as forms of rehabilitation/therapy for certain types of disabilities.

Reasons for not buying a car : a probit-selection multinomial logit choice model

NARCIS (Netherlands)

Gao, Y.; Rasouli, S.; Timmermans, H.J.P.

2014-01-01

Generating and maintaining gradients of cell density and extracellular matrix (ECM) components is a prerequisite for the development of functionality of healthy tissue. Therefore, gaining insights into the drivers of spatial organization of cells and the role of ECM during tissue morphogenesis is

Multinomial model and zero-inflated gamma model to study time spent on leisure time physical activity: an example of ELSA-Brasil.

Science.gov (United States)

Nobre, Aline Araújo; Carvalho, Marilia Sá; Griep, Rosane Härter; Fonseca, Maria de Jesus Mendes da; Melo, Enirtes Caetano Prates; Santos, Itamar de Souza; Chor, Dora

2017-08-17

To compare two methodological approaches: the multinomial model and the zero-inflated gamma model, evaluating the factors associated with the practice and amount of time spent on leisure time physical activity. Data collected from 14,823 baseline participants in the Longitudinal Study of Adult Health (ELSA-Brasil - Estudo Longitudinal de Saúde do Adulto ) have been analysed. Regular leisure time physical activity has been measured using the leisure time physical activity module of the International Physical Activity Questionnaire. The explanatory variables considered were gender, age, education level, and annual per capita family income. The main advantage of the zero-inflated gamma model over the multinomial model is that it estimates mean time (minutes per week) spent on leisure time physical activity. For example, on average, men spent 28 minutes/week longer on leisure time physical activity than women did. The most sedentary groups were young women with low education level and income. The zero-inflated gamma model, which is rarely used in epidemiological studies, can give more appropriate answers in several situations. In our case, we have obtained important information on the main determinants of the duration of leisure time physical activity. This information can help guide efforts towards the most vulnerable groups since physical inactivity is associated with different diseases and even premature death.

Integration of Fishers' Perception on the Environment

African Journals Online (AJOL)

Keywords: Climate change, Green Economy, multinomial probit model, small- scale fisheries ... adoption of new preferences regarding environmental management and how these .... food security and poverty eradication in ... The challenges for SIDS are to integrate these ..... are thus facing unprecedented threats from.

The intermediate endpoint effect in logistic and probit regression

Science.gov (United States)

MacKinnon, DP; Lockwood, CM; Brown, CH; Wang, W; Hoffman, JM

2010-01-01

Background An intermediate endpoint is hypothesized to be in the middle of the causal sequence relating an independent variable to a dependent variable. The intermediate variable is also called a surrogate or mediating variable and the corresponding effect is called the mediated, surrogate endpoint, or intermediate endpoint effect. Clinical studies are often designed to change an intermediate or surrogate endpoint and through this intermediate change influence the ultimate endpoint. In many intermediate endpoint clinical studies the dependent variable is binary, and logistic or probit regression is used. Purpose The purpose of this study is to describe a limitation of a widely used approach to assessing intermediate endpoint effects and to propose an alternative method, based on products of coefficients, that yields more accurate results. Methods The intermediate endpoint model for a binary outcome is described for a true binary outcome and for a dichotomization of a latent continuous outcome. Plots of true values and a simulation study are used to evaluate the different methods. Results Distorted estimates of the intermediate endpoint effect and incorrect conclusions can result from the application of widely used methods to assess the intermediate endpoint effect. The same problem occurs for the proportion of an effect explained by an intermediate endpoint, which has been suggested as a useful measure for identifying intermediate endpoints. A solution to this problem is given based on the relationship between latent variable modeling and logistic or probit regression. Limitations More complicated intermediate variable models are not addressed in the study, although the methods described in the article can be extended to these more complicated models. Conclusions Researchers are encouraged to use an intermediate endpoint method based on the product of regression coefficients. A common method based on difference in coefficient methods can lead to distorted

An Empirical Analysis of Television Commercial Ratings in Alternative Competitive Environments Using Multinomial Logit Model

Directory of Open Access Journals (Sweden)

Dilek ALTAŞ

2013-05-01

Full Text Available Watching the commercials depends on the choice of the viewer. Most of the television viewing takes place during “Prime-Time” unfortunately; many viewers opt to zap to other channels when commercials start. The television viewers’ demographic characteristics may indicate the likelihood of the zapping frequency. Analysis made by using Multinomial Logit Model indicates how effective the demographic variables are in the watching rate of the first minute of the television commercials.

"The empathy impulse: A multinomial model of intentional and unintentional empathy for pain": Correction.

Science.gov (United States)

2018-04-01

Reports an error in "The empathy impulse: A multinomial model of intentional and unintentional empathy for pain" by C. Daryl Cameron, Victoria L. Spring and Andrew R. Todd ( Emotion , 2017[Apr], Vol 17[3], 395-411). In this article, there was an error in the calculation of some of the effect sizes. The w effect size was manually computed incorrectly. The incorrect number of total observations was used, which affected the final effect size estimates. This computing error does not change any of the results or interpretations about model fit based on the G² statistic, or about significant differences across conditions in process parameters. Therefore, it does not change any of the hypothesis tests or conclusions. The w statistics for overall model fit should be .02 instead of .04 in Study 1, .01 instead of .02 in Study 2, .01 instead of .03 for the OIT in Study 3 (model fit for the PIT remains the same: .00), and .02 instead of .03 in Study 4. The corrected tables can be seen here: http://osf.io/qebku at the Open Science Framework site for the article. (The following abstract of the original article appeared in record 2017-01641-001.) Empathy for pain is often described as automatic. Here, we used implicit measurement and multinomial modeling to formally quantify unintentional empathy for pain: empathy that occurs despite intentions to the contrary. We developed the pain identification task (PIT), a sequential priming task wherein participants judge the painfulness of target experiences while trying to avoid the influence of prime experiences. Using multinomial modeling, we distinguished 3 component processes underlying PIT performance: empathy toward target stimuli (Intentional Empathy), empathy toward prime stimuli (Unintentional Empathy), and bias to judge target stimuli as painful (Response Bias). In Experiment 1, imposing a fast (vs. slow) response deadline uniquely reduced Intentional Empathy. In Experiment 2, inducing imagine-self (vs. imagine

Multinomial model and zero-inflated gamma model to study time spent on leisure time physical activity: an example of ELSA-Brasil

Directory of Open Access Journals (Sweden)

Aline Araújo Nobre

2017-08-01

Full Text Available ABSTRACT OBJECTIVE To compare two methodological approaches: the multinomial model and the zero-inflated gamma model, evaluating the factors associated with the practice and amount of time spent on leisure time physical activity. METHODS Data collected from 14,823 baseline participants in the Longitudinal Study of Adult Health (ELSA-Brasil – Estudo Longitudinal de Saúde do Adulto have been analysed. Regular leisure time physical activity has been measured using the leisure time physical activity module of the International Physical Activity Questionnaire. The explanatory variables considered were gender, age, education level, and annual per capita family income. RESULTS The main advantage of the zero-inflated gamma model over the multinomial model is that it estimates mean time (minutes per week spent on leisure time physical activity. For example, on average, men spent 28 minutes/week longer on leisure time physical activity than women did. The most sedentary groups were young women with low education level and income CONCLUSIONS The zero-inflated gamma model, which is rarely used in epidemiological studies, can give more appropriate answers in several situations. In our case, we have obtained important information on the main determinants of the duration of leisure time physical activity. This information can help guide efforts towards the most vulnerable groups since physical inactivity is associated with different diseases and even premature death.

The Finite and Moving Order Multinomial Universal Portfolio

International Nuclear Information System (INIS)

Tan, Choon Peng; Pang, Sook Theng

2013-01-01

An upper bound for the ratio of wealths of the best constant -rebalanced portfolio to that of the multinomial universal portfolio is derived. The finite- order multinomial universal portfolios can reduce the implementation time and computer-memory requirements for computation. The improved performance of the finite-order portfolios on some selected local stock-price data sets is observed.

Modeling the dynamics of urban growth using multinomial logistic regression: a case study of Jiayu County, Hubei Province, China

Science.gov (United States)

Nong, Yu; Du, Qingyun; Wang, Kun; Miao, Lei; Zhang, Weiwei

2008-10-01

Urban growth modeling, one of the most important aspects of land use and land cover change study, has attracted substantial attention because it helps to comprehend the mechanisms of land use change thus helps relevant policies made. This study applied multinomial logistic regression to model urban growth in the Jiayu county of Hubei province, China to discover the relationship between urban growth and the driving forces of which biophysical and social-economic factors are selected as independent variables. This type of regression is similar to binary logistic regression, but it is more general because the dependent variable is not restricted to two categories, as those previous studies did. The multinomial one can simulate the process of multiple land use competition between urban land, bare land, cultivated land and orchard land. Taking the land use type of Urban as reference category, parameters could be estimated with odds ratio. A probability map is generated from the model to predict where urban growth will occur as a result of the computation.

Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

Energy Technology Data Exchange (ETDEWEB)

Ghazali, Amirul Syafiq Mohd; Ali, Zalila; Noor, Norlida Mohd; Baharum, Adam [Pusat Pengajian Sains Matematik, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia amirul@unisel.edu.my, zalila@cs.usm.my, norlida@usm.my, adam@usm.my (Malaysia)

2015-10-22

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test of the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. The results indicated that obesity and overweight of students are related to gender, religion, sleep duration, time spent on electronic games, breakfast intake in a week, with whom meals are taken, protein intake, and also, the interaction between breakfast intake in a week with sleep duration, and the interaction between gender and protein intake.

Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

International Nuclear Information System (INIS)

Ghazali, Amirul Syafiq Mohd; Ali, Zalila; Noor, Norlida Mohd; Baharum, Adam

2015-01-01

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test of the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. The results indicated that obesity and overweight of students are related to gender, religion, sleep duration, time spent on electronic games, breakfast intake in a week, with whom meals are taken, protein intake, and also, the interaction between breakfast intake in a week with sleep duration, and the interaction between gender and protein intake

Performance and separation occurrence of binary probit regression estimator using maximum likelihood method and Firths approach under different sample size

Science.gov (United States)

Lusiana, Evellin Dewi

2017-12-01

The parameters of binary probit regression model are commonly estimated by using Maximum Likelihood Estimation (MLE) method. However, MLE method has limitation if the binary data contains separation. Separation is the condition where there are one or several independent variables that exactly grouped the categories in binary response. It will result the estimators of MLE method become non-convergent, so that they cannot be used in modeling. One of the effort to resolve the separation is using Firths approach instead. This research has two aims. First, to identify the chance of separation occurrence in binary probit regression model between MLE method and Firths approach. Second, to compare the performance of binary probit regression model estimator that obtained by MLE method and Firths approach using RMSE criteria. Those are performed using simulation method and under different sample size. The results showed that the chance of separation occurrence in MLE method for small sample size is higher than Firths approach. On the other hand, for larger sample size, the probability decreased and relatively identic between MLE method and Firths approach. Meanwhile, Firths estimators have smaller RMSE than MLEs especially for smaller sample sizes. But for larger sample sizes, the RMSEs are not much different. It means that Firths estimators outperformed MLE estimator.

Model-based Clustering of Categorical Time Series with Multinomial Logit Classification

Science.gov (United States)

Frühwirth-Schnatter, Sylvia; Pamminger, Christoph; Winter-Ebmer, Rudolf; Weber, Andrea

2010-09-01

A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter [6], is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This model-based clustering method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to further explain group membership we present an extension to the approach of Pamminger and Frühwirth-Schnatter [6] by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule by using a multinomial logit model. The parameters are estimated for a fixed number of clusters within a Bayesian framework using an Markov chain Monte Carlo (MCMC) sampling scheme representing a (full) Gibbs-type sampler which involves only draws from standard distributions. Finally, an application to a panel of Austrian wage mobility data is presented which leads to an interesting segmentation of the Austrian labour market.

Multinomial logistic regression in workers' health

Science.gov (United States)

Grilo, Luís M.; Grilo, Helena L.; Gonçalves, Sónia P.; Junça, Ana

2017-11-01

In European countries, namely in Portugal, it is common to hear some people mentioning that they are exposed to excessive and continuous psychosocial stressors at work. This is increasing in diverse activity sectors, such as, the Services sector. A representative sample was collected from a Portuguese Services' organization, by applying a survey (internationally validated), which variables were measured in five ordered categories in Likert-type scale. A multinomial logistic regression model is used to estimate the probability of each category of the dependent variable general health perception where, among other independent variables, burnout appear as statistically significant.

Predicting longitudinal trajectories of health probabilities with random-effects multinomial logit regression.

Science.gov (United States)

Liu, Xian; Engel, Charles C

2012-12-20

Researchers often encounter longitudinal health data characterized with three or more ordinal or nominal categories. Random-effects multinomial logit models are generally applied to account for potential lack of independence inherent in such clustered data. When parameter estimates are used to describe longitudinal processes, however, random effects, both between and within individuals, need to be retransformed for correctly predicting outcome probabilities. This study attempts to go beyond existing work by developing a retransformation method that derives longitudinal growth trajectories of unbiased health probabilities. We estimated variances of the predicted probabilities by using the delta method. Additionally, we transformed the covariates' regression coefficients on the multinomial logit function, not substantively meaningful, to the conditional effects on the predicted probabilities. The empirical illustration uses the longitudinal data from the Asset and Health Dynamics among the Oldest Old. Our analysis compared three sets of the predicted probabilities of three health states at six time points, obtained from, respectively, the retransformation method, the best linear unbiased prediction, and the fixed-effects approach. The results demonstrate that neglect of retransforming random errors in the random-effects multinomial logit model results in severely biased longitudinal trajectories of health probabilities as well as overestimated effects of covariates on the probabilities. Copyright © 2012 John Wiley & Sons, Ltd.

Default probabilities, CDS premiums and downgrades : A probit-MIDAS analysis

NARCIS (Netherlands)

Freitag, L.

2014-01-01

This paper examines the relationship between sovereign credit default swaps (CDS) and sovereign rating changes of European countries. To this aim, a new estimator is introduced which merges mixed data sampling (MIDAS) with probit regression. Simulations show that the estimator has good properties in

Modeling vehicle operating speed on urban roads in Montreal: a panel mixed ordered probit fractional split model.

Science.gov (United States)

Eluru, Naveen; Chakour, Vincent; Chamberlain, Morgan; Miranda-Moreno, Luis F

2013-10-01

that the proposed panel mixed ordered probit fractional split model offers promise for modeling such proportional ordinal variables. Copyright © 2013 Elsevier Ltd. All rights reserved.

Effectiveness of enforcement levels of speed limit and drink driving laws and associated factors – Exploratory empirical analysis using a bivariate ordered probit model

Directory of Open Access Journals (Sweden)

Behram Wali

2017-06-01

Full Text Available The contemporary traffic safety research comprises little information on quantifying the simultaneous association between drink driving and speeding among fatally injured drivers. Potential correlation between driver's drink driving and speeding behavior poses a substantial methodological concern which needs investigation. This study therefore focused on investigating the simultaneous impact of socioeconomic factors, fatalities, vehicle ownership, health services and highway agency road safety policies on enforcement levels of speed limit and drink driving laws. The effectiveness of enforcement levels of speed limit and drink driving laws has been investigated through development of bivariate ordered probit model using data extricated from WHO's global status report on road safety in 2013. The consistent and intuitive parameter estimates along with statistically significant correlation between response outcomes validates the statistical supremacy of bivariate ordered probit model. The results revealed that fatalities per thousand registered vehicles, hospital beds per hundred thousand population and road safety policies are associated with a likely medium or high effectiveness of enforcement levels of speed limit and drink driving laws, respectively. Also, the model encapsulates the effect of several other agency related variables and socio-economic status on the response outcomes. Marginal effects are reported for analyzing the impact of such factors on intermediate categories of response outcomes. The results of this study are expected to provide necessary insights to elemental enforcement programs. Also, marginal effects of explanatory variables may provide useful directions for formulating effective policy countermeasures for overcoming driver's speeding and drink driving behavior.

A multinomial-logit ordered-probit model for jointly analyzing crash avoidance maneuvers and crash severity

DEFF Research Database (Denmark)

Kaplan, Sigal; Prato, Carlo Giacomo

' propensity to engage in various corrective maneuvers in the case of the critical event of vehicle travelling. Five lateral and speed control maneuvers are considered: “braking”, “steering”, “braking & steering”, and “other maneuvers”, in addition to a “no action” option. The analyzed data are retrieved from...... the United States National Automotive Sampling System General Estimates System (GES) crash database for the years 2005-2009. Results show (i) the correlation between crash avoidance maneuvers and crash severity, and (ii) the link between drivers' attributes, risky driving behavior, road characteristics...

Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?

Science.gov (United States)

Lin, Yingzhi; Deng, Xiangzheng; Li, Xing; Ma, Enjun

2014-12-01

Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.

Uncovering a latent multinomial: Analysis of mark-recapture data with misidentification

Science.gov (United States)

Link, W.A.; Yoshizaki, J.; Bailey, L.L.; Pollock, K.H.

2010-01-01

Natural tags based on DNA fingerprints or natural features of animals are now becoming very widely used in wildlife population biology. However, classic capture-recapture models do not allow for misidentification of animals which is a potentially very serious problem with natural tags. Statistical analysis of misidentification processes is extremely difficult using traditional likelihood methods but is easily handled using Bayesian methods. We present a general framework for Bayesian analysis of categorical data arising from a latent multinomial distribution. Although our work is motivated by a specific model for misidentification in closed population capture-recapture analyses, with crucial assumptions which may not always be appropriate, the methods we develop extend naturally to a variety of other models with similar structure. Suppose that observed frequencies f are a known linear transformation f = A???x of a latent multinomial variable x with cell probability vector ?? = ??(??). Given that full conditional distributions [?? | x] can be sampled, implementation of Gibbs sampling requires only that we can sample from the full conditional distribution [x | f, ??], which is made possible by knowledge of the null space of A???. We illustrate the approach using two data sets with individual misidentification, one simulated, the other summarizing recapture data for salamanders based on natural marks. ?? 2009, The International Biometric Society.

Using multinomial and imprecise probability for non-parametric modelling of rainfall in Manizales (Colombia

Directory of Open Access Journals (Sweden)

Ibsen Chivatá Cárdenas

2008-05-01

Full Text Available This article presents a rainfall model constructed by applying non-parametric modelling and imprecise probabilities; these tools were used because there was not enough homogeneous information in the study area. The area’s hydro-logical information regarding rainfall was scarce and existing hydrological time series were not uniform. A distributed extended rainfall model was constructed from so-called probability boxes (p-boxes, multinomial probability distribu-tion and confidence intervals (a friendly algorithm was constructed for non-parametric modelling by combining the last two tools. This model confirmed the high level of uncertainty involved in local rainfall modelling. Uncertainty en-compassed the whole range (domain of probability values thereby showing the severe limitations on information, leading to the conclusion that a detailed estimation of probability would lead to significant error. Nevertheless, rele-vant information was extracted; it was estimated that maximum daily rainfall threshold (70 mm would be surpassed at least once every three years and the magnitude of uncertainty affecting hydrological parameter estimation. This paper’s conclusions may be of interest to non-parametric modellers and decisions-makers as such modelling and imprecise probability represents an alternative for hydrological variable assessment and maybe an obligatory proce-dure in the future. Its potential lies in treating scarce information and represents a robust modelling strategy for non-seasonal stochastic modelling conditions

Multinomial Bayesian learning for modeling classical and nonclassical receptive field properties.

Science.gov (United States)

Hosoya, Haruo

2012-08-01

We study the interplay of Bayesian inference and natural image learning in a hierarchical vision system, in relation to the response properties of early visual cortex. We particularly focus on a Bayesian network with multinomial variables that can represent discrete feature spaces similar to hypercolumns combining minicolumns, enforce sparsity of activation to learn efficient representations, and explain divisive normalization. We demonstrate that maximal-likelihood learning using sampling-based Bayesian inference gives rise to classical receptive field properties similar to V1 simple cells and V2 cells, while inference performed on the trained network yields nonclassical context-dependent response properties such as cross-orientation suppression and filling in. Comparison with known physiological properties reveals some qualitative and quantitative similarities.

Classification of Effective Soil Depth by Using Multinomial Logistic Regression Analysis

Science.gov (United States)

Chang, C. H.; Chan, H. C.; Chen, B. A.

2016-12-01

Classification of effective soil depth is a task of determining the slopeland utilizable limitation in Taiwan. The "Slopeland Conservation and Utilization Act" categorizes the slopeland into agriculture and husbandry land, land suitable for forestry and land for enhanced conservation according to the factors including average slope, effective soil depth, soil erosion and parental rock. However, sit investigation of the effective soil depth requires a cost-effective field work. This research aimed to classify the effective soil depth by using multinomial logistic regression with the environmental factors. The Wen-Shui Watershed located at the central Taiwan was selected as the study areas. The analysis of multinomial logistic regression is performed by the assistance of a Geographic Information Systems (GIS). The effective soil depth was categorized into four levels including deeper, deep, shallow and shallower. The environmental factors of slope, aspect, digital elevation model (DEM), curvature and normalized difference vegetation index (NDVI) were selected for classifying the soil depth. An Error Matrix was then used to assess the model accuracy. The results showed an overall accuracy of 75%. At the end, a map of effective soil depth was produced to help planners and decision makers in determining the slopeland utilizable limitation in the study areas.

Multinomial logistic models explaining income changes of migrants to high-amenity counties.

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Von Reichert, C; Rudzitis, G

1992-01-01

"A survey of residents of and migrants to 15 fast-growing wilderness counties [in the United States] showed that only 25 percent of the migrants increased their income, while almost 50 percent accepted income losses upon their moves to high-amenity counties. Concomitantly, amenities and quality of life were more important factors in the migration decision than was employment, for instance. We focused on migrants in the labor force and employed multinomial logistic regression to identify the impact of migrants' characteristics, their satisfaction/dissatisfaction with previous location (push), and the importance of destination features (pull) on income change." excerpt

Ordering the Preference Hierarchies for Internal Finance, Bank Loans, Bond and Share Issues

OpenAIRE

Leo de Haan; Jeroen Hinloopen

2002-01-01

We estimate the incremental financing decision for a sample of some 150Dutch companies for the years 1984 through 1997, thereby distinguishinginternal finance and three types of external finance: bank borrowing, bondissues and share issues. First, we estimate a multinomial logit model whichconfirms several predictions of both the static trade-off theory and thepecking-order theory as to the determinants of financing choices. Next, weuse ordered probit models to determine which financing hiera...

An improved probit method for assessment of domino effect to chemical process equipment caused by overpressure.

Science.gov (United States)

Mingguang, Zhang; Juncheng, Jiang

2008-10-30

Overpressure is one important cause of domino effect in accidents of chemical process equipments. Damage probability and relative threshold value are two necessary parameters in QRA of this phenomenon. Some simple models had been proposed based on scarce data or oversimplified assumption. Hence, more data about damage to chemical process equipments were gathered and analyzed, a quantitative relationship between damage probability and damage degrees of equipment was built, and reliable probit models were developed associated to specific category of chemical process equipments. Finally, the improvements of present models were evidenced through comparison with other models in literatures, taking into account such parameters: consistency between models and data, depth of quantitativeness in QRA.

Estimation from incomplete multinomial data. Ph.D. Thesis - Harvard Univ.

Science.gov (United States)

Credeur, K. R.

1978-01-01

The vector of multinomial cell probabilities was estimated from incomplete data, incomplete in that it contains partially classified observations. Each such partially classified observation was observed to fall in one of two or more selected categories but was not classified further into a single category. The data were assumed to be incomplete at random. The estimation criterion was minimization of risk for quadratic loss. The estimators were the classical maximum likelihood estimate, the Bayesian posterior mode, and the posterior mean. An approximation was developed for the posterior mean. The Dirichlet, the conjugate prior for the multinomial distribution, was assumed for the prior distribution.

How Much Math Do Students Need to Succeed in Business and Economics Statistics? An Ordered Probit Analysis

OpenAIRE

Jeffrey J. Green; Courtenay C. Stone; Abera Zegeye; Thomas A. Charles

2008-01-01

Because statistical analysis requires both familiarity with and the ability to use mathematics, students typically are required to take one or more prerequisite math courses prior to enrolling in the business statistics course. Despite these math prerequisites, however, students find it extremely difficult to learn business statistics. In this study, we use an ordered probit model to examine the effect of alternative prerequisite math course sequences on the grade performance of 1,684 busines...

Modelling the vicious circle between obesity and physical activity in children and adolescents using a bivariate probit model with endogenous regressors.

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Yeh, C-Y; Chen, L-J; Ku, P-W; Chen, C-M

2015-01-01

The increasing prevalence of obesity in children and adolescents has become one of the most important public health issues around the world. Lack of physical activity is a risk factor for obesity, while being obese could reduce the likelihood of participating in physical activity. Failing to account for the endogeneity between obesity and physical activity would result in biased estimation. This study investigates the relationship between overweight and physical activity by taking endogeneity into consideration. It develops an endogenous bivariate probit model estimated by the maximum likelihood method. The data included 4008 boys and 4197 girls in the 5th-9th grades in Taiwan in 2007-2008. The relationship between overweight and physical activity is significantly negative in the endogenous model, but insignificant in the comparative exogenous model. This endogenous relationship presents a vicious circle in which lower levels of physical activity lead to overweight, while those who are already overweight engage in less physical activity. The results not only reveal the importance of endogenous treatment, but also demonstrate the robust negative relationship between these two factors. An emphasis should be put on overweight and obese children and adolescents in order to break the vicious circle. Promotion of physical activity by appropriate counselling programmes and peer support could be effective in reducing the prevalence of obesity in children and adolescents.

Assessing characteristics related to the use of seatbelts and cell phones by drivers: application of a bivariate probit model.

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Russo, Brendan J; Kay, Jonathan J; Savolainen, Peter T; Gates, Timothy J

2014-06-01

The effects of cell phone use and safety belt use have been an important focus of research related to driver safety. Cell phone use has been shown to be a significant source of driver distraction contributing to substantial degradations in driver performance, while safety belts have been demonstrated to play a vital role in mitigating injuries to crash-involved occupants. This study examines the prevalence of cell phone use and safety belt non-use among the driving population through direct observation surveys. A bivariate probit model is developed to simultaneously examine the factors that affect cell phone and safety belt use among motor vehicle drivers. The results show that several factors may influence drivers' decision to use cell phones and safety belts, and that these decisions are correlated. Understanding the factors that affect both cell phone use and safety belt non-use is essential to targeting policy and programs that reduce such behavior. Copyright © 2014 Elsevier Ltd. All rights reserved.

Exploring Driver Injury Severity at Intersection: An Ordered Probit Analysis

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Yaping Zhang

2015-02-01

Full Text Available It is well known that intersections are the most hazardous locations; however, only little is known about driver injury severity in intersection crashes. Hence, the main goal of this study was to further examine the different factors contributing to driver injury severity involved in fatal crashes at intersections. Data used for the present analysis was from the US DOT-Fatality Analysis Reporting System (FARS crash database from the year 2011. An ordered probit model was employed to fit the fatal crash data and analyze the factors impacting each injury severity level. The analysis results displayed that driver injury severity is significantly affected by many factors. They include driver age and gender, driver ethnicity, vehicle type and age (years of use, crash type, driving drunk, speeding, violating stop sign, cognitively distracted driving, and seat belt usage. These findings from the current study are beneficial to form a solid basis for adopting corresponding measures to effectively drop injury severity suffering from intersection crash. More insights into the effects of risk factors on driver injury severity could be acquired using more advanced statistical models.

An Instrumental Variable Probit (IVP Analysis on Depressed Mood in Korea: The Impact of Gender Differences and Other Socio-Economic Factors

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Lara Gitto

2015-08-01

-economic factors (such as education, residence in metropolitan areas, and so on. As the results of the Wald test carried out after the estimations did not allow to reject the null hypothesis of endogeneity, a probit model was run too. Results Overall, women tend to develop depression more frequently than men. There is an inverse effect of education on depressed mood (probability of -24.6% to report a depressed mood due to high school education, as it emerges from the probit model marginal effects, while marital status and the number of family members may act as protective factors (probability to report a depressed mood of -1.0% for each family member. Depression is significantly associated with socio-economic conditions, such as work and income. Living in metropolitan areas is inversely correlated with depression (probability of -4.1% to report a depressed mood estimated through the probit model: this could be explained considering that, in rural areas, people rarely have immediate access to high-quality health services. Conclusion This study outlines the factors that are more likely to impact on depression, and applies an IVP model to take into account the potential endogeneity of some of the predictors of depressive mood, such as female participation to workforce and health status. A probit model has been estimated too. Depression is associated with a wide range of socioeconomic factors, although the strength and direction of the association can differ by gender. Prevention approaches to contrast depressive symptoms might take into consideration the evidence offered by the present study.

An Instrumental Variable Probit (IVP) analysis on depressed mood in Korea: the impact of gender differences and other socio-economic factors.

Science.gov (United States)

Gitto, Lara; Noh, Yong-Hwan; Andrés, Antonio Rodríguez

2015-04-16

, residence in metropolitan areas, and so on). As the results of the Wald test carried out after the estimations did not allow to reject the null hypothesis of endogeneity, a probit model was run too. Overall, women tend to develop depression more frequently than men. There is an inverse effect of education on depressed mood (probability of -24.6% to report a depressed mood due to high school education, as it emerges from the probit model marginal effects), while marital status and the number of family members may act as protective factors (probability to report a depressed mood of -1.0% for each family member). Depression is significantly associated with socio-economic conditions, such as work and income. Living in metropolitan areas is inversely correlated with depression (probability of -4.1% to report a depressed mood estimated through the probit model): this could be explained considering that, in rural areas, people rarely have immediate access to high-quality health services. This study outlines the factors that are more likely to impact on depression, and applies an IVP model to take into account the potential endogeneity of some of the predictors of depressive mood, such as female participation to workforce and health status. A probit model has been estimated too. Depression is associated with a wide range of socio-economic factors, although the strength and direction of the association can differ by gender. Prevention approaches to contrast depressive symptoms might take into consideration the evidence offered by the present study. © 2015 by Kerman University of Medical Sciences.

Recreation Value of Water to Wetlands in the San Joaquin Valley: Linked Multinomial Logit and Count Data Trip Frequency Models

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Creel, Michael; Loomis, John

1992-10-01

The recreational benefits from providing increased quantities of water to wildlife and fisheries habitats is estimated using linked multinomial logit site selection models and count data trip frequency models. The study encompasses waterfowl hunting, fishing and wildlife viewing at 14 recreational resources in the San Joaquin Valley, including the National Wildlife Refuges, the State Wildlife Management Areas, and six river destinations. The economic benefits of increasing water supplies to wildlife refuges were also examined by using the estimated models to predict changing patterns of site selection and overall participation due to increases in water allocations. Estimates of the dollar value per acre foot of water are calculated for increases in water to refuges. The resulting model is a flexible and useful tool for estimating the economic benefits of alternative water allocation policies for wildlife habitat and rivers.

Health Care Facility Choice and User Fee Abolition: Regression Discontinuity in a Multinomial Choice Setting

OpenAIRE

Steven F. Koch; Jeffrey S. Racine

2013-01-01

We apply parametric and nonparametric regression discontinuity methodology within a multinomial choice setting to examine the impact of public health care user fee abolition on health facility choice using data from South Africa. The nonparametric model is found to outperform the parametric model both in- and out-of-sample, while also delivering more plausible estimates of the impact of user fee abolition (i.e. the 'treatment effect'). In the parametric framework, treatment effects were relat...

Appropriateness of Probit-9 in development of quarantine treatments for timber and timber commodities

Science.gov (United States)

Marcus Schortemeyer; Ken Thomas; Robert A. Haack; Adnan Uzunovic; Kelli Hoover; Jack A. Simpson; Cheryl A. Grgurinovic

2011-01-01

Following the increasing international phasing out of methyl bromide for quarantine purposes, the development of alternative treatments for timber pests becomes imperative. The international accreditation of new quarantine treatments requires verification standards that give confidence in the effectiveness of a treatment. Probit-9 mortality is a standard for treatment...

The differentiated impacts of organizational innovation practices on technological innovation persistence

OpenAIRE

Le Bas , Christian; Mothe , Caroline; Nguyen-Thi , Thuc Uyen

2015-01-01

International audience; Purpose – The purpose of this paper is to test the major determinants of technological (product and process) innovation persistence and provides evidence of the significant role of organizational innovation. Design/methodology/approach – Data came from two waves of the Luxembourg Community Innovation Survey (CIS): CIS2006 for 2004-2006 and CIS2008 for 2006-2008. The longitudinal data set resulted in a final sample of 287 firms. A multinomial probit model estimates the ...

Vocational and General Education of Girls and Boys in Tunisia: The Effects of Income and Parental Education

OpenAIRE

Mohamed Siala; Nehed Ben Ammar

2014-01-01

Throughout Tunisia, basic education is compulsory. Children are required to enroll for at least 9 years from age 6. This paper examines gender differences in education choice of upper basic education of youths aged 15–24 in Tunisia. To investigate the factors that influence an individual’s choice between vocational education, general education (secondary and high education) and leaving school, the paper estimates a multinomial probit model of education choice. We focus on the i...

Unter welchen Umständen würden deutsche Landwirte gentechnisch veränderten Raps anbauen? Ein Discrete-Choice-Experiment

OpenAIRE

Breustedt, Gunnar; Muller-Scheessel, Jorg; Meyer-Schatz, Henrika Marie

2007-01-01

We examine the factors affecting the willingness of German farmers to adopt genetically modified (GM) oilseed rape after the pending commercial release of GM varieties. The analysis is based mainly on a web-based Discrete Choice Experiment with 217 oilseed rape growers in Germany. The determinants of adoption were estimated with the use of a multinomial probit model. Results indicate a significant impact of economic determinants on the adoption willingness: the difference in gross margins bet...

Exploring the Factors that Impact on Transit Use through an Ordered Probit Model: the Case of Metro of Madrid

Energy Technology Data Exchange (ETDEWEB)

Eboli, L.; Forciniti, C.; Mazzulla, G.; Calvo, F.

2016-07-01

The configuration of urban areas is the result of a cyclic relationship between land use and transportation system: the changes in transportation system arrangements influence the localisation of residence and economic activities, as well as the changes in land use affect transportation system characteristics. In this context, by operating on land use, travel demand can be shift from the individual transportation modes to transit systems. In the literature, many conceptual models were proposed to describe the complex relationship between land use and travel behaviour. In addition to spatial variation, the study of travel demand shows the categorical variation of variables. This work aims to analyse the influence of the categorical variation of variables impacting on transit use. An ordered probit model is proposed for evaluating how transit use depends on variables related to socio-economic characteristics of population, territorial features, accessibility, and transportation system. The study case is Madrid metro network (Spain). The results show a strong influence of characteristics of population and land use variables on daily trips made using metro system and highlighted the aspects that mainly impact on the choice to travel by metro, providing useful suggestions for shifting people from individual transportation mode to transit systems. (Author)

Measuring public understanding on Tenaga Nasional Berhad (TNB) electricity bills using ordered probit model

Science.gov (United States)

Zainudin, WNRA; Ramli, NA

2017-09-01

In 2016, Tenaga Nasional Berhad (TNB) had introduced an upgrade in its Billing and Customer Relationship Management (BCRM) as part of its long-term initiative to provide its customers with greater access to billing information. This includes information on real and suggested power consumption by the customers and further details in their billing charges. This information is useful to help TNB customers to gain better understanding on their electricity usage patterns and items involved in their billing charges. Up to date, there are not many studies done to measure public understanding on current electricity bills and whether this understanding could contribute towards positive impacts. The purpose of this paper is to measure public understanding on current TNB electricity bills and whether their satisfaction towards energy-related services, electricity utility services, and their awareness on the amount of electricity consumed by various appliances and equipment in their home could improve this understanding on the electricity bills. Both qualitative and quantitative research methods are used to achieve these objectives. A total of 160 respondents from local universities in Malaysia participated in a survey used to collect relevant information. Using Ordered Probit model, this paper finds respondents that are highly satisfied with the electricity utility services tend to understand their electricity bills better. The electric utility services include management of electricity bills and the information obtained from utility or non-utility supplier to help consumers manage their energy usage or bills. Based on the results, this paper concludes that the probability to understand the components in the monthly electricity bill increases as respondents are more satisfied with their electric utility services and are more capable to value the energy-related services.

Recommender system based on scarce information mining.

Science.gov (United States)

Lu, Wei; Chung, Fu-Lai; Lai, Kunfeng; Zhang, Liang

2017-09-01

Guessing what user may like is now a typical interface for video recommendation. Nowadays, the highly popular user generated content sites provide various sources of information such as tags for recommendation tasks. Motivated by a real world online video recommendation problem, this work targets at the long tail phenomena of user behavior and the sparsity of item features. A personalized compound recommendation framework for online video recommendation called Dirichlet mixture probit model for information scarcity (DPIS) is hence proposed. Assuming that each clicking sample is generated from a representation of user preferences, DPIS models the sample level topic proportions as a multinomial item vector, and utilizes topical clustering on the user part for recommendation through a probit classifier. As demonstrated by the real-world application, the proposed DPIS achieves better performance in accuracy, perplexity as well as diversity in coverage than traditional methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predictive occurrence models for coastal wetland plant communities: Delineating hydrologic response surfaces with multinomial logistic regression

Science.gov (United States)

Snedden, Gregg A.; Steyer, Gregory D.

2013-02-01

Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007-Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.

Measuring public acceptance on renewable energy (RE) development in Malaysia using ordered probit model

Science.gov (United States)

Zainudin, W. N. R. A.; Ishak, W. W. M.

2017-09-01

In 2009, government of Malaysia has announced a National Renewable Energy Policy and Action Plan as part of their commitment to accelerate the growth in renewable energies (RE). However, an adoption of RE as a main source of energy is still at an early stage due to lack of public awareness and acceptance on RE. Up to date, there are insufficient studies done on the reasons behind this lack of awareness and acceptance. Therefore, this paper is interested to investigate the public acceptance towards development of RE by measuring their willingness to pay slightly more for energy generated from RE sources, denote as willingness level and whether the importance for the electricity to be supplied at absolute lowest possible cost regardless of source and environmental impact, denote as importance level and other socio-economic factors could improve their willingness level. Both qualitative and quantitative research methods are used to achieve the research objectives. A total of 164 respondents from local universities in Malaysia participated in a survey to collect this relevant information. Using Ordered Probit model, the study shows that among the relevant socio-economic factors, age seems to be an important factor to influence the willingness level of the respondents. This paper concludes that younger generation are more willing to pay slightly more for energy generated from RE sources as compared to older generation. One of the possible reason may due to better information access by the younger generation on the RE issues and its positive implication to the world. Finding from this paper is useful to help policy maker in designing RE advocacy programs that would be able to secure public participation. These efforts are important to ensure future success of the RE policy.

Predictive occurrence models for coastal wetland plant communities: delineating hydrologic response surfaces with multinomial logistic regression

Science.gov (United States)

Snedden, Gregg A.; Steyer, Gregory D.

2013-01-01

Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007–Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.

Ordered Probit Analysis of Consumers’ Preferences for Milk and Meat Quality Attributes in the Emerging Cities of Southern India

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S. PRIYADHARSINI

2017-09-01

Full Text Available In order to assess consumer preferences for milk and meat quality attributes, a study was carried out in two Second-Tier cities of Tamil Nadu. Personal interviews were done to collect the data from 160 respondents chosen through a multistage sampling procedure in each of the two cities selected for this study. Ordered Probit model fitted for the attributes of milk showed that: family size had a significant positive preference towards texture, low fat and low price of milk, educated consumers paid greater attention to taste, safety, flavour, packaging and low fat attributes of milk and low income consumers paid less importance on most of the attributes of milk. Ordered Probit model for meat revealed that as the family size increased, the consumers were likely to give more importance to ageing and tenderness and less importance to leanness of meat. Male consumers paid greater attention to colour and females were none concerned with tenderness, cooking quality and price. As the education level increased, the consumers became more and more quality and price conscious. Households having children paid more importance to tenderness and taste attributes of meat, whereas the household having aged people opted for colour, taste, tenderness, cooking quality, leanness and price attributes. Low income consumers paid less importance to quality attributes and the respondents performing more physical activity paid lesser attention towards leanness and more importance to price of the meat. This suggests the need for enhancing the production of quality livestock products, together by developing a well-organized distribution system.

Prevalence and Predictors of Pre-Diabetes and Diabetes among Adults 18 Years or Older in Florida: A Multinomial Logistic Modeling Approach.

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Ifechukwude Obiamaka Okwechime

Full Text Available Individuals with pre-diabetes and diabetes have increased risks of developing macro-vascular complications including heart disease and stroke; which are the leading causes of death globally. The objective of this study was to estimate the prevalence of pre-diabetes and diabetes, and to investigate their predictors among adults ≥18 years in Florida.Data covering the time period January-December 2013, were obtained from Florida's Behavioral Risk Factor Surveillance System (BRFSS. Survey design of the study was declared using SVYSET statement of STATA 13.1. Descriptive analyses were performed to estimate the prevalence of pre-diabetes and diabetes. Predictors of pre-diabetes and diabetes were investigated using multinomial logistic regression model. Model goodness-of-fit was evaluated using both the multinomial goodness-of-fit test proposed by Fagerland, Hosmer, and Bofin, as well as, the Hosmer-Lemeshow's goodness of fit test.There were approximately 2,983 (7.3% and 5,189 (12.1% adults in Florida diagnosed with pre-diabetes and diabetes, respectively. Over half of the study respondents were white, married and over the age of 45 years while 36.4% reported being physically inactive, overweight (36.4% or obese (26.4%, hypertensive (34.6%, hypercholesteremic (40.3%, and 26% were arthritic. Based on the final multivariable multinomial model, only being overweight (Relative Risk Ratio [RRR] = 1.85, 95% Confidence Interval [95% CI] = 1.41, 2.42, obese (RRR = 3.41, 95% CI = 2.61, 4.45, hypertensive (RRR = 1.69, 95% CI = 1.33, 2.15, hypercholesterolemic (RRR = 1.94, 95% CI = 1.55, 2.43, and arthritic (RRR = 1.24, 95% CI = 1.00, 1.55 had significant associations with pre-diabetes. However, more predictors had significant associations with diabetes and the strengths of associations tended to be higher than for the association with pre-diabetes. For instance, the relative risk ratios for the association between diabetes and being overweight (RRR = 2.00, 95

Seeking alternatives to probit 9 when developing treatments for wood packaging materials under ISPM No. 15

Science.gov (United States)

R.A. Haack; A. Uzunovic; K. Hoover; J.A. Cook

2011-01-01

ISPM No. 15 presents guidelines for treating wood packaging material used in international trade. There are currently two approved phytosanitary treatments: heat treatment and methyl bromide fumigation. New treatments are under development, and are needed given that methyl bromide is being phased out. Probit 9 efficacy (100% mortality of at least 93 613 test organisms...

An econometric analysis of changes in arable land utilization using multinomial logit model in Pinggu district, Beijing, China.

Science.gov (United States)

Xu, Yueqing; McNamara, Paul; Wu, Yanfang; Dong, Yue

2013-10-15

Arable land in China has been decreasing as a result of rapid population growth and economic development as well as urban expansion, especially in developed regions around cities where quality farmland quickly disappears. This paper analyzed changes in arable land utilization during 1993-2008 in the Pinggu district, Beijing, China, developed a multinomial logit (MNL) model to determine spatial driving factors influencing arable land-use change, and simulated arable land transition probabilities. Land-use maps, as well as social-economic and geographical data were used in the study. The results indicated that arable land decreased significantly between 1993 and 2008. Lost arable land shifted into orchard, forestland, settlement, and transportation land. Significant differences existed for arable land transitions among different landform areas. Slope, elevation, population density, urbanization rate, distance to settlements, and distance to roadways were strong drivers influencing arable land transition to other uses. The MNL model was proved effective for predicting transition probabilities in land use from arable land to other land-use types, thus can be used for scenario analysis to develop land-use policies and land-management measures in this metropolitan area. Copyright © 2013 Elsevier Ltd. All rights reserved.

Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification

Science.gov (United States)

Cao, Faxian; Yang, Zhijing; Ren, Jinchang; Ling, Wing-Kuen; Zhao, Huimin; Marshall, Stephen

2017-12-01

Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.

The Effect of Task Duration on Event-Based Prospective Memory: A Multinomial Modeling Approach

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Hongxia Zhang

2017-11-01

Full Text Available Remembering to perform an action when a specific event occurs is referred to as Event-Based Prospective Memory (EBPM. This study investigated how EBPM performance is affected by task duration by having university students (n = 223 perform an EBPM task that was embedded within an ongoing computer-based color-matching task. For this experiment, we separated the overall task’s duration into the filler task duration and the ongoing task duration. The filler task duration is the length of time between the intention and the beginning of the ongoing task, and the ongoing task duration is the length of time between the beginning of the ongoing task and the appearance of the first Prospective Memory (PM cue. The filler task duration and ongoing task duration were further divided into three levels: 3, 6, and 9 min. Two factors were then orthogonally manipulated between-subjects using a multinomial processing tree model to separate the effects of different task durations on the two EBPM components. A mediation model was then created to verify whether task duration influences EBPM via self-reminding or discrimination. The results reveal three points. (1 Lengthening the duration of ongoing tasks had a negative effect on EBPM performance while lengthening the duration of the filler task had no significant effect on it. (2 As the filler task was lengthened, both the prospective and retrospective components show a decreasing and then increasing trend. Also, when the ongoing task duration was lengthened, the prospective component decreased while the retrospective component significantly increased. (3 The mediating effect of discrimination between the task duration and EBPM performance was significant. We concluded that different task durations influence EBPM performance through different components with discrimination being the mediator between task duration and EBPM performance.

Exploratory multinomial logit model-based driver injury severity analyses for teenage and adult drivers in intersection-related crashes.

Science.gov (United States)

Wu, Qiong; Zhang, Guohui; Ci, Yusheng; Wu, Lina; Tarefder, Rafiqul A; Alcántara, Adélamar Dely

2016-05-18

Teenage drivers are more likely to be involved in severely incapacitating and fatal crashes compared to adult drivers. Moreover, because two thirds of urban vehicle miles traveled are on signal-controlled roadways, significant research efforts are needed to investigate intersection-related teenage driver injury severities and their contributing factors in terms of driver behavior, vehicle-infrastructure interactions, environmental characteristics, roadway geometric features, and traffic compositions. Therefore, this study aims to explore the characteristic differences between teenage and adult drivers in intersection-related crashes, identify the significant contributing attributes, and analyze their impacts on driver injury severities. Using crash data collected in New Mexico from 2010 to 2011, 2 multinomial logit regression models were developed to analyze injury severities for teenage and adult drivers, respectively. Elasticity analyses and transferability tests were conducted to better understand the quantitative impacts of these factors and the teenage driver injury severity model's generality. The results showed that although many of the same contributing factors were found to be significant in the both teenage and adult driver models, certain different attributes must be distinguished to specifically develop effective safety solutions for the 2 driver groups. The research findings are helpful to better understand teenage crash uniqueness and develop cost-effective solutions to reduce intersection-related teenage injury severities and facilitate driver injury mitigation research.

A Capacity-Restraint Transit Assignment Model When a Predetermination Method Indicates the Invalidity of Time Independence

Directory of Open Access Journals (Sweden)

Haoyang Ding

2015-01-01

Full Text Available The statistical independence of time of every two adjacent bus links plays a crucial role in deciding the feasibility of using many mathematical models to analyze urban transit networks. Traditional research generally ignores the time independence that acts as the ground of their models. Assumption is usually made that time independence of every two adjacent links is sound. This is, however, actually groundless and probably causes problematic conclusions reached by corresponding models. Many transit assignment models such as multinomial probit-based models lose their effects when the time independence is not valid. In this paper, a simple method to predetermine the time independence is proposed. Based on the predetermination method, a modified capacity-restraint transit assignment method aimed at engineering practice is put forward and tested through a small contrived network and a case study in Nanjing city, China, respectively. It is found that the slope of regression equation between the mean and standard deviation of normal distribution acts as the indicator of time independence at the same time. Besides, our modified assignment method performs better than the traditional one with more reasonable results while keeping the property of simplicity well.

Migration plans of the rural populations of the Third World countries: a probit analysis of micro-level data from Asia, Africa, and Latin America.

Science.gov (United States)

Mcdevitt, T M; Hawley, A H; Udry, J R; Gadalla, S; Leoprapai, B; Cardona, R

1986-07-01

This study 1) examines the extent to which a given set of microlevel factors has predictive value in different socioeconomic settings and 2) demonstrates the utility of a probit estimation technique in examining plans of rural populations to migrate. Data were collected in 1977-1979 in Thailand, Egypt, and Colombia, 3 countries which differ in culture, extent of urbanization, and proportion of labor force engaged in nonextractive industries. The researchers used identical questionnaires and obtained interviews in 4 rural villages with the "migration shed" of each country's capital city. There were 1088 rural-resident men and women interviewed in Thailand, 1088 in Colombia, and 1376 in Egypt. The researchers gathered information about year-to-year changes in residence, marital status, fertility, housing, employment status, occupation, and industry. While in all 3 countries return moves are relatively frequent, especially among males, the proportions of migrants who have moved 3 or more times do not rise above 10%. The model used portrays the formation of migration intentions of the individual as the outcome of a decision process involving the subjective weighing of perceived differentials in well-being associated with current residence and 1 or more potential destinations, taking into account the direct relocation costs and ability to finance a move. The researchers used dichotomous probit and ordinal probit techniques and 4 variations on the dependant variable to generate some of the results. The only expectancy variable significant in all countries is age. Education is also positively and significantly associated with intentions to move for both sexes in Colombia and Egypt. Marital status is a deterrent to migration plans for males in Colombia and both sexes in Egypt. Previous migration experience fails to show any significant relationship to propensity to move. Conclusions drawn from the data include: 1) the effects of age and economic status appear to increase

Is the probit 9 security level appropriate for disinfestation using gamma-radiation

International Nuclear Information System (INIS)

Ohta, A.T.; Kaneshiro, K.Y.; Kurihara, J.S.; Kanegawa, K.M.; Nagamine, L.R.

1985-01-01

The probit 9 concept requires that a given treatment result in 99.9968 percent mortality in an estimated population of 100,000 individuals. The USDA-Hawaiian Fruit Fly Investigations Laboratory has determined that 0.26 kGy is the minimum absorbed dose of gamma-radiation required to prevent adult emergence of the three species of fruit flies in Hawaii: the Mediterranean fruit fly, Ceratitis capitata; the Oriental fruit fly, Dacus dorsalis; and the melon fly, Dacus cucurbitae. However, at dosages higher than 0.26 kGy, the authors observed relatively high rates of egg hatch (10-30 percent). In addition, when eggs are treated at 0.26 kGy, those larvae that do hatch may develop into third instar larvae, and their feeing may decrease the marketability of the fruits. Furthermore, there is some uncertainty as to whether or not importing countries would accept fruits with any living larvae in the shipment. For these reasons, the authors tried to determine the minimum absorbed dosages required to obtain mortality in mature eggs and larvae of the medfly. Results of the research showed that although high egg and larval mortality was observed at dosages of 0.50 to 0.60 kGy in nearly all of the fruit types and varieties studied, 100 percent mortality of mature eggs and larvae was not attained at these dosages. Nevertheless, the authors think that an increase in the minimum absorbed dose higher than that determined using the probit 9 concept (i.e., 0.26 kGy) should be considered because they were able to ascertain that, at dosages from 0.40 to 0.60 kGy, not only is egg hatch greatly reduced but the larvae hatching from these eggs developed only to the late first or early second larval instar stages

Evaluation of Factors Affecting E-Bike Involved Crash and E-Bike License Plate Use in China Using a Bivariate Probit Model

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Yanyong Guo

2017-01-01

Full Text Available The primary objective of this study is to evaluate factors affecting e-bike involved crash and license plate use in China. E-bike crashes data were collected from police database and completed through a telephone interview. Noncrash samples were collected by a questionnaire survey. A bivariate probit (BP model was developed to simultaneously examine the significant factors associated with e-bike involved crash and e-bike license plate and to account for the correlations between them. Marginal effects for contributory factors were calculated to quantify their impacts on the outcomes. The results show that several contributory factors, including gender, age, education level, driver license, car in household, experiences in using e-bike, law compliance, and aggressive driving behaviors, are found to have significant impacts on both e-bike involved crash and license plate use. Moreover, type of e-bike, frequency of using e-bike, impulse behavior, degree of riding experience, and risk perception scale are found to be associated with e-bike involved crash. It is also found that e-bike involved crash and e-bike license plate use are strongly correlated and are negative in direction. The result enhanced our comprehension of the factors related to e-bike involved crash and e-bike license plate use.

Analysis of the liquidity risk in credit unions: a logit multinomial approach

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Rosiane Maria Lima Gonçalves

2008-10-01

Full Text Available Liquidity risk in financial institutions is associated to balance between working capital and financial demands. Other factors that affect credit union liquidity are an unanticipated increase of withdrawals without an offsetting amount of new deposits, and the lack of ability in promoting the product geographical diversification. The objective of this study is to analyze Minas Gerais state credit union liquidity risk and its factor determinants. Financial ratios and the multinomial logit model are used. The cooperatives were classified in five categories of liquidity risk: very low, low, medium, high and very high. The empirical results indicate that high levels of liquidity are related to smaller values of the outsourcing capital use, immobilization of the turnover capital, and provision ratios. So, they are associated to larger values of the deposit total/credit operations, and asset growth ratios.

The Application of Multinomial Logistic Regression Models for the Assessment of Parameters of Oocytes and Embryos Quality in Predicting Pregnancy and Miscarriage

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Milewska Anna Justyna

2017-09-01

Full Text Available Infertility is a huge problem nowadays, not only from the medical but also from the social point of view. A key step to improve treatment outcomes is the possibility of effective prediction of treatment result. In a situation when a phenomenon with more than 2 states needs to be explained, e.g. pregnancy, miscarriage, non-pregnancy, the use of multinomial logistic regression is a good solution. The aim of this paper is to select those features that have a significant impact on achieving clinical pregnancy as well as those that determine the occurrence of spontaneous miscarriage (non-pregnancy was set as the reference category. Two multi-factor models were obtained, used in predicting infertility treatment outcomes. One of the models enabled to conclude that the number of follicles and the percentage of retrieved mature oocytes have a significant impact when prediction of treatment outcome is made on the basis of information about oocytes. The other model, built on the basis of information about embryos, showed the significance of the number of fertilized oocytes, the percentage of at least 7-cell embryos on day 3, the percentage of blasts on day 5, and the day of transfer.

Influencing Factors of Currency Risk of Deposit Banks in Turkey by Using Probit Method

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Serhat Yüksel

2016-12-01

Full Text Available In this paper, we aimed to analyze the factors that affect currency risk of the banks. Within this scope, annual data of 23 deposit banks for the periods between 2005 and 2015 was evaluated. In addition to this situation, panel probit model was used in order to achieve this objective. Regarding the subject of the currency risk, this model was firstly used in this study. According to the results of the analysis, it was determined that 3 independent variables affect the currency risk of deposit banks in Turkey. Firstly, it was identified that there is a positive relationship between total assets and currency risk. This situation explains that when the size of the banks increases, they tend to take more currency risk. In addition to this variable, it was also defined that there is a direct relationship between economic growth and currency risk of the banks. This result refers that in case of an increment in the market stability; banks think that the market is safer and they increase their currency risk. Moreover, it was also concluded that there is a negative relationship between interest rate and currency risk of the banks. This aspect shows that when interest rate decreases, it will lower uncertainty in the market. Thus, banks would take higher currency risk in such markets.

The Role of Wealth and Health in Insurance Choice: Bivariate Probit Analysis in China

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Yiding Yue

2014-01-01

Full Text Available This paper captures the correlation between the choices of health insurance and pension insurance using the bivariate probit model and then studies the effect of wealth and health on insurance choice. Our empirical evidence shows that people who participate in a health care program are more likely to participate in a pension plan at the same time, while wealth and health have different effects on the choices of the health care program and the pension program. Generally, the higher an individual’s wealth level is, the more likelihood he will participate in a health care program; but wealth has no effect on the participation of pension. Health status has opposite effects on choices of health care programs and pension plans; the poorer an individual’s health is, the more likely he is to participate in health care programs, while the better health he enjoys, the more likely he is to participate in pension plans. When the investigation scope narrows down to commercial insurance, there is only a significant effect of health status on commercial health insurance. The commercial insurance choice and the insurance choice of the agricultural population are more complicated.

Modeling Word Burstiness Using the Dirichlet Distribution

DEFF Research Database (Denmark)

Madsen, Rasmus Elsborg; Kauchak, David; Elkan, Charles

2005-01-01

Multinomial distributions are often used to model text documents. However, they do not capture well the phenomenon that words in a document tend to appear in bursts: if a word appears once, it is more likely to appear again. In this paper, we propose the Dirichlet compound multinomial model (DCM......) as an alternative to the multinomial. The DCM model has one additional degree of freedom, which allows it to capture burstiness. We show experimentally that the DCM is substantially better than the multinomial at modeling text data, measured by perplexity. We also show using three standard document collections...

Modelling Stochastic Route Choice Behaviours with a Closed-Form Mixed Logit Model

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Xinjun Lai

2015-01-01

Full Text Available A closed-form mixed Logit approach is proposed to model the stochastic route choice behaviours. It combines both the advantages of Probit and Logit to provide a flexible form in alternatives correlation and a tractable form in expression; besides, the heterogeneity in alternative variance can also be addressed. Paths are compared by pairs where the superiority of the binary Probit can be fully used. The Probit-based aggregation is also used for a nested Logit structure. Case studies on both numerical and empirical examples demonstrate that the new method is valid and practical. This paper thus provides an operational solution to incorporate the normal distribution in route choice with an analytical expression.

Identifying the Factors Influence Turkish Deposit Banks to Join Corporate Social Responsibility Activities by Using Panel Probit Method

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Serhat Yuksel

2017-02-01

Full Text Available This study aims to determine the influencing factors of the banks to join corporate social responsibility activities. Within this scope, annual data of 23 deposit banks in Turkey for the periods between 2005 and 2015 was taken into the consideration. In addition to this situation, panel probit model was used in the analysis so as to achieve this objective. According to the results of the analysis, it was determined that there is a negative relationship between CSR activities and nonperforming loans ratio. This situation shows that banks do not prefer to make social responsibility activities in case of higher financial losses. In addition to this situation, it was also identified that there is a positive relationship between return on asset and corporate social responsibility activities of the banks. In other words, it can be understood that Turkish deposit banks, which have higher profitability, joint more CSR activities in comparison with others.

Work Intensity of Households: Multinomial Logit Analysis and Correspondence Analysis for Slovak Republic

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Erik Šoltés

2018-03-01

Full Text Available Exclusion from the labour market is a serious social problem that is also addressed by the Europe 2020 strategy. While in the past the attention of statisticians and sociologists in the fight against poverty and social exclusion has concentrated mainly on income poverty and material deprivation, in recent times many studies and analyses are much more focused on work intensity as well. Households that use their work potential to less than 20%, have a very low work intensity, and members of such households are included into the population of people who are at risk of poverty or social exclusion. Moreover, the low use of labour potential of households significantly increases the risk of income poverty and the threat of material deprivation. This article provides an analysis of work intensity levels of Slovak households depending on the factors that are monitored by the EU-SILC 2015. The impact of relevant factors is quantified by correspondence analysis and by multinomial logistic regression model.

Job Motivation and Self-Confidence for Learning and Development as Predictors of Support for Change

OpenAIRE

Vithessonthi, Chaiporn; Schwaninger, Markus

2008-01-01

For the most part, studies on change management have attempted to determine the factors that influence employee resistance to change. The focus of the present study is to test whether job motivation and self-confidence for learning and development influence employee support for downsizing. Data were gathered from a sample of 86 teachers at one private school in Bangkok, Thailand. The analysis was carried out using multinomial ordered probit regression. The results suggest that the level of jo...

A general equation to obtain multiple cut-off scores on a test from multinomial logistic regression.

Science.gov (United States)

Bersabé, Rosa; Rivas, Teresa

2010-05-01

The authors derive a general equation to compute multiple cut-offs on a total test score in order to classify individuals into more than two ordinal categories. The equation is derived from the multinomial logistic regression (MLR) model, which is an extension of the binary logistic regression (BLR) model to accommodate polytomous outcome variables. From this analytical procedure, cut-off scores are established at the test score (the predictor variable) at which an individual is as likely to be in category j as in category j+1 of an ordinal outcome variable. The application of the complete procedure is illustrated by an example with data from an actual study on eating disorders. In this example, two cut-off scores on the Eating Attitudes Test (EAT-26) scores are obtained in order to classify individuals into three ordinal categories: asymptomatic, symptomatic and eating disorder. Diagnoses were made from the responses to a self-report (Q-EDD) that operationalises DSM-IV criteria for eating disorders. Alternatives to the MLR model to set multiple cut-off scores are discussed.

Evaluating risk factors for endemic human Salmonella Enteritidis infections with different phage types in Ontario, Canada using multinomial logistic regression and a case-case study approach

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Varga Csaba

2012-10-01

Full Text Available Abstract Background Identifying risk factors for Salmonella Enteritidis (SE infections in Ontario will assist public health authorities to design effective control and prevention programs to reduce the burden of SE infections. Our research objective was to identify risk factors for acquiring SE infections with various phage types (PT in Ontario, Canada. We hypothesized that certain PTs (e.g., PT8 and PT13a have specific risk factors for infection. Methods Our study included endemic SE cases with various PTs whose isolates were submitted to the Public Health Laboratory-Toronto from January 20th to August 12th, 2011. Cases were interviewed using a standardized questionnaire that included questions pertaining to demographics, travel history, clinical symptoms, contact with animals, and food exposures. A multinomial logistic regression method using the Generalized Linear Latent and Mixed Model procedure and a case-case study design were used to identify risk factors for acquiring SE infections with various PTs in Ontario, Canada. In the multinomial logistic regression model, the outcome variable had three categories representing human infections caused by SE PT8, PT13a, and all other SE PTs (i.e., non-PT8/non-PT13a as a referent category to which the other two categories were compared. Results In the multivariable model, SE PT8 was positively associated with contact with dogs (OR=2.17, 95% CI 1.01-4.68 and negatively associated with pepper consumption (OR=0.35, 95% CI 0.13-0.94, after adjusting for age categories and gender, and using exposure periods and health regions as random effects to account for clustering. Conclusions Our study findings offer interesting hypotheses about the role of phage type-specific risk factors. Multinomial logistic regression analysis and the case-case study approach are novel methodologies to evaluate associations among SE infections with different PTs and various risk factors.

Anàlisi cluster multinomial bayesià..Aplicació a dades electorals

OpenAIRE

Montón Domingo, Maria

2009-01-01

En aquest treball fi de màster se li vol donar una altra visió a les dades de resultats electorals, en concret, les del Parlament de Catalunya. Així doncs, l'eina d'anàlisi que s'utilitza és l'anàlisi clúster multinomial bayesià i les unitats d'estudi són les zones de recerca petita de la ciutat de Barcelona. D'aquesta manera es determina com s'agrupen les diferents zones de recerca petita de Barcelona des del punt de vista de les seves votacions i quina relació hi ha entre els partits en fun...

DETERMINATION OF FACTORS AFFECTING LENGTH OF STAY WITH MULTINOMIAL LOGISTIC REGRESSION IN TURKEY

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Öğr. Gör. Rukiye NUMAN TEKİN

2016-08-01

Full Text Available Length of stay (LOS has important implications in various aspects of health services, can vary according to a wide range of factors. It is noticed that LOS has been neglected mostly in both theoratical studies and practice of health care management in Turkey. The main purpose of this study is to identify factors related to LOS in Turkey. A retrospective analysis of 2.255.836 patients hospitalized to private, university, foundation university and other (municipality, association and foreigners/minority hospitals hospitals which have an agreement with Social Security Institution (SSI in Turkey, from January 1, 2010, until the December 31, 2010, was examined. Patient’s data were taken from MEDULA (National Electronic Invoice System and SPSS 18.0 was used to perform statistical analysis. In this study t-test, one way anova and multinomial logistic regression are used to determine variables that may affect to LOS. The average LOS of patients was 3,93 days (SD = 5,882. LOS showed a statistically significant difference according to all independent variables used in the study (age, gender, disease class, type of hospitalization, presence of comorbidity, type and number of surgery, season of hospitalization, hospital ownership/bed capacity/ geographical region/residential area/type of service. According to the results of the multinomial lojistic regression analysis, LOS was negatively affected in terms of gender, presence of comorbidity, geographical region of hospital and was positively affected in terms of age, season of hospitalization, hospital bed capacity/ ownership/type of service/residential area.

Composite Linear Models | Division of Cancer Prevention

Science.gov (United States)

By Stuart G. Baker The composite linear models software is a matrix approach to compute maximum likelihood estimates and asymptotic standard errors for models for incomplete multinomial data. It implements the method described in Baker SG. Composite linear models for incomplete multinomial data. Statistics in Medicine 1994;13:609-622. The software includes a library of thirty

Poisson versus threshold models for genetic analysis of clinical mastitis in US Holsteins.

Science.gov (United States)

Vazquez, A I; Weigel, K A; Gianola, D; Bates, D M; Perez-Cabal, M A; Rosa, G J M; Chang, Y M

2009-10-01

Typically, clinical mastitis is coded as the presence or absence of disease in a given lactation, and records are analyzed with either linear models or binary threshold models. Because the presence of mastitis may include cows with multiple episodes, there is a loss of information when counts are treated as binary responses. Poisson models are appropriated for random variables measured as the number of events, and although these models are used extensively in studying the epidemiology of mastitis, they have rarely been used for studying the genetic aspects of mastitis. Ordinal threshold models are pertinent for ordered categorical responses; although one can hypothesize that the number of clinical mastitis episodes per animal reflects a continuous underlying increase in mastitis susceptibility, these models have rarely been used in genetic analysis of mastitis. The objective of this study was to compare probit, Poisson, and ordinal threshold models for the genetic evaluation of US Holstein sires for clinical mastitis. Mastitis was measured as a binary trait or as the number of mastitis cases. Data from 44,908 first-parity cows recorded in on-farm herd management software were gathered, edited, and processed for the present study. The cows were daughters of 1,861 sires, distributed over 94 herds. Predictive ability was assessed via a 5-fold cross-validation using 2 loss functions: mean squared error of prediction (MSEP) as the end point and a cost difference function. The heritability estimates were 0.061 for mastitis measured as a binary trait in the probit model and 0.085 and 0.132 for the number of mastitis cases in the ordinal threshold and Poisson models, respectively; because of scale differences, only the probit and ordinal threshold models are directly comparable. Among healthy animals, MSEP was smallest for the probit model, and the cost function was smallest for the ordinal threshold model. Among diseased animals, MSEP and the cost function were smallest

Comparison on models for genetic evaluation of non-return rate and success in first insemination of the Danish Holstein cow

DEFF Research Database (Denmark)

Sun, C; Su, G

2010-01-01

The aim of is study was to compare a linear Gaussian model with logit model and probit model for genetic evaluation of non-return rate within 56 d after first-insemination (NRR56) and success in first insemination (SFI). The whole dataset used in the analysis contained 471,742 records from...... the EBV of proven bulls, obtained from the whole dataset and from a reduced dataset which only contains the first-crop daughters of sires; 2) χ2 statistic for the expected and observed frequency in a cross validation. Heritabilities estimated using linear, probit and logit models were 0.011, 0.014 and 0....... Model validation showed that there was no difference between probit model and logit model, but the two models were better than linear model in stability and predictive ability for genetic evaluation of NRR56 and SFI. However, based on the whole dataset, the correlations between EBV estimated using...

Development and Implementation of a Telecommuting Evaluation Framework, and Modeling the Executive Telecommuting Adoption Process

Science.gov (United States)

Vora, V. P.; Mahmassani, H. S.

2002-02-01

This work proposes and implements a comprehensive evaluation framework to document the telecommuter, organizational, and societal impacts of telecommuting through telecommuting programs. Evaluation processes and materials within the outlined framework are also proposed and implemented. As the first component of the evaluation process, the executive survey is administered within a public sector agency. The survey data is examined through exploratory analysis and is compared to a previous survey of private sector executives. The ordinal probit, dynamic probit, and dynamic generalized ordinal probit (DGOP) models of telecommuting adoption are calibrated to identify factors which significantly influence executive adoption preferences and to test the robustness of such factors. The public sector DGOP model of executive willingness to support telecommuting under different program scenarios is compared with an equivalent private sector DGOP model. Through the telecommuting program, a case study of telecommuting travel impacts is performed to further substantiate research.

Bayes factor covariance testing in item response models

NARCIS (Netherlands)

Fox, J.P.; Mulder, J.; Sinharay, Sandip

2017-01-01

Two marginal one-parameter item response theory models are introduced, by integrating out the latent variable or random item parameter. It is shown that both marginal response models are multivariate (probit) models with a compound symmetry covariance structure. Several common hypotheses concerning

Bayes Factor Covariance Testing in Item Response Models

NARCIS (Netherlands)

Fox, Jean-Paul; Mulder, Joris; Sinharay, Sandip

2017-01-01

Two marginal one-parameter item response theory models are introduced, by integrating out the latent variable or random item parameter. It is shown that both marginal response models are multivariate (probit) models with a compound symmetry covariance structure. Several common hypotheses concerning

The consumer’s choice among television displays: A multinomial logit approach

Directory of Open Access Journals (Sweden)

Carlos Giovanni González Espitia

2013-07-01

Full Text Available The consumer’s choice over a bundle of products depends on observable and unobservable characteristics of goods and consumers. This choice is made in order to maximize utility subject to a budget constraint. At the same time, firms make product differentiation decisions to maximize profit. Quality is a form of differentiation. An example of this occurs in the TV market, where several displays are developed. Our objective is to determine the probability for a consumer of choosing a type of display from among five kinds: standard tube, LCD, plasma, projection and LED. Using a multinomial logit approach, we find that electronic appliances like DVDs and audio systems, as well as socioeconomic status, increase the probability of choosing a high-tech television display. Our empirical approximation contributes to further understanding rational consumer behavior through the theory of utility maximization and highlights the importance of studying market structure and analyzing changes in welfare and efficiency.

Multinomial logistic regression analysis for differentiating 3 treatment outcome trajectory groups for headache-associated disability.

Science.gov (United States)

Lewis, Kristin Nicole; Heckman, Bernadette Davantes; Himawan, Lina

2011-08-01

Growth mixture modeling (GMM) identified latent groups based on treatment outcome trajectories of headache disability measures in patients in headache subspecialty treatment clinics. Using a longitudinal design, 219 patients in headache subspecialty clinics in 4 large cities throughout Ohio provided data on their headache disability at pretreatment and 3 follow-up assessments. GMM identified 3 treatment outcome trajectory groups: (1) patients who initiated treatment with elevated disability levels and who reported statistically significant reductions in headache disability (high-disability improvers; 11%); (2) patients who initiated treatment with elevated disability but who reported no reductions in disability (high-disability nonimprovers; 34%); and (3) patients who initiated treatment with moderate disability and who reported statistically significant reductions in headache disability (moderate-disability improvers; 55%). Based on the final multinomial logistic regression model, a dichotomized treatment appointment attendance variable was a statistically significant predictor for differentiating high-disability improvers from high-disability nonimprovers. Three-fourths of patients who initiated treatment with elevated disability levels did not report reductions in disability after 5 months of treatment with new preventive pharmacotherapies. Preventive headache agents may be most efficacious for patients with moderate levels of disability and for patients with high disability levels who attend all treatment appointments. Copyright © 2011 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

Expert Elicitation of Multinomial Probabilities for Decision-Analytic Modeling: An Application to Rates of Disease Progression in Undiagnosed and Untreated Melanoma.

Science.gov (United States)

Wilson, Edward C F; Usher-Smith, Juliet A; Emery, Jon; Corrie, Pippa G; Walter, Fiona M

2018-06-01

Expert elicitation is required to inform decision making when relevant "better quality" data either do not exist or cannot be collected. An example of this is to inform decisions as to whether to screen for melanoma. A key input is the counterfactual, in this case the natural history of melanoma in patients who are undiagnosed and hence untreated. To elicit expert opinion on the probability of disease progression in patients with melanoma that is undetected and hence untreated. A bespoke webinar-based expert elicitation protocol was administered to 14 participants in the United Kingdom, Australia, and New Zealand, comprising 12 multinomial questions on the probability of progression from one disease stage to another in the absence of treatment. A modified Connor-Mosimann distribution was fitted to individual responses to each question. Individual responses were pooled using a Monte-Carlo simulation approach. Participants were asked to provide feedback on the process. A pooled modified Connor-Mosimann distribution was successfully derived from participants' responses. Feedback from participants was generally positive, with 86% willing to take part in such an exercise again. Nevertheless, only 57% of participants felt that this was a valid approach to determine the risk of disease progression. Qualitative feedback reflected some understanding of the need to rely on expert elicitation in the absence of "hard" data. We successfully elicited and pooled the beliefs of experts in melanoma regarding the probability of disease progression in a format suitable for inclusion in a decision-analytic model. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

The multivariate Dirichlet-multinomial distribution and its application in forensic genetics to adjust for subpopulation effects using the θ-correction

DEFF Research Database (Denmark)

Tvedebrink, Torben; Eriksen, Poul Svante; Morling, Niels

2015-01-01

In this paper, we discuss the construction of a multivariate generalisation of the Dirichlet-multinomial distribution. An example from forensic genetics in the statistical analysis of DNA mixtures motivates the study of this multivariate extension. In forensic genetics, adjustment of the match...... probabilities due to remote ancestry in the population is often done using the so-called θ-correction. This correction increases the probability of observing multiple copies of rare alleles in a subpopulation and thereby reduces the weight of the evidence for rare genotypes. A recent publication by Cowell et al....... (2015) showed elegantly how to use Bayesian networks for efficient computations of likelihood ratios in a forensic genetic context. However, their underlying population genetic model assumed independence of alleles, which is not realistic in real populations. We demonstrate how the so-called θ...

Dealing with selection bias in educational transition models

DEFF Research Database (Denmark)

Holm, Anders; Jæger, Mads Meier

2011-01-01

This paper proposes the bivariate probit selection model (BPSM) as an alternative to the traditional Mare model for analyzing educational transitions. The BPSM accounts for selection on unobserved variables by allowing for unobserved variables which affect the probability of making educational tr...... account for selection on unobserved variables and high-quality data are both required in order to estimate credible educational transition models.......This paper proposes the bivariate probit selection model (BPSM) as an alternative to the traditional Mare model for analyzing educational transitions. The BPSM accounts for selection on unobserved variables by allowing for unobserved variables which affect the probability of making educational...... transitions to be correlated across transitions. We use simulated and real data to illustrate how the BPSM improves on the traditional Mare model in terms of correcting for selection bias and providing credible estimates of the effect of family background on educational success. We conclude that models which...

A comparison of dose-response models for death from hematological depression

International Nuclear Information System (INIS)

Morris, M.D.; Jones, T.D.

1987-01-01

Many radiation-induced lethality experiments that have been published for various mammalian species have been compiled into a database suitable to study interspecific variability of radiosensitivity, dose-rate dependence of sensitivity, dose-response behavior within each experiment, etc. The data compiled were restricted to continuous and nearly continuous exposures to photon radiations having source energies above 100 keV. Also, photon source energy, exposure geometry, and body weight considerations were used to select studies where the dose to hematopoietic marrow was nearly uniform, i.e., < +- 20%. The data base reflects 13 mammalian test species ranging from mouse to cattle. Some 211 studies were compiled but only 105 were documented in adequate detail to be useful in development and evaluation of dose-response models of interest to practical human exposures. Of the 105 studies, 70 were for various rodent species, and 35 were for nonrodent groups ranging from standard laboratory primates (body weight ∼5 kg) to cattle (body weight 375 kg). This paper considers seven different dose-response models which are tested for validity against those 105 studies. The dose-response models included: a right-skewed extreme value, a left-skewed extreme value model, log-logistic, log-probit, logistic, probit, and Weibull models. In general, the log transformed models did not improve model performance and the extreme value models did not seem consistent with the preponderance of the data. Overall, the probit and the logistic models seemed preferable over the Weibull model. 30 refs., 8 tabs

Testing independence between two Poisson-generated multinomial variables in case-series and cohort studies.

Science.gov (United States)

Hocine, Mounia; Guillemot, Didier; Tubert-Bitter, Pascale; Moreau, Thierry

2005-12-30

In case-series or cohort studies, we propose a test of independence between the occurrences of two types of recurrent events (such as two repeated infections) related to an intermittent exposure (such as an antibiotic treatment). The test relies upon an extension of a recent method for analysing case-series data, in the presence of one type of recurrent event. The test statistic is derived from a bivariate Poisson generated-multinomial distribution. Simulations for checking the validity of the test concerning the type I error and the power properties are presented. The test is illustrated using data from a cohort on antibiotics bacterial resistance in schoolchildren. Copyright 2005 John Wiley & Sons, Ltd.

Bayesian mixture models for assessment of gene differential behaviour and prediction of pCR through the integration of copy number and gene expression data.

Directory of Open Access Journals (Sweden)

Filippo Trentini

Full Text Available We consider modeling jointly microarray RNA expression and DNA copy number data. We propose Bayesian mixture models that define latent Gaussian probit scores for the DNA and RNA, and integrate between the two platforms via a regression of the RNA probit scores on the DNA probit scores. Such a regression conveniently allows us to include additional sample specific covariates such as biological conditions and clinical outcomes. The two developed methods are aimed respectively to make inference on differential behaviour of genes in patients showing different subtypes of breast cancer and to predict the pathological complete response (pCR of patients borrowing strength across the genomic platforms. Posterior inference is carried out via MCMC simulations. We demonstrate the proposed methodology using a published data set consisting of 121 breast cancer patients.

Reproductive risk factors assessment for anaemia among pregnant women in India using a multinomial logistic regression model.

Science.gov (United States)

Perumal, Vanamail

2014-07-01

To assess reproductive risk factors for anaemia among pregnant women in urban and rural areas of India. The International Institute of Population Sciences, India, carried out third National Family Health Survey in 2005-2006 to estimate a key indicator from a sample of ever-married women in the reproductive age group 15-49 years. Data on various dimensions were collected using a structured questionnaire, and anaemia was measured using a portable HemoCue instrument. Anaemia prevalence among pregnant women was compared between rural and urban areas using chi-square test and odds ratio. Multinomial logistic regression analysis was used to determine risk factors. Anaemia prevalence was assessed among 3355 pregnant women from rural areas and 1962 pregnant women from urban areas. Moderate-to-severe anaemia in rural areas (32.4%) is significantly more common than in urban areas (27.3%) with an excess risk of 30%. Gestational age specific prevalence of anaemia significantly increases in rural areas after 6 months. Pregnancy duration is a significant risk factor in both urban and rural areas. In rural areas, increasing age at marriage and mass media exposure are significant protective factors of anaemia. However, more births in the last five years, alcohol consumption and smoking habits are significant risk factors. In rural areas, various reproductive factors and lifestyle characteristics constitute significant risk factors for moderate-to-severe anaemia. Therefore, intensive health education on reproductive practices and the impact of lifestyle characteristics are warranted to reduce anaemia prevalence. © 2014 John Wiley & Sons Ltd.

The Effect of Supplemental Instruction on Retention: A Bivariate Probit Model

Science.gov (United States)

Bowles, Tyler J.; Jones, Jason

2004-01-01

Single equation regression models have been used to test the effect of Supplemental Instruction (SI) on student retention. These models, however, fail to account for the two salient features of SI attendance and retention: (1) both SI attendance and retention are categorical variables, and (2) are jointly determined endogenous variables. Adopting…

A new spatial multiple discrete-continuous modeling approach to land use change analysis.

Science.gov (United States)

2013-09-01

This report formulates a multiple discrete-continuous probit (MDCP) land-use model within a : spatially explicit economic structural framework for land-use change decisions. The spatial : MDCP model is capable of predicting both the type and intensit...

Do objective neighbourhood characteristics relate to residents' preferences for certain sports locations? A cross-sectional study using a discrete choice modelling approach.

Science.gov (United States)

Deelen, Ineke; Jansen, Marijke; Dogterom, Nico J; Kamphuis, Carlijn B M; Ettema, Dick

2017-12-11

The number of sports facilities, sports clubs, or city parks in a residential neighbourhood may affect the likelihood that people participate in sports and their preferences for a certain sports location. This study aimed to assess whether objective physical and socio-spatial neighbourhood characteristics relate to sports participation and preferences for sports locations. Data from Dutch adults (N = 1201) on sports participation, their most-used sports location, and socio-demographic characteristics were collected using an online survey. Objective land-use data and the number of sports facilities were gathered for each participant using a 2000-m buffer around their home locations, whereas socio-spatial neighbourhood characteristics (i.e., density, socio-economic status, and safety) were determined at the neighbourhood level. A discrete choice-modelling framework (multinomial probit model) was used to model the associations between neighbourhood characteristics and sports participation and location. Higher proportions of green space, blue space, and the number of sports facilities were positively associated with sports participation in public space, at sports clubs, and at other sports facilities. Higher degrees of urbanization were negatively associated with sports participation at public spaces, sports clubs, and other sports facilities. Those with more green space, blue space or sports facilities in their residential neighbourhood were more likely to participate in sports, but these factors did not affect their preference for a certain sports location. Longitudinal study designs are necessary to assess causality: do active people choose to live in sports-facilitating neighbourhoods, or do neighbourhood characteristics affect sports participation?

Regression Models For Multivariate Count Data.

Science.gov (United States)

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

The outcome of tuberculosis treatment in subjects with chronic kidney disease in Brazil: a multinomial analysis

Directory of Open Access Journals (Sweden)

Barbara Reis-Santos

2013-09-01

Full Text Available OBJECTIVE: To analyze the association between clinical/epidemiological characteristics and outcomes of tuberculosis treatment in patients with concomitant tuberculosis and chronic kidney disease (CKD in Brazil. METHODS: We used the Brazilian Ministry of Health National Case Registry Database to identify patients with tuberculosis and CKD, treated between 2007 and 2011. The tuberculosis treatment outcomes were compared with epidemiological and clinical characteristics of the subjects using a hierarchical multinomial logistic regression model, in which cure was the reference outcome. RESULTS: The prevalence of CKD among patients with tuberculosis was 0.4% (95% CI: 0.37-0.42%. The sample comprised 1,077 subjects. The outcomes were cure, in 58%; treatment abandonment, in 7%; death from tuberculosis, in 13%; and death from other causes, in 22%. The characteristics that differentiated the ORs for treatment abandonment or death were age; alcoholism; AIDS; previous noncompliance with treatment; transfer to another facility; suspected tuberculosis on chest X-ray; positive results in the first smear microscopy; and indications for/use of directly observed treatment, short-course strategy. CONCLUSIONS: Our data indicate the importance of sociodemographic characteristics for the diagnosis of tuberculosis in patients with CKD and underscore the need for tuberculosis control strategies targeting patients with chronic noncommunicable diseases, such as CKD.

The relationship between organizational culture and performance in acute hospitals.

Science.gov (United States)

Jacobs, Rowena; Mannion, Russell; Davies, Huw T O; Harrison, Stephen; Konteh, Fred; Walshe, Kieran

2013-01-01

This paper examines the relationship between senior management team culture and organizational performance in English acute hospitals (NHS Trusts) over three time periods between 2001/2002 and 2007/2008. We use a validated culture rating instrument, the Competing Values Framework, to measure senior management team culture. Organizational performance is assessed using a wide range of routinely collected indicators. We examine the associations between organizational culture and performance using ordered probit and multinomial logit models. We find that organizational culture varies across hospitals and over time, and this variation is at least in part associated in consistent and predictable ways with a variety of organizational characteristics and routine measures of performance. Moreover, hospitals are moving towards more competitive culture archetypes which mirror the current policy context, though with a stronger blend of cultures. The study provides evidence for a relationship between culture and performance in hospital settings. Copyright © 2012 Elsevier Ltd. All rights reserved.

Innovation investment decisions: are post(transition economies different from the rest of the EU?

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Ljiljana BOZIĆ

2017-12-01

Full Text Available The slow progress of innovation in transition economies is not related just to firms’ decision to invest in innovation activities. Rather, it is worth distinguishing between their decision to increase investment, reduce it, keep their investments at the same level or not invest in innovation activities at all. To understand these decisions we develop and estimate models for post-transition and developed European countries employing multinomial probit. The analysis relies on responses of 2580 firms from 11 post-transition countries and 4058 firms from 18 European countries collected by the Flash Eurobarometer 433 - Innobarometer 2016 survey. We have established that the firms’ decision making process in general is mostly related to previous innovation investment experience. In transition countries, the higher the percent of turnover invested in innovation, the lower the probability of an increase in the future. In the firms operating in developed economies, lower turnover from new products is related to the decision to decrease innovation investment in the future.

Modeling pedestrian shopping behavior using principles of bounded rationality: model comparison and validation

Science.gov (United States)

Zhu, Wei; Timmermans, Harry

2011-06-01

Models of geographical choice behavior have been dominantly based on rational choice models, which assume that decision makers are utility-maximizers. Rational choice models may be less appropriate as behavioral models when modeling decisions in complex environments in which decision makers may simplify the decision problem using heuristics. Pedestrian behavior in shopping streets is an example. We therefore propose a modeling framework for pedestrian shopping behavior incorporating principles of bounded rationality. We extend three classical heuristic rules (conjunctive, disjunctive and lexicographic rule) by introducing threshold heterogeneity. The proposed models are implemented using data on pedestrian behavior in Wang Fujing Street, the city center of Beijing, China. The models are estimated and compared with multinomial logit models and mixed logit models. Results show that the heuristic models are the best for all the decisions that are modeled. Validation tests are carried out through multi-agent simulation by comparing simulated spatio-temporal agent behavior with the observed pedestrian behavior. The predictions of heuristic models are slightly better than those of the multinomial logit models.

Discrete choice models for commuting interactions

DEFF Research Database (Denmark)

Rouwendal, Jan; Mulalic, Ismir; Levkovich, Or

An emerging quantitative spatial economics literature models commuting interactions by a gravity equation that is mathematically equivalent to a multinomial logit model. This model is widely viewed as restrictive because of the independence of irrelevant alternatives (IIA) property that links sub...

A Bayesian Multi-Level Factor Analytic Model of Consumer Price Sensitivities across Categories

Science.gov (United States)

Duvvuri, Sri Devi; Gruca, Thomas S.

2010-01-01

Identifying price sensitive consumers is an important problem in marketing. We develop a Bayesian multi-level factor analytic model of the covariation among household-level price sensitivities across product categories that are substitutes. Based on a multivariate probit model of category incidence, this framework also allows the researcher to…

Evaluation of Statistical Methods for Modeling Historical Resource Production and Forecasting

Science.gov (United States)

Nanzad, Bolorchimeg

This master's thesis project consists of two parts. Part I of the project compares modeling of historical resource production and forecasting of future production trends using the logit/probit transform advocated by Rutledge (2011) with conventional Hubbert curve fitting, using global coal production as a case study. The conventional Hubbert/Gaussian method fits a curve to historical production data whereas a logit/probit transform uses a linear fit to a subset of transformed production data. Within the errors and limitations inherent in this type of statistical modeling, these methods provide comparable results. That is, despite that apparent goodness-of-fit achievable using the Logit/Probit methodology, neither approach provides a significant advantage over the other in either explaining the observed data or in making future projections. For mature production regions, those that have already substantially passed peak production, results obtained by either method are closely comparable and reasonable, and estimates of ultimately recoverable resources obtained by either method are consistent with geologically estimated reserves. In contrast, for immature regions, estimates of ultimately recoverable resources generated by either of these alternative methods are unstable and thus, need to be used with caution. Although the logit/probit transform generates high quality-of-fit correspondence with historical production data, this approach provides no new information compared to conventional Gaussian or Hubbert-type models and may have the effect of masking the noise and/or instability in the data and the derived fits. In particular, production forecasts for immature or marginally mature production systems based on either method need to be regarded with considerable caution. Part II of the project investigates the utility of a novel alternative method for multicyclic Hubbert modeling tentatively termed "cycle-jumping" wherein overlap of multiple cycles is limited. The

Sequential and Simultaneous Logit: A Nested Model.

NARCIS (Netherlands)

van Ophem, J.C.M.; Schram, A.J.H.C.

1997-01-01

A nested model is presented which has both the sequential and the multinomial logit model as special cases. This model provides a simple test to investigate the validity of these specifications. Some theoretical properties of the model are discussed. In the analysis a distribution function is

COMPARACION DE 13 INTERVALOS DE CONFIANZA PARA LOS PARAMETROS DE LA DISTRIBUCION MULTINOMIAL

Directory of Open Access Journals (Sweden)

Difariney González-Gómez

2015-07-01

Full Text Available La distribución multinomial es fundamental para la descripción de fenómenos en los que pueden ocurrir k > 2 eventos excluyentes, cada uno con probabilidad π = (π1, π2, . . . , πk. Algunos ejemplos de esta distribución incluyen la calidad de un producto o encuestas de selección múltiple. Un problema de gran interés en inferencia estadística es la construcción de intervalos de confianza los parámetros para π. En este trabajo se comparan, a través de un estudio de simulación, 13 metodologías para la construcción de intervalos de confianza para dicha distribución. Utilizando como criterios de comparación el nivel de confianza nominal, la longitud del intervalo y una combinación de estos, se encuentra que los intervalos de confianza basados en el Teorema del Límite Central no presentan el mejor desempeño. Finalmente se recomiendan los métodos basados en la distribución F (Leemis, 1996, seguido del método de verosimilitud relativa (Kalbfleish, 1985 y Quesenberry & Hurst (1964.

Multinomial Logistic Regression & Bootstrapping for Bayesian Estimation of Vertical Facies Prediction in Heterogeneous Sandstone Reservoirs

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Al-Mudhafar, W. J.

2013-12-01

Precisely prediction of rock facies leads to adequate reservoir characterization by improving the porosity-permeability relationships to estimate the properties in non-cored intervals. It also helps to accurately identify the spatial facies distribution to perform an accurate reservoir model for optimal future reservoir performance. In this paper, the facies estimation has been done through Multinomial logistic regression (MLR) with respect to the well logs and core data in a well in upper sandstone formation of South Rumaila oil field. The entire independent variables are gamma rays, formation density, water saturation, shale volume, log porosity, core porosity, and core permeability. Firstly, Robust Sequential Imputation Algorithm has been considered to impute the missing data. This algorithm starts from a complete subset of the dataset and estimates sequentially the missing values in an incomplete observation by minimizing the determinant of the covariance of the augmented data matrix. Then, the observation is added to the complete data matrix and the algorithm continues with the next observation with missing values. The MLR has been chosen to estimate the maximum likelihood and minimize the standard error for the nonlinear relationships between facies & core and log data. The MLR is used to predict the probabilities of the different possible facies given each independent variable by constructing a linear predictor function having a set of weights that are linearly combined with the independent variables by using a dot product. Beta distribution of facies has been considered as prior knowledge and the resulted predicted probability (posterior) has been estimated from MLR based on Baye's theorem that represents the relationship between predicted probability (posterior) with the conditional probability and the prior knowledge. To assess the statistical accuracy of the model, the bootstrap should be carried out to estimate extra-sample prediction error by randomly

Willingness to pay for renewable energy investment in Korea: A choice experiment study

International Nuclear Information System (INIS)

Ku, Se-Ju; Yoo, Seung-Hoon

2010-01-01

Renewable energy sources are considered as alternatives for coping with the high price of oil and global warming. The Korean government has set a target that 11% of the total primary energy supply should be obtained through renewable energy sources until 2030. In order to develop proper policies for renewable energy investment, it is necessary to analyze the benefits of renewable energy investment based on households' willingness to pay. This study attempts to apply a choice experiment (CE) for assessing renewable energy investment in Korea. Moreover, we employ a multinomial probit (MNP) model to relax the assumption that all respondents have the same preferences for the attributes being valued, which is usually required in empirical CE studies. An MNP model allows the most flexible pattern of error correlation structure. The results reveal that the Korean public puts a value on the protection of wildlife, reduction of pollution, and increased employment opportunities. On the other hand, respondents do not derive significant values from the improvement of landscapes. This study is expected to provide policy-makers with useful information for evaluating and planning policies related to renewable energy investment. (author)

Market Participation and Agro-Biodiversity Loss: The Case of Native Chili Varieties in the Amazon Rainforest of Peru

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Jaqueline Garcia-Yi

2014-01-01

Full Text Available Policies for promoting the in situ conservation of underutilized crop varieties include the provision of economic incentives to farmers for their market commercialization. Nevertheless, market participation could also have the counter-effect of favoring the cultivation of uniform commercial crop varieties and inducing the erosion of crop genetic diversity. The objective of this research was to identify the determinants of the in situ conservation of native chili varieties, including market participation. To this end, 128 farmers were surveyed in the Amazon rainforest region of Ucayali in Peru. The data were analyzed using probit, multinomial logit and truncated Poisson models with covariance matrix correction for cluster errors by rural community. Results suggest that participation in commercial agriculture statistically significantly increases the in situ conservation of native chili varieties; only when farmers sell their products to local retailers, but not when they supply wholesalers. In particular, this result implies that policies designed to encourage specific forms of market participation could have a positive effect on farmers’ economic well-being and simultaneously could help to achieve crop genetic diversity conservation goals.

Highway Expenditures and Associated Customer Satisfaction: A Case Study

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Alexander Paz

2016-01-01

Full Text Available This study analyzes the satisfaction of the Nevadans with respect to their highway transportation system and the corresponding expenditures of Nevada Department of Transportation (NDOT. A survey questionnaire was designed to capture the opinions of the Nevadans (customers about a number of characteristics of their transportation system. Data from the financial data warehouse of the NDOT was used to evaluate expenditures. Multinomial probit models were estimated to study the correlations between customers’ opinion and the government expenditures in transportation. The results indicate the customer satisfaction is decreasing with respect to traffic safety throughout Northwestern and Southern Nevada highways. In addition, users of Northwestern highways are more likely to be satisfied, compared to their counterparts, with increasing construction spending to reduce the time taken to complete construction projects. In Southern Nevada highways, customers’ satisfaction increases with the expenditures associated with reduction of congestion. These insights are examples of the conclusions that were obtained as a consequence of simultaneously considering customer satisfaction and the corresponding expenditures in transportation.

Total, Direct, and Indirect Effects in Logit Models

DEFF Research Database (Denmark)

Karlson, Kristian Bernt; Holm, Anders; Breen, Richard

It has long been believed that the decomposition of the total effect of one variable on another into direct and indirect effects, while feasible in linear models, is not possible in non-linear probability models such as the logit and probit. In this paper we present a new and simple method...... average partial effects, as defined by Wooldridge (2002). We present the method graphically and illustrate it using the National Educational Longitudinal Study of 1988...

Disability weights for infectious diseases in four European countries: comparison between countries and across respondent characteristics

Science.gov (United States)

Maertens de Noordhout, Charline; Devleesschauwer, Brecht; Salomon, Joshua A; Turner, Heather; Cassini, Alessandro; Colzani, Edoardo; Speybroeck, Niko; Polinder, Suzanne; Kretzschmar, Mirjam E; Havelaar, Arie H; Haagsma, Juanita A

2018-01-01

Abstract Background In 2015, new disability weights (DWs) for infectious diseases were constructed based on data from four European countries. In this paper, we evaluated if country, age, sex, disease experience status, income and educational levels have an impact on these DWs. Methods We analyzed paired comparison responses of the European DW study by participants’ characteristics with separate probit regression models. To evaluate the effect of participants’ characteristics, we performed correlation analyses between countries and within country by respondent characteristics and constructed seven probit regression models, including a null model and six models containing participants’ characteristics. We compared these seven models using Akaike Information Criterion (AIC). Results According to AIC, the probit model including country as covariate was the best model. We found a lower correlation of the probit coefficients between countries and income levels (range rs: 0.97–0.99, P European countries. We recommend future researches studying the effect of other characteristics of respondents on health assessment. PMID:29020343

A public perspective on the adoption of microgeneration technologies in New Zealand: A multivariate probit approach

International Nuclear Information System (INIS)

Baskaran, Ramesh; Managi, Shunsuke; Bendig, Mirko

2013-01-01

The growing demand for electricity in New Zealand has led to the construction of new hydro-dams or power stations that have had environmental, social and cultural effects. These effects may drive increases in electricity prices, as such prices reflect the cost of running existing power stations as well as building new ones. This study uses Canterbury and Central Otago as case studies because both regions face similar issues in building new hydro-dams and ever-increasing electricity prices that will eventually prompt households to buy power at higher prices. One way for households to respond to these price changes is to generate their own electricity through microgeneration technologies (MGT). The objective of this study is to investigate public perception and preferences regarding MGT and to analyze the factors that influence people’s decision to adopt such new technologies in New Zealand. The study uses a multivariate probit approach to examine households’ willingness to adopt any one MGT system or a combination of the MGT systems. Our findings provide valuable information for policy makers and marketers who wish to promote effective microgeneration technologies. - Highlights: ► We examine New Zealand households’ awareness level for microgeneration technologies (MGT) and empirically explore the factors that determine people’s willingness to adopt for MGT. ► The households are interested and willing to adopt the MGT systems. ► Noticeable heterogeneity exists between groups of households in adopting the MGT. ► No significant regional difference exists in promoting solar hot water policies. ► Public and private sectors incentives are important in promoting the MGT

Experimental and statistical study on fracture boundary of non-irradiated Zircaloy-4 cladding tube under LOCA conditions

Science.gov (United States)

Narukawa, Takafumi; Yamaguchi, Akira; Jang, Sunghyon; Amaya, Masaki

2018-02-01

For estimating fracture probability of fuel cladding tube under loss-of-coolant accident conditions of light-water-reactors, laboratory-scale integral thermal shock tests were conducted on non-irradiated Zircaloy-4 cladding tube specimens. Then, the obtained binary data with respect to fracture or non-fracture of the cladding tube specimen were analyzed statistically. A method to obtain the fracture probability curve as a function of equivalent cladding reacted (ECR) was proposed using Bayesian inference for generalized linear models: probit, logit, and log-probit models. Then, model selection was performed in terms of physical characteristics and information criteria, a widely applicable information criterion and a widely applicable Bayesian information criterion. As a result, it was clarified that the log-probit model was the best among the three models to estimate the fracture probability in terms of the degree of prediction accuracy for both next data to be obtained and the true model. Using the log-probit model, it was shown that 20% ECR corresponded to a 5% probability level with a 95% confidence of fracture of the cladding tube specimens.

Comparative analysis of informal borrowing behaviour between ...

African Journals Online (AJOL)

Tools of analyses were descriptive statistics of mean and percentages and probit model, The result of the Probit model on the variables influencing borrowing behaviour of male-headed households indicated that the coefficients of household size, farm size, purpose of borrowing, loan duration, interest rate and collateral ...

Immigrants' language skills: the immigrant experience in a longitudinal survey

OpenAIRE

Barry CHISWICK; Yew LEE; Paul W. MILLER

2003-01-01

This paper is concerned with the determinants of English language proficiency among immigrants. It presents a model based on economic incentives, exposure, and efficiency in language acquisition, which it tests using the Longitudinal Survey of Immigrants to Australia. Probit and bivariate probit analyses are employed. The hypotheses are supported by the data. The bivariate probit analysis across waves indicates a "regression to the mean" in the unobserved components of English language profic...

The Dirichlet-Multinomial Model for Multivariate Randomized Response Data and Small Samples

Science.gov (United States)

Avetisyan, Marianna; Fox, Jean-Paul

2012-01-01

In survey sampling the randomized response (RR) technique can be used to obtain truthful answers to sensitive questions. Although the individual answers are masked due to the RR technique, individual (sensitive) response rates can be estimated when observing multivariate response data. The beta-binomial model for binary RR data will be generalized…

Correlations and Non-Linear Probability Models

DEFF Research Database (Denmark)

Breen, Richard; Holm, Anders; Karlson, Kristian Bernt

2014-01-01

the dependent variable of the latent variable model and its predictor variables. We show how this correlation can be derived from the parameters of non-linear probability models, develop tests for the statistical significance of the derived correlation, and illustrate its usefulness in two applications. Under......Although the parameters of logit and probit and other non-linear probability models are often explained and interpreted in relation to the regression coefficients of an underlying linear latent variable model, we argue that they may also be usefully interpreted in terms of the correlations between...... certain circumstances, which we explain, the derived correlation provides a way of overcoming the problems inherent in cross-sample comparisons of the parameters of non-linear probability models....

Bayesian inference in an item response theory model with a generalized student t link function

Science.gov (United States)

Azevedo, Caio L. N.; Migon, Helio S.

2012-10-01

In this paper we introduce a new item response theory (IRT) model with a generalized Student t-link function with unknown degrees of freedom (df), named generalized t-link (GtL) IRT model. In this model we consider only the difficulty parameter in the item response function. GtL is an alternative to the two parameter logit and probit models, since the degrees of freedom (df) play a similar role to the discrimination parameter. However, the behavior of the curves of the GtL is different from those of the two parameter models and the usual Student t link, since in GtL the curve obtained from different df's can cross the probit curves in more than one latent trait level. The GtL model has similar proprieties to the generalized linear mixed models, such as the existence of sufficient statistics and easy parameter interpretation. Also, many techniques of parameter estimation, model fit assessment and residual analysis developed for that models can be used for the GtL model. We develop fully Bayesian estimation and model fit assessment tools through a Metropolis-Hastings step within Gibbs sampling algorithm. We consider a prior sensitivity choice concerning the degrees of freedom. The simulation study indicates that the algorithm recovers all parameters properly. In addition, some Bayesian model fit assessment tools are considered. Finally, a real data set is analyzed using our approach and other usual models. The results indicate that our model fits the data better than the two parameter models.

QR-GARCH-M Model for Risk-Return Tradeoff in U.S. Stock Returns and Business Cycles

OpenAIRE

Nyberg, Henri

2010-01-01

In the empirical finance literature findings on the risk return tradeoff in excess stock market returns are ambiguous. In this study, we develop a new QR-GARCH-M model combining a probit model for a binary business cycle indicator and a regime switching GARCH-in-mean model for excess stock market return with the business cycle indicator defining the regime. Estimation results show that there is statistically significant variation in the U.S. excess stock returns over the business cycle. Howev...

Differentiated influences of benefit and risk perceptions on nuclear power acceptance according to acceptance levels. Evidence from Korea

International Nuclear Information System (INIS)

Roh, Seungkook; Lee Jinwon

2017-01-01

The perceived benefit and risk of nuclear power generation have received considerable attention as determinants of the public's nuclear power acceptance. However, the contingency of the relative importance of these benefit and risk has been less explored. Using Korea as an example, this study explores the possibility that the relative importance of perceived benefit and risk on nuclear power acceptance depends on acceptance levels. Our results from latent class analysis and multinomial probit show that, in determining whether an individual shows a moderate level of nuclear power acceptance rather than a low level, perceived risk plays a dominant role compared to perceived benefit; however, regarding whether he/she shows a high level of nuclear power acceptance rather than a moderate level, this relative importance is reversed. These results carry practical implications for risk governance of nuclear power, particularly with regard to communication with the public. (author)

Predicting Lung Radiotherapy-Induced Pneumonitis Using a Model Combining Parametric Lyman Probit With Nonparametric Decision Trees

International Nuclear Information System (INIS)

Das, Shiva K.; Zhou Sumin; Zhang, Junan; Yin, F.-F.; Dewhirst, Mark W.; Marks, Lawrence B.

2007-01-01

Purpose: To develop and test a model to predict for lung radiation-induced Grade 2+ pneumonitis. Methods and Materials: The model was built from a database of 234 lung cancer patients treated with radiotherapy (RT), of whom 43 were diagnosed with pneumonitis. The model augmented the predictive capability of the parametric dose-based Lyman normal tissue complication probability (LNTCP) metric by combining it with weighted nonparametric decision trees that use dose and nondose inputs. The decision trees were sequentially added to the model using a 'boosting' process that enhances the accuracy of prediction. The model's predictive capability was estimated by 10-fold cross-validation. To facilitate dissemination, the cross-validation result was used to extract a simplified approximation to the complicated model architecture created by boosting. Application of the simplified model is demonstrated in two example cases. Results: The area under the model receiver operating characteristics curve for cross-validation was 0.72, a significant improvement over the LNTCP area of 0.63 (p = 0.005). The simplified model used the following variables to output a measure of injury: LNTCP, gender, histologic type, chemotherapy schedule, and treatment schedule. For a given patient RT plan, injury prediction was highest for the combination of pre-RT chemotherapy, once-daily treatment, female gender and lowest for the combination of no pre-RT chemotherapy and nonsquamous cell histologic type. Application of the simplified model to the example cases revealed that injury prediction for a given treatment plan can range from very low to very high, depending on the settings of the nondose variables. Conclusions: Radiation pneumonitis prediction was significantly enhanced by decision trees that added the influence of nondose factors to the LNTCP formulation

TAFV Alternative Fuels and Vehicles Choice Model Documentation; TOPICAL

International Nuclear Information System (INIS)

Greene, D.L.

2001-01-01

A model for predicting choice of alternative fuel and among alternative vehicle technologies for light-duty motor vehicles is derived. The nested multinomial logit (NML) mathematical framework is used. Calibration of the model is based on information in the existing literature and deduction based on assuming a small number of key parameters, such as the value of time and discount rates. A spreadsheet model has been developed for calibration and preliminary testing of the model

Predicting the probability of recession in Croatia:Is economic sentiment the missing link?

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Nataša Erjavec

2016-12-01

Full Text Available This paper aims to assess the possibility of predicting Croatian recessionary episodes using probit models. The authors first estimate a baseline static model using four leading indicators of recession (monetary base, unemployment, industrial production, and CROBEX stock market index. Lag lengths of up to 6 months are examined for each of the observed variables in the probit specification, and several important conclusions arise from the estimated models. First, the stock market and money supply exhibit the most pronounced leading characteristics in the Croatian economy (a 3-month lag length is selected by the information criteria. Second, the dynamic model (including a lagged dependent dummy variable significantly outperforms the baseline static model. Third, the authors augment the probit model by the Economic Sentiment Indicator, which significantly contributes to the model accuracy. The latter confirms the main hypothesis of the paper, going in line with the assertion that psychological factors largely govern the economic cycles, growing in significance in times of economic hardship.

Choice experiments versus revealed choice models : a before-after study of consumer spatial shopping behavior

NARCIS (Netherlands)

Timmermans, H.J.P.; Borgers, A.W.J.; Waerden, van der P.J.H.J.

1992-01-01

The purpose of this article is to compare a set of multinomial logit models derived from revealed choice data and a decompositional choice model derived from experimental data in terms of predictive success in the context of consumer spatial shopping behavior. Data on consumer shopping choice

Risk Pricing in Emerging Economies: Credit Scoring and Private Banking in Iran

Directory of Open Access Journals (Sweden)

Yiannis Anagnostopoulos

2016-01-01

Full Text Available Iran’s banking industry as a developing country is comparatively very new to risk management practices. An inevitable predictive implication of this rapid growth is the growing concerns with regard to credit risk management which is the motivation of conducting this research. The paper focuses on the credit scoring aspect of credit risk management using both logit and probit regression approaches. Real data on corporate customers are available for conducting this research which is also a contribution to this area for all other developing countries. Our questions focus on how future customers can be classified in terms of credibility, which models and methods are more effective in better capturing risks. Findings suggest that probit approaches are more effective in capturing the significance of variables and goodness-of-fitness tests. Seven variables of the Ohlson O-Score model are used: CL_CA, INTWO, OENEG, TA_TL, SIZE, WCAP_TA, and ROA; two were found to be statistically significant in logit (ROA, TL_TA and three were statistically significant in probit (ROA, TL_TA, SIZE. Also, CL_CA, ROA, and WCAP_TA were the three variables with an unexpected correlation to the probability of default. The prediction power with the cut-off point is set equal to 26% and 56.91% for defaulted customers in both logit and probit models. However, logit achieved 54.85% correct estimation of defaulted assets, 0.37% more than what probit estimated.

Identifiability in N-mixture models: a large-scale screening test with bird data.

Science.gov (United States)

Kéry, Marc

2018-02-01

Binomial N-mixture models have proven very useful in ecology, conservation, and monitoring: they allow estimation and modeling of abundance separately from detection probability using simple counts. Recently, doubts about parameter identifiability have been voiced. I conducted a large-scale screening test with 137 bird data sets from 2,037 sites. I found virtually no identifiability problems for Poisson and zero-inflated Poisson (ZIP) binomial N-mixture models, but negative-binomial (NB) models had problems in 25% of all data sets. The corresponding multinomial N-mixture models had no problems. Parameter estimates under Poisson and ZIP binomial and multinomial N-mixture models were extremely similar. Identifiability problems became a little more frequent with smaller sample sizes (267 and 50 sites), but were unaffected by whether the models did or did not include covariates. Hence, binomial N-mixture model parameters with Poisson and ZIP mixtures typically appeared identifiable. In contrast, NB mixtures were often unidentifiable, which is worrying since these were often selected by Akaike's information criterion. Identifiability of binomial N-mixture models should always be checked. If problems are found, simpler models, integrated models that combine different observation models or the use of external information via informative priors or penalized likelihoods, may help. © 2017 by the Ecological Society of America.

Measuring Response Styles Across the Big Five: A Multiscale Extension of an Approach Using Multinomial Processing Trees.

Science.gov (United States)

Khorramdel, Lale; von Davier, Matthias

2014-01-01

This study shows how to address the problem of trait-unrelated response styles (RS) in rating scales using multidimensional item response theory. The aim is to test and correct data for RS in order to provide fair assessments of personality. Expanding on an approach presented by Böckenholt (2012), observed rating data are decomposed into multiple response processes based on a multinomial processing tree. The data come from a questionnaire consisting of 50 items of the International Personality Item Pool measuring the Big Five dimensions administered to 2,026 U.S. students with a 5-point rating scale. It is shown that this approach can be used to test if RS exist in the data and that RS can be differentiated from trait-related responses. Although the extreme RS appear to be unidimensional after exclusion of only 1 item, a unidimensional measure for the midpoint RS is obtained only after exclusion of 10 items. Both RS measurements show high cross-scale correlations and item response theory-based (marginal) reliabilities. Cultural differences could be found in giving extreme responses. Moreover, it is shown how to score rating data to correct for RS after being proved to exist in the data.

—: A Multicategory Brand Equity Model and Its Application at Allstate

OpenAIRE

Venkatesh Shankar; Pablo Azar; Matthew Fuller

2008-01-01

We develop a robust model for estimating, tracking, and managing brand equity for multicategory brands based on customer survey and financial measures. This model has two components: (1) offering value (computed from discounted cash flow analysis) and (2) relative brand importance (computed from brand choice models such as multinomial logit, heteroscedastic extreme value, and mixed logit). We apply this model to estimate the brand equity of Allstate—a leading insurance company—and its leading...

Text document classification based on mixture models

Czech Academy of Sciences Publication Activity Database

Novovičová, Jana; Malík, Antonín

2004-01-01

Roč. 40, č. 3 (2004), s. 293-304 ISSN 0023-5954 R&D Projects: GA AV ČR IAA2075302; GA ČR GA102/03/0049; GA AV ČR KSK1019101 Institutional research plan: CEZ:AV0Z1075907 Keywords : text classification * text categorization * multinomial mixture model Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.224, year: 2004

Modeling a Multinomial Logit Model of Intercity Travel Mode Choice Behavior for All Trips in Libya

OpenAIRE

Manssour A. Abdulsalam Bin Miskeen; Ahmed Mohamed Alhodairi; Riza Atiq Abdullah Bin O. K. Rahmat

2013-01-01

In the planning point of view, it is essential to have mode choice, due to the massive amount of incurred in transportation systems. The intercity travellers in Libya have distinct features, as against travellers from other countries, which includes cultural and socioeconomic factors. Consequently, the goal of this study is to recognize the behavior of intercity travel using disaggregate models, for projecting the demand of nation-level intercity travel in Libya. Multinom...

Innovation and motivation in public health professionals.

Science.gov (United States)

García-Goñi, Manuel; Maroto, Andrés; Rubalcaba, Luis

2007-12-01

Innovations in public health services promote increases in the health status of the population. Therefore, it is a major concern for health policy makers to understand the drivers of innovation processes. This paper focuses on the differences in behaviour of managers and front-line employees in the pro-innovative provision of public health services. We utilize a survey conducted on front-line employees and managers in public health institutions across six European countries. The survey covers topics related to satisfaction, or attitude towards innovation or their institution. We undertake principal components analysis and analysis of variance, and estimate a multinomial ordered probit model to analyse the existence of different behaviour in managers and front-line employees with respect to innovation. Perception of innovation is different for managers and front-line employees in public health institutions. While front-line employees' attitude depends mostly on the overall performance of the institution, managers feel more involved and motivated, and their behaviour depends more on individual and organisational innovative profiles. It becomes crucial to make both managers and front-line employees at public health institutions feel participative and motivated in order to maximise the benefits of technical or organisational innovative process in the health services provision.

Modelling the monetary policy reaction function of the Colombian Central Bank

OpenAIRE

Otero, Jesus; Ramírez, Manuel

2008-01-01

This paper proposes a simple Ordered Probit model to analyse the monetary policy reaction function of the Colombian Central Bank. There is evidence that the reaction function is asymmetric, in the sense that the Bank increases the Bank rate when the gap between observed inflation and the inflation target (lagged once) is positive, but it does not reduce the Bank rate when the gap is negative. This behaviour suggests that the Bank is more interested in fulfilling the announced inflation target...

Measuring decision weights in recognition experiments with multiple response alternatives: comparing the correlation and multinomial-logistic-regression methods.

Science.gov (United States)

Dai, Huanping; Micheyl, Christophe

2012-11-01

Psychophysical "reverse-correlation" methods allow researchers to gain insight into the perceptual representations and decision weighting strategies of individual subjects in perceptual tasks. Although these methods have gained momentum, until recently their development was limited to experiments involving only two response categories. Recently, two approaches for estimating decision weights in m-alternative experiments have been put forward. One approach extends the two-category correlation method to m > 2 alternatives; the second uses multinomial logistic regression (MLR). In this article, the relative merits of the two methods are discussed, and the issues of convergence and statistical efficiency of the methods are evaluated quantitatively using Monte Carlo simulations. The results indicate that, for a range of values of the number of trials, the estimated weighting patterns are closer to their asymptotic values for the correlation method than for the MLR method. Moreover, for the MLR method, weight estimates for different stimulus components can exhibit strong correlations, making the analysis and interpretation of measured weighting patterns less straightforward than for the correlation method. These and other advantages of the correlation method, which include computational simplicity and a close relationship to other well-established psychophysical reverse-correlation methods, make it an attractive tool to uncover decision strategies in m-alternative experiments.

Mind the information gap: fertility rate and use of cesarean delivery and tocolytic hospitalizations in Taiwan.

Science.gov (United States)

Ma, Ke-Zong M; Norton, Edward C; Lee, Shoou-Yih D

2011-12-12

Physician-induced demand (PID) is an important theory to test given the longstanding controversy surrounding it. Empirical health economists have been challenged to find natural experiments to test the theory because PID is tantamount to strong income effects. The data requirements are both a strong exogenous change in income and two types of treatment that are substitutes but have different net revenues. The theory implies that an exogenous fall in income would lead physicians to recoup their income by substituting a more expensive treatment for a less expensive treatment. This study takes advantages of the dramatic decline in the Taiwanese fertility rate to examine whether an exogenous and negative income shock to obstetricians and gynecologists (ob/gyns) affected the use of c-sections, which has a higher reimbursement rate than vaginal delivery under Taiwan's National Health Insurance system during the study period, and tocolytic hospitalizations. The primary data were obtained from the 1996 to 2004 National Health Insurance Research Database in Taiwan. We hypothesized that a negative income shock to ob/gyns would cause them to provide more c-sections and tocolytic hospitalizations to less medically-informed pregnant women. Multinomial probit and probit models were estimated and the marginal effects of the interaction term were conducted to estimate the impacts of ob/gyn to birth ratio and the information gap. Our results showed that a decline in fertility did not lead ob/gyns to supply more c-sections to less medically-informed pregnant women, and that during fertility decline ob/gyns may supply more tocolytic hospitalizations to compensate their income loss, regardless of pregnant women's access to health information. The exogenous decline in the Taiwanese fertility rate and the use of detailed medical information and demographic attributes of pregnant women allowed us to avoid the endogeneity problem that threatened the validity of prior research. They also

Determination of the Factors Influencing Store Preference in Erzurum by a Multinomial Logit Model

Directory of Open Access Journals (Sweden)

Hüseyin ÖZER

2008-12-01

Full Text Available The main objective of this study is to determine factors influencing store preference of the store costumers in Erzurum in terms of some characteristics of the store and its product and costumers’ demographic characteristics (sex, age, marital status, level of education and their income level. In order to carry out this objective, Pearson chi-square test is applied to determine whether there is a relationship between the store preference and customer, stores, and some characteristics of products and a multinominal logit model is fitted by stepwise regression method to the cross-section data compiled from a questionnaire applied to 384 store costumers in the center of Erzurum province. According to the model estimation and test results, the variables of marital status (married, education (primary and cheapness (unimportant for Migros; education (middle for Özmar and marital status (married for the other stores are determined as statistically significant at the level of 5 percent

The effect of patient satisfaction with pharmacist consultation on medication adherence: an instrumental variable approach

Directory of Open Access Journals (Sweden)

Gu NY

2008-12-01

Full Text Available There are limited studies on quantifying the impact of patient satisfaction with pharmacist consultation on patient medication adherence. Objectives: The objective of this study is to evaluate the effect of patient satisfaction with pharmacist consultation services on medication adherence in a large managed care organization. Methods: We analyzed data from a patient satisfaction survey of 6,916 patients who had used pharmacist consultation services in Kaiser Permanente Southern California from 1993 to 1996. We compared treating patient satisfaction as exogenous, in a single-equation probit model, with a bivariate probit model where patient satisfaction was treated as endogenous. Different sets of instrumental variables were employed, including measures of patients' emotional well-being and patients' propensity to fill their prescriptions at a non-Kaiser Permanente (KP pharmacy. The Smith-Blundell test was used to test whether patient satisfaction was endogenous. Over-identification tests were used to test the validity of the instrumental variables. The Staiger-Stock weak instrument test was used to evaluate the explanatory power of the instrumental variables. Results: All tests indicated that the instrumental variables method was valid and the instrumental variables used have significant explanatory power. The single equation probit model indicated that the effect of patient satisfaction with pharmacist consultation was significant (p<0.010. However, the bivariate probit models revealed that the marginal effect of pharmacist consultation on medication adherence was significantly greater than the single equation probit. The effect increased from 7% to 30% (p<0.010 after controlling for endogeneity bias. Conclusion: After appropriate adjustment for endogeneity bias, patients satisfied with their pharmacy services are substantially more likely to adhere to their medication. The results have important policy implications given the increasing focus

Analysis of transtheoretical model of health behavioral changes in a nutrition intervention study--a continuous time Markov chain model with Bayesian approach.

Science.gov (United States)

Ma, Junsheng; Chan, Wenyaw; Tsai, Chu-Lin; Xiong, Momiao; Tilley, Barbara C

2015-11-30

Continuous time Markov chain (CTMC) models are often used to study the progression of chronic diseases in medical research but rarely applied to studies of the process of behavioral change. In studies of interventions to modify behaviors, a widely used psychosocial model is based on the transtheoretical model that often has more than three states (representing stages of change) and conceptually permits all possible instantaneous transitions. Very little attention is given to the study of the relationships between a CTMC model and associated covariates under the framework of transtheoretical model. We developed a Bayesian approach to evaluate the covariate effects on a CTMC model through a log-linear regression link. A simulation study of this approach showed that model parameters were accurately and precisely estimated. We analyzed an existing data set on stages of change in dietary intake from the Next Step Trial using the proposed method and the generalized multinomial logit model. We found that the generalized multinomial logit model was not suitable for these data because it ignores the unbalanced data structure and temporal correlation between successive measurements. Our analysis not only confirms that the nutrition intervention was effective but also provides information on how the intervention affected the transitions among the stages of change. We found that, compared with the control group, subjects in the intervention group, on average, spent substantively less time in the precontemplation stage and were more/less likely to move from an unhealthy/healthy state to a healthy/unhealthy state. Copyright © 2015 John Wiley & Sons, Ltd.

Insights from computational modeling in inflammation and acute rejection in limb transplantation.

Directory of Open Access Journals (Sweden)

Dolores Wolfram

Full Text Available Acute skin rejection in vascularized composite allotransplantation (VCA is the major obstacle for wider adoption in clinical practice. This study utilized computational modeling to identify biomarkers for diagnosis and targets for treatment of skin rejection. Protein levels of 14 inflammatory mediators in skin and muscle biopsies from syngeneic grafts [n = 10], allogeneic transplants without immunosuppression [n = 10] and allografts treated with tacrolimus [n = 10] were assessed by multiplexed analysis technology. Hierarchical Clustering Analysis, Principal Component Analysis, Random Forest Classification and Multinomial Logistic Regression models were used to segregate experimental groups. Based on Random Forest Classification, Multinomial Logistic Regression and Hierarchical Clustering Analysis models, IL-4, TNF-α and IL-12p70 were the best predictors of skin rejection and identified rejection well in advance of histopathological alterations. TNF-α and IL-12p70 were the best predictors of muscle rejection and also preceded histopathological alterations. Principal Component Analysis identified IL-1α, IL-18, IL-1β, and IL-4 as principal drivers of transplant rejection. Thus, inflammatory patterns associated with rejection are specific for the individual tissue and may be superior for early detection and targeted treatment of rejection.

Patient choice modelling: how do patients choose their hospitals?

Science.gov (United States)

Smith, Honora; Currie, Christine; Chaiwuttisak, Pornpimol; Kyprianou, Andreas

2018-06-01

As an aid to predicting future hospital admissions, we compare use of the Multinomial Logit and the Utility Maximising Nested Logit models to describe how patients choose their hospitals. The models are fitted to real data from Derbyshire, United Kingdom, which lists the postcodes of more than 200,000 admissions to six different local hospitals. Both elective and emergency admissions are analysed for this mixed urban/rural area. For characteristics that may affect a patient's choice of hospital, we consider the distance of the patient from the hospital, the number of beds at the hospital and the number of car parking spaces available at the hospital, as well as several statistics publicly available on National Health Service (NHS) websites: an average waiting time, the patient survey score for ward cleanliness, the patient safety score and the inpatient survey score for overall care. The Multinomial Logit model is successfully fitted to the data. Results obtained with the Utility Maximising Nested Logit model show that nesting according to city or town may be invalid for these data; in other words, the choice of hospital does not appear to be preceded by choice of city. In all of the analysis carried out, distance appears to be one of the main influences on a patient's choice of hospital rather than statistics available on the Internet.

Beyond ROC Curvature: Strength Effects and Response Time Data Support Continuous-Evidence Models of Recognition Memory

Science.gov (United States)

Dube, Chad; Starns, Jeffrey J.; Rotello, Caren M.; Ratcliff, Roger

2012-01-01

A classic question in the recognition memory literature is whether retrieval is best described as a continuous-evidence process consistent with signal detection theory (SDT), or a threshold process consistent with many multinomial processing tree (MPT) models. Because receiver operating characteristics (ROCs) based on confidence ratings are…

Longitudinal categorical data analysis

CERN Document Server

Sutradhar, Brajendra C

2014-01-01

This is the first book in longitudinal categorical data analysis with parametric correlation models developed based on dynamic relationships among repeated categorical responses. This book is a natural generalization of the longitudinal binary data analysis to the multinomial data setup with more than two categories. Thus, unlike the existing books on cross-sectional categorical data analysis using log linear models, this book uses multinomial probability models both in cross-sectional and longitudinal setups. A theoretical foundation is provided for the analysis of univariate multinomial responses, by developing models systematically for the cases with no covariates as well as categorical covariates, both in cross-sectional and longitudinal setups. In the longitudinal setup, both stationary and non-stationary covariates are considered. These models have also been extended to the bivariate multinomial setup along with suitable covariates. For the inferences, the book uses the generalized quasi-likelihood as w...

Joint models for noise annoyance and willingness to pay for road noise reduction

DEFF Research Database (Denmark)

Fosgerau, Mogens; Bue Bjørner, Thomas

2006-01-01

Recent contingent valuation (CV) studies of the willingness to pay (WTP) for road noise reduction have used stated annoyance as an independent variable. We argue that this may be inappropriate due to potential endogeneity bias. Instead, an alternative model is proposed that treats both WTP...... and annoyance as endogenous variables in a simultaneous equation model as a combination of a linear regression with an ordered probit with correlated error terms and possibly common parameters. Thus, information on stated annoyance is utilised to estimate WTP with increased efficiency. Application of the model...... to a dataset from Copenhagen indicates a potential for improving the precision of the estimate of WTP for noise reduction with CV data....

Comparison of Weibull and Probit Analysis in Toxicity Testing of ...

African Journals Online (AJOL)

HP

Keywords: Hunteria umbellata, Weibull model, Acute toxicity, Median lethal dose (LD50). Received: 7 November ... (PBPK) models [14,15], and (v) biologically-. Based Models: Moolgavkar-Venzon-Kundson. (MVK) model [16] and Ellwein and Cohen model [17]. ... Nigeria, Ibadan, where a sample with number. FHI107618 ...

Modeling the Choice of Telecommuting Frequency in California: An Exploratory Analysis

OpenAIRE

Mannering, Jill S.; Mokhtarian, Patricia L.

1995-01-01

This study explores the individual's choice of telecommuting frequency as a function of demographic, travel, work and attitudinal factors. To do this, multinomial logit models are estimated using data collected in a recent survey of employees from three public agencies in California. Separate models are estimated, one for data collected from the Franchise Tax Board in Sacramento, one for data from the Public Utilities Commission in San Francisco, and one for data collected from employees of t...

Summary goodness-of-fit statistics for binary generalized linear models with noncanonical link functions.

Science.gov (United States)

Canary, Jana D; Blizzard, Leigh; Barry, Ronald P; Hosmer, David W; Quinn, Stephen J

2016-05-01

Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness-of-fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (TG), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer-Lemeshow (HL) and Pigeon-Heyse (J(2) ) statistics can be applied directly. In a simulation study, TG, HL, and J(2) were used to evaluate the fit of probit, log-log, complementary log-log, and log models, all calculated with a common grouping method. The TG statistic consistently maintained Type I error rates, while those of HL and J(2) were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, TG had more power than HL or J(2) . © 2015 John Wiley & Sons Ltd/London School of Economics.

Causal mediation analysis with a binary outcome and multiple continuous or ordinal mediators: Simulations and application to an alcohol intervention

OpenAIRE

Nguyen, Trang Quynh; Webb-Vargas, Yenny; Koning, Ina M.; Stuart, Elizabeth A.

2016-01-01

We investigate a method to estimate the combined effect of multiple continuous/ordinal mediators on a binary outcome: 1) fit a structural equation model with probit link for the outcome and identity/probit link for continuous/ordinal mediators, 2) predict potential outcome probabilities, and 3) compute natural direct and indirect effects. Step 2 involves rescaling the latent continuous variable underlying the outcome to address residual mediator variance/covariance. We evaluate the estimation...

Formal and informal care for disabled elderly living in the community: an appraisal of French care composition and costs.

Science.gov (United States)

Paraponaris, Alain; Davin, Bérengère; Verger, Pierre

2012-06-01

Choices between formal and informal care for disabled elderly people living at home are a key component of the long-term care provision issues faced by an ageing population. This paper aims to identify factors associated with the type of care (informal, formal, mixed or no care at all) received by the French disabled elderly and to assess the care's relative costs. This paper uses data from a French survey on disability; the 3,500 respondents of interest lived at home, were aged 60 and over, had severe disability and needed help with activities of daily living. We use a multinomial probit model to determine factors associated with type of care. We also assess the cost of care with the help of the proxy good method. One-third of disabled elderly people receive no care. Among those who are helped, 55% receive informal, 25% formal, and 20% mixed care. Low socioeconomic status increases difficulties in accessing formal care. The estimated economic value of informal care is 6.6 billion euro [95% CI = 5.9-7.2] and represents about two-thirds of the total cost of care. Public policies should pay more attention to inequalities in access to community care. They also should better support informal care, through respite care or workplace accommodations (working hours rescheduling or reduction for instance) not detrimental for the career of working caregivers.

Comparing Multiple-Group Multinomial Log-Linear Models for Multidimensional Skill Distributions in the General Diagnostic Model. Research Report. ETS RR-08-35

Science.gov (United States)

Xu, Xueli; von Davier, Matthias

2008-01-01

The general diagnostic model (GDM) utilizes located latent classes for modeling a multidimensional proficiency variable. In this paper, the GDM is extended by employing a log-linear model for multiple populations that assumes constraints on parameters across multiple groups. This constrained model is compared to log-linear models that assume…

Patterns and correlates of solid waste disposal practices in Dar es ...

African Journals Online (AJOL)

USER

collection. Key words: Solid waste, garbage, waste disposal, waste management, Multinomial Logit model. INTRODUCTION. Urbanization introduces society to a new, modern way of ..... Multinomial logistic estimation. .... The trend of using.

Estimation of the Thurstonian model for the 2-AC protocol

DEFF Research Database (Denmark)

Christensen, Rune Haubo Bojesen; Lee, Hye-Seong; Brockhoff, Per B.

2012-01-01

. This relationship makes it possible to extract estimates and standard errors of δ and τ from general statistical software, and furthermore, it makes it possible to combine standard regression modelling with the Thurstonian model for the 2-AC protocol. A model for replicated 2-AC data is proposed using cumulative......The 2-AC protocol is a 2-AFC protocol with a “no-difference” option and is technically identical to the paired preference test with a “no-preference” option. The Thurstonian model for the 2-AC protocol is parameterized by δ and a decision parameter τ, the estimates of which can be obtained...... by fairly simple well-known methods. In this paper we describe how standard errors of the parameters can be obtained and how exact power computations can be performed. We also show how the Thurstonian model for the 2-AC protocol is closely related to a statistical model known as a cumulative probit model...

Two-vehicle injury severity models based on integration of pavement management and traffic engineering factors.

Science.gov (United States)

Jiang, Ximiao; Huang, Baoshan; Yan, Xuedong; Zaretzki, Russell L; Richards, Stephen

2013-01-01

The severity of traffic-related injuries has been studied by many researchers in recent decades. However, the evaluation of many factors is still in dispute and, until this point, few studies have taken into account pavement management factors as points of interest. The objective of this article is to evaluate the combined influences of pavement management factors and traditional traffic engineering factors on the injury severity of 2-vehicle crashes. This study examines 2-vehicle rear-end, sideswipe, and angle collisions that occurred on Tennessee state routes from 2004 to 2008. Both the traditional ordered probit (OP) model and Bayesian ordered probit (BOP) model with weak informative prior were fitted for each collision type. The performances of these models were evaluated based on the parameter estimates and deviances. The results indicated that pavement management factors played identical roles in all 3 collision types. Pavement serviceability produces significant positive effects on the severity of injuries. The pavement distress index (PDI), rutting depth (RD), and rutting depth difference between right and left wheels (RD_df) were not significant in any of these 3 collision types. The effects of traffic engineering factors varied across collision types, except that a few were consistently significant in all 3 collision types, such as annual average daily traffic (AADT), rural-urban location, speed limit, peaking hour, and light condition. The findings of this study indicated that improved pavement quality does not necessarily lessen the severity of injuries when a 2-vehicle crash occurs. The effects of traffic engineering factors are not universal but vary by the type of crash. The study also found that the BOP model with a weak informative prior can be used as an alternative but was not superior to the traditional OP model in terms of overall performance.

The Development of Dynamic Brand Equity Chase Model and Its Application to Digital Industry Based on Scanner Data

OpenAIRE

Nam Yongsik; Kwak Youngsik

2009-01-01

The purpose of this research is to develop a comprehensive modeling for measuring dynamics of brand power. We define brand power as brand specific coefficients to yield the sales volume for each period. The modeling consists of multinomial log it model for eachproduct category, the brand-specific coefficients, mixture modeling and fuzzy clustering algorithm. We apply our modeling to TV scanner data in Tianjin China. The results show 5 brands have 12 to 23 times change on their brand power in ...

The design and analysis of salmonid tagging studies in the Columbia basin. Volume 8: A new model for estimating survival probabilities and residualization from a release-recapture study of fall chinook salmon (Oncorhynchus tschawytscha) smolts in the Snake River

International Nuclear Information System (INIS)

Lowther, A.B.; Skalski, J.

1997-09-01

Standard release-recapture analysis using Cormack-Jolly-Seber (CJS) models to estimate survival probabilities between hydroelectric facilities for Snake river fall chinook salmon (Oncorhynchus tschawytscha) ignore the possibility of individual fish residualizing and completing their migration in the year following tagging. These models do not utilize available capture history data from this second year and, thus, produce negatively biased estimates of survival probabilities. A new multinomial likelihood model was developed that results in biologically relevant, unbiased estimates of survival probabilities using the full two years of capture history data. This model was applied to 1995 Snake River fall chinook hatchery releases to estimate the true survival probability from one of three upstream release points (Asotin, Billy Creek, and Pittsburgh Landing) to Lower Granite Dam. In the data analyzed here, residualization is not a common physiological response and thus the use of CJS models did not result in appreciably different results than the true survival probability obtained using the new multinomial likelihood model

Latent spatial models and sampling design for landscape genetics

Science.gov (United States)

Hanks, Ephraim M.; Hooten, Mevin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K.

2016-01-01

We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.

Mathematical Modeling for Risk Averse Firm Facing Loss Averse Customer’s Stochastic Uncertainty

Directory of Open Access Journals (Sweden)

Seungbeom Kim

2017-01-01

Full Text Available To optimize the firm’s profit during a finite planning horizon, a dynamic programming model is used to make joint pricing and inventory replenishment decision assuming that customers are loss averse and the firm is risk averse. We model the loss averse customer’s demand using the multinomial choice model. In this choice model, we consider the acquisition and transition utilities widely used by a mental accounting theory which also incorporate the reference price and actual price. Then, we show that there is an optimal inventory policy which is base-stock policy depending on the accumulated wealth in each period.

Web based health surveys: Using a Two Step Heckman model to examine their potential for population health analysis.

Science.gov (United States)

Morrissey, Karyn; Kinderman, Peter; Pontin, Eleanor; Tai, Sara; Schwannauer, Mathias

2016-08-01

In June 2011 the BBC Lab UK carried out a web-based survey on the causes of mental distress. The 'Stress Test' was launched on 'All in the Mind' a BBC Radio 4 programme and the test's URL was publicised on radio and TV broadcasts, and made available via BBC web pages and social media. Given the large amount of data created, over 32,800 participants, with corresponding diagnosis, demographic and socioeconomic characteristics; the dataset are potentially an important source of data for population based research on depression and anxiety. However, as respondents self-selected to participate in the online survey, the survey may comprise a non-random sample. It may be only individuals that listen to BBC Radio 4 and/or use their website that participated in the survey. In this instance using the Stress Test data for wider population based research may create sample selection bias. Focusing on the depression component of the Stress Test, this paper presents an easy-to-use method, the Two Step Probit Selection Model, to detect and statistically correct selection bias in the Stress Test. Using a Two Step Probit Selection Model; this paper did not find a statistically significant selection on unobserved factors for participants of the Stress Test. That is, survey participants who accessed and completed an online survey are not systematically different from non-participants on the variables of substantive interest. Copyright © 2016 Elsevier Ltd. All rights reserved.

Bayesian Analysis of Multilevel Probit Models for Data with Friendship Dependencies

Science.gov (United States)

Koskinen, Johan; Stenberg, Sten-Ake

2012-01-01

When studying educational aspirations of adolescents, it is unrealistic to assume that the aspirations of pupils are independent of those of their friends. Considerable attention has also been given to the study of peer influence in the educational and behavioral literature. Typically, in empirical studies, the friendship networks have either been…

Intercity Travel Demand Analysis Model

Directory of Open Access Journals (Sweden)

Ming Lu

2014-01-01

Full Text Available It is well known that intercity travel is an important component of travel demand which belongs to short distance corridor travel. The conventional four-step method is no longer suitable for short distance corridor travel demand analysis for the time spent on urban traffic has a great impact on traveler's main mode choice. To solve this problem, the author studied the existing intercity travel demand analysis model, then improved it based on the study, and finally established a combined model of main mode choice and access mode choice. At last, an integrated multilevel nested logit model structure system was built. The model system includes trip generation, destination choice, and mode-route choice based on multinomial logit model, and it achieved linkage and feedback of each part through logsum variable. This model was applied in Shenzhen intercity railway passenger demand forecast in 2010 as a case study. As a result, the forecast results were consistent with the actuality. The model's correctness and feasibility were verified.

Workshop on Model Uncertainty and its Statistical Implications

CERN Document Server

1988-01-01

In this book problems related to the choice of models in such diverse fields as regression, covariance structure, time series analysis and multinomial experiments are discussed. The emphasis is on the statistical implications for model assessment when the assessment is done with the same data that generated the model. This is a problem of long standing, notorious for its difficulty. Some contributors discuss this problem in an illuminating way. Others, and this is a truly novel feature, investigate systematically whether sample re-use methods like the bootstrap can be used to assess the quality of estimators or predictors in a reliable way given the initial model uncertainty. The book should prove to be valuable for advanced practitioners and statistical methodologists alike.

Study on the Rationality and Validity of Probit Models of Domino Effect to Chemical Process Equipment caused by Overpressure

International Nuclear Information System (INIS)

Sun, Dongliang; Huang, Guangtuan; Jiang, Juncheng; Zhang, Mingguang; Wang, Zhirong

2013-01-01

Overpressure is one important cause of domino effect in accidents of chemical process equipments. Some models considering propagation probability and threshold values of the domino effect caused by overpressure have been proposed in previous study. In order to prove the rationality and validity of the models reported in the reference, two boundary values of three damage degrees reported were considered as random variables respectively in the interval [0, 100%]. Based on the overpressure data for damage to the equipment and the damage state, and the calculation method reported in the references, the mean square errors of the four categories of damage probability models of overpressure were calculated with random boundary values, and then a relationship of mean square error vs. the two boundary value was obtained, the minimum of mean square error was obtained, compared with the result of the present work, mean square error decreases by about 3%. Therefore, the error was in the acceptable range of engineering applications, the models reported can be considered reasonable and valid.

Study on the Rationality and Validity of Probit Models of Domino Effect to Chemical Process Equipment caused by Overpressure

Science.gov (United States)

Sun, Dongliang; Huang, Guangtuan; Jiang, Juncheng; Zhang, Mingguang; Wang, Zhirong

2013-04-01

Overpressure is one important cause of domino effect in accidents of chemical process equipments. Some models considering propagation probability and threshold values of the domino effect caused by overpressure have been proposed in previous study. In order to prove the rationality and validity of the models reported in the reference, two boundary values of three damage degrees reported were considered as random variables respectively in the interval [0, 100%]. Based on the overpressure data for damage to the equipment and the damage state, and the calculation method reported in the references, the mean square errors of the four categories of damage probability models of overpressure were calculated with random boundary values, and then a relationship of mean square error vs. the two boundary value was obtained, the minimum of mean square error was obtained, compared with the result of the present work, mean square error decreases by about 3%. Therefore, the error was in the acceptable range of engineering applications, the models reported can be considered reasonable and valid.

Identifying response styles: A latent-class bilinear multinomial logit model

NARCIS (Netherlands)

van Rosmalen, J.; van Herk, H.; Groenen, P.J.F.

2010-01-01

Respondents can vary strongly in the way they use rating scales. Specifically, respondents can exhibit a variety of response styles, which threatens the validity of the responses. The purpose of this article Is to investigate how response style and content of the items affect rating scale responses.

Assessment of professional risk caused by heating microclimate in the process of un-derground mining

Directory of Open Access Journals (Sweden)

М. Л. Рудаков

2017-06-01

Full Text Available The paper reviews the possibility to apply probit-function to assess professional risks of underground mining under conditions of heating microclimate. Operations under conditions of heating microclimate, whose parameters exceed threshold criteria, can lead to dehydration, fainting and heat stroke for mine workers. Basing on the results of medico-biological research on the effects of microclimate on human body, the authors have assessed probabilistic nature of excessive heat accumulation depending on heat stress index.Using Shapiro-Wilk statistics, an assessment has been carried out in order to test correspondence of experimental data on heat accumulation in the human body to the normal law of distribution for different values of heat stress index, measured in the process of underground mining operations under conditions of heating microclimate.The paper justifies construction of a probit-model to assess professional risks caused by overheating for various types of underground mining operations, depending on their intensity.Modeling results have been verified by way of comparison with a currently used deterministic model of body overheating. Taking into account satisfactory convergence of results, the authors suggest using probit-model to assess professional risks of overheating, as this model allows to obtain a continuous dependency between professional risk and heat stress index, which in its own turn facilitates a more justified approach to the selection of measures to upgrade working conditions of personnel.

Rigorously testing multialternative decision field theory against random utility models.

Science.gov (United States)

Berkowitsch, Nicolas A J; Scheibehenne, Benjamin; Rieskamp, Jörg

2014-06-01

Cognitive models of decision making aim to explain the process underlying observed choices. Here, we test a sequential sampling model of decision making, multialternative decision field theory (MDFT; Roe, Busemeyer, & Townsend, 2001), on empirical grounds and compare it against 2 established random utility models of choice: the probit and the logit model. Using a within-subject experimental design, participants in 2 studies repeatedly choose among sets of options (consumer products) described on several attributes. The results of Study 1 showed that all models predicted participants' choices equally well. In Study 2, in which the choice sets were explicitly designed to distinguish the models, MDFT had an advantage in predicting the observed choices. Study 2 further revealed the occurrence of multiple context effects within single participants, indicating an interdependent evaluation of choice options and correlations between different context effects. In sum, the results indicate that sequential sampling models can provide relevant insights into the cognitive process underlying preferential choices and thus can lead to better choice predictions. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Differential Topic Models.

Science.gov (United States)

Chen, Changyou; Buntine, Wray; Ding, Nan; Xie, Lexing; Du, Lan

2015-02-01

In applications we may want to compare different document collections: they could have shared content but also different and unique aspects in particular collections. This task has been called comparative text mining or cross-collection modeling. We present a differential topic model for this application that models both topic differences and similarities. For this we use hierarchical Bayesian nonparametric models. Moreover, we found it was important to properly model power-law phenomena in topic-word distributions and thus we used the full Pitman-Yor process rather than just a Dirichlet process. Furthermore, we propose the transformed Pitman-Yor process (TPYP) to incorporate prior knowledge such as vocabulary variations in different collections into the model. To deal with the non-conjugate issue between model prior and likelihood in the TPYP, we thus propose an efficient sampling algorithm using a data augmentation technique based on the multinomial theorem. Experimental results show the model discovers interesting aspects of different collections. We also show the proposed MCMC based algorithm achieves a dramatically reduced test perplexity compared to some existing topic models. Finally, we show our model outperforms the state-of-the-art for document classification/ideology prediction on a number of text collections.

Effects of supplementary private health insurance on physician visits in Korea.

Science.gov (United States)

Kang, Sungwook; You, Chang Hoon; Kwon, Young Dae; Oh, Eun-Hwan

2009-12-01

The coverage of social health insurance has remained limited, despite it being compulsory in Korea. Accordingly, Koreans have come to rely upon supplementary private health insurance (PHI) to cover their medical costs. We examined the effects of supplementary PHI on physician visits in Korea. This study used individual data from 11,043 respondents who participated in the Korean Labor and Income Panel Survey in 2001. We conducted a single probit model to identify the relationship between PHI and physician visits, with adjustment for the following covariates: demographic characteristics, socioeconomic status, health status, and health-related behavior. Finally, we performed a bivariate probit model to examine the true effect of PHI on physician visits, with adjustment for the above covariates plus unobservable covariates that might affect not only physician visit, but also the purchase of PHI. We found that about 38% of all respondents had one or more private health plans. Forty-five percent of all respondents visited one or more physicians, and 49% of those who were privately insured had physician visits compared with 42% of the uninsured. The single probit model showed that those with PHI were about 14 percentage points more likely to visit physicians than those who do not have PHI. However, this distinction disappears in the bivariate probit model. This result might have been a consequence of the nature of private health plans in Korea. Private insurance companies pay a fixed amount directly to their enrollees in case of illness/injury, and the individuals are responsible subsequently for purchasing their own healthcare services. This study demonstrated the potential of Korean PHI to address the problem of moral hazard. These results serve as a reference for policy makers when considering how to finance healthcare services, as well as to contain healthcare expenditure.

Causal Mediation Analysis of Survival Outcome with Multiple Mediators.

Science.gov (United States)

Huang, Yen-Tsung; Yang, Hwai-I

2017-05-01

Mediation analyses have been a popular approach to investigate the effect of an exposure on an outcome through a mediator. Mediation models with multiple mediators have been proposed for continuous and dichotomous outcomes. However, development of multimediator models for survival outcomes is still limited. We present methods for multimediator analyses using three survival models: Aalen additive hazard models, Cox proportional hazard models, and semiparametric probit models. Effects through mediators can be characterized by path-specific effects, for which definitions and identifiability assumptions are provided. We derive closed-form expressions for path-specific effects for the three models, which are intuitively interpreted using a causal diagram. Mediation analyses using Cox models under the rare-outcome assumption and Aalen additive hazard models consider effects on log hazard ratio and hazard difference, respectively; analyses using semiparametric probit models consider effects on difference in transformed survival time and survival probability. The three models were applied to a hepatitis study where we investigated effects of hepatitis C on liver cancer incidence mediated through baseline and/or follow-up hepatitis B viral load. The three methods show consistent results on respective effect scales, which suggest an adverse estimated effect of hepatitis C on liver cancer not mediated through hepatitis B, and a protective estimated effect mediated through the baseline (and possibly follow-up) of hepatitis B viral load. Causal mediation analyses of survival outcome with multiple mediators are developed for additive hazard and proportional hazard and probit models with utility demonstrated in a hepatitis study.

Modeling the Joint Labor-Commute Engagement Decisions of San Francisco Bay Area Residents

OpenAIRE

Ory, David T.; Mokhtarian, Patricia L.

2005-01-01

Using socio-demographic, personality, and attitudinal data from 1,680 residents of the San Francisco Bay Area, we develop and estimate binary, multinomial, and nested logit models of the choice to work or not, whether or not to work at home, and whether to commute all of the time or some of the time (either by only working part time, or by working a compressed work week, or by telecommuting some of the time). To our knowledge, these are the first models of all these choices simultaneously. Th...

Choice Model and Influencing Factor Analysis of Travel Mode for Migrant Workers: Case Study in Xi’an, China

OpenAIRE

Hong Chen; Zuo-xian Gan; Yu-ting He

2015-01-01

Based on the basic theory and methods of disaggregate choice model, the influencing factors in travel mode choice for migrant workers are analyzed, according to 1366 data samples of Xi’an migrant workers. Walking, bus, subway, and taxi are taken as the alternative parts of travel modes for migrant workers, and a multinomial logit (MNL) model of travel mode for migrant workers is set up. The validity of the model is verified by the hit rate, and the hit rates of four travel modes are all great...

Survival analysis with covariates in combination with multinomial analysis to parametrize time to event for multi-state models

NARCIS (Netherlands)

Feenstra, T.L.; Postmus, D.; Quik, E.H.; Langendijk, H.; Krabbe, P.F.M.

Objectives: Recent ISPOR Good practice guidelines as well as literature encourage to use a single distribution rather than the latent failure approach to model time to event for patient level simulation models with multiple competing outcomes. Aim was to apply the preferred method of a single

Survival analysis with covariates in combination with multinomial analysis to parametrize time to event for multi-state models

NARCIS (Netherlands)

Feenstra, T.L.; Postmus, D.; Quik, E.H.; Langendijk, H.; Krabbe, P.F.M.

2013-01-01

Objectives: Recent ISPOR Good practice guidelines as well as literature encourage to use a single distribution rather than the latent failure approach to model time to event for patient level simulation models with multiple competing outcomes. Aim was to apply the preferred method of a single

Bayes Factor Covariance Testing in Item Response Models.

Science.gov (United States)

Fox, Jean-Paul; Mulder, Joris; Sinharay, Sandip

2017-12-01

Two marginal one-parameter item response theory models are introduced, by integrating out the latent variable or random item parameter. It is shown that both marginal response models are multivariate (probit) models with a compound symmetry covariance structure. Several common hypotheses concerning the underlying covariance structure are evaluated using (fractional) Bayes factor tests. The support for a unidimensional factor (i.e., assumption of local independence) and differential item functioning are evaluated by testing the covariance components. The posterior distribution of common covariance components is obtained in closed form by transforming latent responses with an orthogonal (Helmert) matrix. This posterior distribution is defined as a shifted-inverse-gamma, thereby introducing a default prior and a balanced prior distribution. Based on that, an MCMC algorithm is described to estimate all model parameters and to compute (fractional) Bayes factor tests. Simulation studies are used to show that the (fractional) Bayes factor tests have good properties for testing the underlying covariance structure of binary response data. The method is illustrated with two real data studies.

A constrained rasch model of trace redintegration in serial recall.

Science.gov (United States)

Roodenrys, Steven; Miller, Leonie M

2008-04-01

The notion that verbal short-term memory tasks, such as serial recall, make use of information in long-term as well as in short-term memory is instantiated in many models of these tasks. Such models incorporate a process in which degraded traces retrieved from a short-term store are reconstructed, or redintegrated (Schweickert, 1993), through the use of information in long-term memory. This article presents a conceptual and mathematical model of this process based on a class of item-response theory models. It is demonstrated that this model provides a better fit to three sets of data than does the multinomial processing tree model of redintegration (Schweickert, 1993) and that a number of conceptual accounts of serial recall can be related to the parameters of the model.

Attitudinal travel demand model for non-work trips of homogeneously constrained segments of a population

Energy Technology Data Exchange (ETDEWEB)

Recker, W.W.; Stevens, R.F.

1977-06-01

Market-segmentation techniques are used to capture effects of opportunity and availability constraints on urban residents' choice of mode for trips for major grocery shopping and for visiting friends and acquaintances. Attitudinal multinomial logit choice models are estimated for each market segment. Explanatory variables are individual's beliefs about attributes of four modal alternatives: bus, car, taxi and walking. Factor analysis is employed to identify latent dimensions of perception of the modal alternatives and to eliminate problems of multicollinearity in model estimation.

Knowledge and adoption of solar home systems in rural Nicaragua

International Nuclear Information System (INIS)

Rebane, Kaja L.; Barham, Bradford L.

2011-01-01

Solar home systems (SHSs) are a promising electrification option for many households in the developing world. In most countries SHSs are at an early stage of dissemination, and thus face a hurdle common to many emerging alternative energy technologies: many people do not know enough about them to decide whether to adopt one or not. This study uses survey data collected in Nicaragua to investigate characteristics that predict the knowledge and adoption of SHSs among the rural population. First, a series of probit models is used to model the determinants of four measures of SHS knowledge. Next, a biprobit model with sample selection is employed to investigate the factors that predict SHS adoption, conditional on having sufficient knowledge to make an adoption decision. Comparison of the biprobit formulation to a standard probit model of adoption affirms its value. This study identifies multiple determinants of SHS knowledge and adoption, offers several practical recommendations to project planners, and provides an analytical framework for future work in this policy-relevant area. - Research highlights: → Solar home systems (SHSs) are a promising rural electrification option in the developing world. → As with many emerging renewable energy technologies, lack of knowledge may limit SHS adoption. → We use probit models to investigate the determinants of SHS knowledge in rural Nicaragua. → We also employ a biprobit model linking the determinants of knowledge and adoption. → We find that in analyzing SHS adoption, accounting for sample selection based on knowledge is key.

Longitudinal beta-binomial modeling using GEE for overdispersed binomial data.

Science.gov (United States)

Wu, Hongqian; Zhang, Ying; Long, Jeffrey D

2017-03-15

Longitudinal binomial data are frequently generated from multiple questionnaires and assessments in various scientific settings for which the binomial data are often overdispersed. The standard generalized linear mixed effects model may result in severe underestimation of standard errors of estimated regression parameters in such cases and hence potentially bias the statistical inference. In this paper, we propose a longitudinal beta-binomial model for overdispersed binomial data and estimate the regression parameters under a probit model using the generalized estimating equation method. A hybrid algorithm of the Fisher scoring and the method of moments is implemented for computing the method. Extensive simulation studies are conducted to justify the validity of the proposed method. Finally, the proposed method is applied to analyze functional impairment in subjects who are at risk of Huntington disease from a multisite observational study of prodromal Huntington disease. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A model for predicting Inactivity in the European Banking Sector

Directory of Open Access Journals (Sweden)

Themistokles Lazarides

2015-08-01

Full Text Available Purpose – The paper will addresses the issue of inactivity and will try to detect its causes using econometric models. The Banking sector of Europe has been under transformation or restructuring for almost half a century. Design/methodology/approach – Probit models and descriptive statistics have been used to create a system that predicts inactivity. The data was collected from Bankscope. Findings – The results of the econometric models show that from the six groups of indicators, four have been found to be statistically important (performance, size, ownership, corporate governance. These findings are consistent with the theory. Research limitations/implications – The limitation is that Bankscope does not provide any longitudinal data regarding ownership, management structure and there are some many missing values before 2007 for some of the financial ratios and data. Originality/value – The paper's value and innovation is that it has given a systemic approach to find indicators of inactivity.

A Heckman selection model for the safety analysis of signalized intersections.

Directory of Open Access Journals (Sweden)

Xuecai Xu

Full Text Available The objective of this paper is to provide a new method for estimating crash rate and severity simultaneously.This study explores a Heckman selection model of the crash rate and severity simultaneously at different levels and a two-step procedure is used to investigate the crash rate and severity levels. The first step uses a probit regression model to determine the sample selection process, and the second step develops a multiple regression model to simultaneously evaluate the crash rate and severity for slight injury/kill or serious injury (KSI, respectively. The model uses 555 observations from 262 signalized intersections in the Hong Kong metropolitan area, integrated with information on the traffic flow, geometric road design, road environment, traffic control and any crashes that occurred during two years.The results of the proposed two-step Heckman selection model illustrate the necessity of different crash rates for different crash severity levels.A comparison with the existing approaches suggests that the Heckman selection model offers an efficient and convenient alternative method for evaluating the safety performance at signalized intersections.

Modeling the Cumulative Effects of Social Exposures on Health: Moving beyond Disease-Specific Models

Directory of Open Access Journals (Sweden)

Heather L. White

2013-03-01

Full Text Available The traditional explanatory models used in epidemiology are “disease specific”, identifying risk factors for specific health conditions. Yet social exposures lead to a generalized, cumulative health impact which may not be specific to one illness. Disease-specific models may therefore misestimate social factors’ effects on health. Using data from the Canadian Community Health Survey and Canada 2001 Census we construct and compare “disease-specific” and “generalized health impact” (GHI models to gauge the negative health effects of one social exposure: socioeconomic position (SEP. We use logistic and multinomial multilevel modeling with neighbourhood-level material deprivation, individual-level education and household income to compare and contrast the two approaches. In disease-specific models, the social determinants under study were each associated with the health conditions of interest. However, larger effect sizes were apparent when outcomes were modeled as compound health problems (0, 1, 2, or 3+ conditions using the GHI approach. To more accurately estimate social exposures’ impacts on population health, researchers should consider a GHI framework.

ABrox-A user-friendly Python module for approximate Bayesian computation with a focus on model comparison.

Science.gov (United States)

Mertens, Ulf Kai; Voss, Andreas; Radev, Stefan

2018-01-01

We give an overview of the basic principles of approximate Bayesian computation (ABC), a class of stochastic methods that enable flexible and likelihood-free model comparison and parameter estimation. Our new open-source software called ABrox is used to illustrate ABC for model comparison on two prominent statistical tests, the two-sample t-test and the Levene-Test. We further highlight the flexibility of ABC compared to classical Bayesian hypothesis testing by computing an approximate Bayes factor for two multinomial processing tree models. Last but not least, throughout the paper, we introduce ABrox using the accompanied graphical user interface.

Modelling and Forecasting Stock Price Movements with Serially Dependent Determinants

Directory of Open Access Journals (Sweden)

Rasika Yatigammana

2018-05-01

Full Text Available The direction of price movements are analysed under an ordered probit framework, recognising the importance of accounting for discreteness in price changes. By extending the work of Hausman et al. (1972 and Yang and Parwada (2012,This paper focuses on improving the forecast performance of the model while infusing a more practical perspective by enhancing flexibility. This is achieved by extending the existing framework to generate short term multi period ahead forecasts for better decision making, whilst considering the serial dependence structure. This approach enhances the flexibility and adaptability of the model to future price changes, particularly targeting risk minimisation. Empirical evidence is provided, based on seven stocks listed on the Australian Securities Exchange (ASX. The prediction success varies between 78 and 91 per cent for in-sample and out-of-sample forecasts for both the short term and long term.

Statistical modelling for social researchers principles and practice

CERN Document Server

Tarling, Roger

2008-01-01

This book explains the principles and theory of statistical modelling in an intelligible way for the non-mathematical social scientist looking to apply statistical modelling techniques in research. The book also serves as an introduction for those wishing to develop more detailed knowledge and skills in statistical modelling. Rather than present a limited number of statistical models in great depth, the aim is to provide a comprehensive overview of the statistical models currently adopted in social research, in order that the researcher can make appropriate choices and select the most suitable model for the research question to be addressed. To facilitate application, the book also offers practical guidance and instruction in fitting models using SPSS and Stata, the most popular statistical computer software which is available to most social researchers. Instruction in using MLwiN is also given. Models covered in the book include; multiple regression, binary, multinomial and ordered logistic regression, log-l...

Endogeneity and Heterogeneity in a Probit Demand Model: Estimation Using Aggregate Data

OpenAIRE

Pradeep K. Chintagunta

2001-01-01

Two issues that have become increasingly important while estimating the parameters of aggregate demand functions to study firm behavior are the of marketing activities (typically, price) and across consumers in the market under consideration. Ignoring these issues in the estimation of the demand function parameters can lead to biased and inconsistent estimates for the effects of marketing activities. Endogeneity and heterogeneity have achieved prominence in large measure because of the increa...

Effects of ignoring baseline on modeling transitions from intact cognition to dementia.

Science.gov (United States)

Yu, Lei; Tyas, Suzanne L; Snowdon, David A; Kryscio, Richard J

2009-07-01

This paper evaluates the effect of ignoring baseline when modeling transitions from intact cognition to dementia with mild cognitive impairment (MCI) and global impairment (GI) as intervening cognitive states. Transitions among states are modeled by a discrete-time Markov chain having three transient (intact cognition, MCI, and GI) and two competing absorbing states (death and dementia). Transition probabilities depend on two covariates, age and the presence/absence of an apolipoprotein E-epsilon4 allele, through a multinomial logistic model with shared random effects. Results are illustrated with an application to the Nun Study, a cohort of 678 participants 75+ years of age at baseline and followed longitudinally with up to ten cognitive assessments per nun.

Nonparametric Bayes Modeling of Multivariate Categorical Data.

Science.gov (United States)

Dunson, David B; Xing, Chuanhua

2012-01-01

Modeling of multivariate unordered categorical (nominal) data is a challenging problem, particularly in high dimensions and cases in which one wishes to avoid strong assumptions about the dependence structure. Commonly used approaches rely on the incorporation of latent Gaussian random variables or parametric latent class models. The goal of this article is to develop a nonparametric Bayes approach, which defines a prior with full support on the space of distributions for multiple unordered categorical variables. This support condition ensures that we are not restricting the dependence structure a priori. We show this can be accomplished through a Dirichlet process mixture of product multinomial distributions, which is also a convenient form for posterior computation. Methods for nonparametric testing of violations of independence are proposed, and the methods are applied to model positional dependence within transcription factor binding motifs.

A behavioral choice model of the use of car-sharing and ride-sourcing services

Energy Technology Data Exchange (ETDEWEB)

Dias, Felipe F.; Lavieri, Patrícia S.; Garikapati, Venu M.; Astroza, Sebastian; Pendyala, Ram M.; Bhat, Chandra R.

2017-07-26

There are a number of disruptive mobility services that are increasingly finding their way into the marketplace. Two key examples of such services are car-sharing services and ride-sourcing services. In an effort to better understand the influence of various exogenous socio-economic and demographic variables on the frequency of use of ride-sourcing and car-sharing services, this paper presents a bivariate ordered probit model estimated on a survey data set derived from the 2014-2015 Puget Sound Regional Travel Study. Model estimation results show that users of these services tend to be young, well-educated, higher-income, working individuals residing in higher-density areas. There are significant interaction effects reflecting the influence of children and the built environment on disruptive mobility service usage. The model developed in this paper provides key insights into factors affecting market penetration of these services, and can be integrated in larger travel forecasting model systems to better predict the adoption and use of mobility-on-demand services.

Modelling female fertility traits in beef cattle using linear and non-linear models.

Science.gov (United States)

Naya, H; Peñagaricano, F; Urioste, J I

2017-06-01

Female fertility traits are key components of the profitability of beef cattle production. However, these traits are difficult and expensive to measure, particularly under extensive pastoral conditions, and consequently, fertility records are in general scarce and somehow incomplete. Moreover, fertility traits are usually dominated by the effects of herd-year environment, and it is generally assumed that relatively small margins are kept for genetic improvement. New ways of modelling genetic variation in these traits are needed. Inspired in the methodological developments made by Prof. Daniel Gianola and co-workers, we assayed linear (Gaussian), Poisson, probit (threshold), censored Poisson and censored Gaussian models to three different kinds of endpoints, namely calving success (CS), number of days from first calving (CD) and number of failed oestrus (FE). For models involving FE and CS, non-linear models overperformed their linear counterparts. For models derived from CD, linear versions displayed better adjustment than the non-linear counterparts. Non-linear models showed consistently higher estimates of heritability and repeatability in all cases (h 2  linear models; h 2  > 0.23 and r > 0.24, for non-linear models). While additive and permanent environment effects showed highly favourable correlations between all models (>0.789), consistency in selecting the 10% best sires showed important differences, mainly amongst the considered endpoints (FE, CS and CD). In consequence, endpoints should be considered as modelling different underlying genetic effects, with linear models more appropriate to describe CD and non-linear models better for FE and CS. © 2017 Blackwell Verlag GmbH.

A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer data.

Science.gov (United States)

Bertl, Johanna; Guo, Qianyun; Juul, Malene; Besenbacher, Søren; Nielsen, Morten Muhlig; Hornshøj, Henrik; Pedersen, Jakob Skou; Hobolth, Asger

2018-04-19

Detailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration. To take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures. We find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model

A real-time crash prediction model for the ramp vicinities of urban expressways

Directory of Open Access Journals (Sweden)

Moinul Hossain

2013-07-01

Full Text Available Ramp vicinities are arguably the known black-spots on urban expressways. There, while maintaining high speed, drivers need to respond to several complex events such as maneuvering, reading road signs, route planning and maintaining safe distance from other maneuvering vehicles simultaneously which demand higher level of cognitive response to ensure safety. Therefore, any additional discomfort caused by traffic dynamics may induce driving error resulting in a crash. This manuscript presents a methodology for identifying these dynamically forming hazardous traffic conditions near the ramp vicinities with high resolution real-time traffic flow data. It separates the ramp vicinities into four zones – upstream and downstream of entrance and exit ramps, and builds four separate real-time crash prediction models. Around two year (December 2007 to October 2009 crash data as well as their matching traffic sensor data from Shibuya 3 and Shinjuku 4 expressways under the jurisdiction of Tokyo Metropolitan Expressway Company Limited have been utilized for this research. Random multinomial logit, a forest of multinomial logit models, has been used to identify the most important variables. Finally, a real-time modeling method, Bayesian belief net (BBN, has been employed to build the four models using ramp flow, flow and congestion index in the upstream and flow and speed in the downstream of the ramp location as variables. The newly proposed models could predict 50%, 42%, 43% and 55% of the future crashes with around 10% false alarm for the downstream of entrance, downstream of exit, upstream of entrance and upstream of exit ramps respectively. The models can be utilized in combination with various traffic smoothing measures such as ramp metering, variable speed limit, warning messages through variable message signs, etc. to enhance safety near the ramp vicinities.

Unpaid overtime in the Netherlands : forward- or backward-looking incentives?

NARCIS (Netherlands)

Van der Meer, Peter H.; Wielers, Rudi

2015-01-01

The purpose of this paper is to test forward-looking incentives against backward-looking incentives. Design/methodology/approach ? Wage growth model to estimate forward-looking effects of unpaid overtime and a probit model of participation in unpaid overtime controlling for excessive pay to estimate

An Econometric Analysis of the Mexican Peso Crisis of 1994-1995 = 1994-1995 Meksika Pezo Krizi'nin Ekonometrik Bir Analizi

Directory of Open Access Journals (Sweden)

Mete FERİDUN

2007-01-01

Full Text Available This article aims at identifying the factors behind the Mexican Peso Crisis of 1994-1995 through building a probit model incorporating 20 monthly macroeconomic, political, and financial sector variables from 1970:1 - 1995:1. As a result of the probit regressions, strong evidence emerges that the significant variables are political instability, foreign exchange reserves, domestic credit/GDP, lending and deposit rate spread, national savings, and foreign direct investment/GDP. Evidence further indicates that the signs of the variables are mostly in line with our expectations, with the exception of inflation, bank reserves/bank assets, export growth, and lending and deposit rate spread.

Mixed multinomial logit model for out-of-home leisure activity choice

NARCIS (Netherlands)

Grigolon, A.B.; Kemperman, A.D.A.M.; Timmermans, H.J.P.

2013-01-01

This paper documents the design and results of a study on the factors influencing the choice of out-of-home leisure activities. Influencing factors seem related to socio-demographic characteristics, personal preferences, characteristics of the built environment and other aspects of the activities

Identifying Unknown Response Styles: A Latent-Class Bilinear Multinomial Logit Model

NARCIS (Netherlands)

J.M. van Rosmalen (Joost); H. van Herk (Hester); P.J.F. Groenen (Patrick)

2007-01-01

textabstractRespondents can vary significantly in the way they use rating scales. Specifically, respondents can exhibit varying degrees of response style, which threatens the validity of the responses. The purpose of this article is to investigate to what extent rating scale responses show response

Forecasting Macedonian Business Cycle Turning Points Using Qual Var Model

Directory of Open Access Journals (Sweden)

Petrovska Magdalena

2016-09-01

Full Text Available This paper aims at assessing the usefulness of leading indicators in business cycle research and forecast. Initially we test the predictive power of the economic sentiment indicator (ESI within a static probit model as a leading indicator, commonly perceived to be able to provide a reliable summary of the current economic conditions. We further proceed analyzing how well an extended set of indicators performs in forecasting turning points of the Macedonian business cycle by employing the Qual VAR approach of Dueker (2005. In continuation, we evaluate the quality of the selected indicators in pseudo-out-of-sample context. The results show that the use of survey-based indicators as a complement to macroeconomic data work satisfactory well in capturing the business cycle developments in Macedonia.

mixtools: An R Package for Analyzing Mixture Models

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Tatiana Benaglia

2009-10-01

Full Text Available The mixtools package for R provides a set of functions for analyzing a variety of finite mixture models. These functions include both traditional methods, such as EM algorithms for univariate and multivariate normal mixtures, and newer methods that reflect some recent research in finite mixture models. In the latter category, mixtools provides algorithms for estimating parameters in a wide range of different mixture-of-regression contexts, in multinomial mixtures such as those arising from discretizing continuous multivariate data, in nonparametric situations where the multivariate component densities are completely unspecified, and in semiparametric situations such as a univariate location mixture of symmetric but otherwise unspecified densities. Many of the algorithms of the mixtools package are EM algorithms or are based on EM-like ideas, so this article includes an overview of EM algorithms for finite mixture models.

CHAPTER 1

African Journals Online (AJOL)

Dr Olaleye

The Probit regression showed statistical significant (p<0.05) impact of contact and precautionary indices .... parameters of the estimated model are denoted as β. ... Where is the constant term and is the stochastic error term.

Factors influencing cassava - pulp fermentation period for gari ...

African Journals Online (AJOL)

Factors influencing cassava - pulp fermentation period for gari processing among ... Result of probit model analysis at 5% significance level shows an R value ... Marital status (2.236**) and respondents' cultural influences (1.960**) were ...

The Dynamics of Poverty and Vulnerability in Rural Ethiopia

African Journals Online (AJOL)

from the random effects probit model suggest that determinants of poverty status in rural Ethiopia ... 1 School of Agricultural Economics and Agribusiness, ... security policies, strategies and programs in the last two decades (FDRE,. 2004 ...

Relative efficiency of joint-model and full-conditional-specification multiple imputation when conditional models are compatible: The general location model.

Science.gov (United States)

Seaman, Shaun R; Hughes, Rachael A

2018-06-01

Estimating the parameters of a regression model of interest is complicated by missing data on the variables in that model. Multiple imputation is commonly used to handle these missing data. Joint model multiple imputation and full-conditional specification multiple imputation are known to yield imputed data with the same asymptotic distribution when the conditional models of full-conditional specification are compatible with that joint model. We show that this asymptotic equivalence of imputation distributions does not imply that joint model multiple imputation and full-conditional specification multiple imputation will also yield asymptotically equally efficient inference about the parameters of the model of interest, nor that they will be equally robust to misspecification of the joint model. When the conditional models used by full-conditional specification multiple imputation are linear, logistic and multinomial regressions, these are compatible with a restricted general location joint model. We show that multiple imputation using the restricted general location joint model can be substantially more asymptotically efficient than full-conditional specification multiple imputation, but this typically requires very strong associations between variables. When associations are weaker, the efficiency gain is small. Moreover, full-conditional specification multiple imputation is shown to be potentially much more robust than joint model multiple imputation using the restricted general location model to mispecification of that model when there is substantial missingness in the outcome variable.

Discrete choice experiments in pharmacy: a review of the literature.

Science.gov (United States)

Naik-Panvelkar, Pradnya; Armour, Carol; Saini, Bandana

2013-02-01

Discrete choice experiments (DCEs) have been widely used to elicit patient preferences for various healthcare services and interventions. The aim of our study was to conduct an in-depth scoping review of the literature and provide a current overview of the progressive application of DCEs within the field of pharmacy. Electronic databases (MEDLINE, EMBASE, SCOPUS, ECONLIT) were searched (January 1990-August 2011) to identify published English language studies using DCEs within the pharmacy context. Data were abstracted with respect to DCE methodology and application to pharmacy. Our search identified 12 studies. The DCE methodology was utilised to elicit preferences for different aspects of pharmacy products, therapy or services. Preferences were elicited from either patients or pharmacists, with just two studies incorporating the views of both. Most reviewed studies examined preferences for process-related or provider-related aspects with a lesser focus on health outcomes. Monetary attributes were considered to be important by most patients and pharmacists in the studies reviewed. Logit, probit or multinomial logit models were most commonly employed for estimation. Our study showed that the pharmacy profession has adopted the DCE methodology consistent with the general health DCEs although the number of studies is quite limited. Future studies need to examine preferences of both patients and providers for particular products or disease-state management services. Incorporation of health outcome attributes in the design, testing for external validity and the incorporation of DCE results in economic evaluation framework to inform pharmacy policy remain important areas for future research. © 2012 The Authors. IJPP © 2012 Royal Pharmaceutical Society.

A Simulation-Based Dynamic Stochastic Route Choice Model for Evacuation

Directory of Open Access Journals (Sweden)

Xing Zhao

2012-01-01

Full Text Available This paper establishes a dynamic stochastic route choice model for evacuation to simulate the propagation process of traffic flow and estimate the stochastic route choice under evacuation situations. The model contains a lane-group-based cell transmission model (CTM which sets different traffic capacities for links with different turning movements to flow out in an evacuation situation, an actual impedance model which is to obtain the impedance of each route in time units at each time interval and a stochastic route choice model according to the probit-based stochastic user equilibrium. In this model, vehicles loading at each origin at each time interval are assumed to choose an evacuation route under determinate road network, signal design, and OD demand. As a case study, the proposed model is validated on the network nearby Nanjing Olympic Center after the opening ceremony of the 10th National Games of the People's Republic of China. The traffic volumes and clearing time at five exit points of the evacuation zone are calculated by the model to compare with survey data. The results show that this model can appropriately simulate the dynamic route choice and evolution process of the traffic flow on the network in an evacuation situation.

CSR and Company's Stock Price. A Comparative Evidence from Bucharest Stock Exchange

Directory of Open Access Journals (Sweden)

Adina Dornean

2017-05-01

Full Text Available This paper aims at analysing the relationship between Corporate Social Responsibility (CSR and stock price for the companies listed on Bucharest Stock Exchange (BSE in 2015, comparing with the results obtained for 2014. This study investigates the differences in the market stock price (and other market variables, such as dividends and stock return of companies that show CSR compared with those that do not. For this purpose we will use three statistical techniques: discriminant analysis, probit analysis model and logistic regression. There is no significant difference between the prediction ability of the models, in the context in which probit model and logistic regression have and average correct classification of 70.29%, while discriminant analysis records 71.62%. Our analysis highlighted that stock return has a significant impact on CSR activities of a company. Moreover, all discriminants have a positive impact on CSR.

extent of use of ict by fish farmers in isoko agricultural zone of delta ...

African Journals Online (AJOL)

Mr. TONY A

Descriptive statistics and binary probit model were the tools of analyses. ... extension services and affordable credit will help in promoting the ... Depending on the extension approach, farmers should either pay totally or partially the extension ...

Determinants of Fisher's Choice of Fishing Activity along the Volta ...

African Journals Online (AJOL)

Determinants of Fisher's Choice of Fishing Activity along the Volta Lake in Yeji ... The analysis was done using the Ordered Probit Model and descriptive statistics. ... economic growth, reduce poverty and ensure household food security in Yeji.

Think twice before you book? Modelling the choice of public vs private dentist in a choice experiment.

Science.gov (United States)

Kiiskinen, Urpo; Suominen-Taipale, Anna Liisa; Cairns, John

2010-06-01

This study concerns the choice of primary dental service provider by consumers. If the health service delivery system allows individuals to choose between public-care providers or if complementary private services are available, it is typically assumed that utilisation is a three-stage decision process. The patient first makes a decision to seek care, and then chooses the service provider. The final stage, involving decisions over the amount and form of treatment, is not considered here. The paper reports a discrete choice experiment (DCE) designed to evaluate attributes affecting individuals' choice of dental-care provider. The feasibility of the DCE approach in modelling consumers' choice in the context of non-acute need for dental care is assessed. The aim is to test whether a separate two-stage logit, a multinomial logit, or a nested logit best fits the choice process of consumers. A nested logit model of indirect utility functions is estimated and inclusive value (IV) constraints are tested for modelling implications. The results show that non-trading behaviour has an impact on the choice of appropriate modelling technique, but is to some extent dependent on the choice of scenarios offered. It is concluded that for traders multinomial logit is appropriate, whereas for non-traders and on average the nested logit is the method supported by the analyses. The consistent finding in all subgroup analyses is that the traditional two-stage decision process is found to be implausible in the context of consumer's choice of dental-care provider.

Exits from Temporary Jobs in Europe: A Competing Risks Analysis

DEFF Research Database (Denmark)

D'Addio, Anna Christina; Rosholm, Michael

2005-01-01

We study transitions out of temporary jobs using the waves 1994-1999 of the European Community Household Panel applying a discrete time duration model. Specifically, we use a multinomial logitmodel distinguishing between exits into permanent employment and non-employment. Two different specificat......We study transitions out of temporary jobs using the waves 1994-1999 of the European Community Household Panel applying a discrete time duration model. Specifically, we use a multinomial logitmodel distinguishing between exits into permanent employment and non-employment. Two different...

Econometric analyses of microfinance credit group formation, contractual risks and welfare impacts in Northern Ethiopia

NARCIS (Netherlands)

Berhane Tesfay, G.

2009-01-01

Key words
Microfinance, joint liability, contractual risk, group formation, risk-matching, impact evaluation, Panel data econometrics, dynamic panel probit, trend models, fixed-effects, composite counterfactuals, propensity score matching, farm households, Ethiopia.

Lack of

Socio-economic impacts and determinants of parasitic weed infestation in rainfed rice systems of sub-Saharan Africa

NARCIS (Netherlands)

N'cho, A.S.

2014-01-01

Keywords: rice; weed; weed management practices, adoption, impact, parasitic weeds; Rhamphicarpa fistulosa; Striga asiatica; Striga hermonthica, double hurdle model; multivariate probit, productivity, stochastic frontier analysis, data

The Cognitive Processes Underlying Event-Based Prospective Memory In School Age Children and Young Adults: A Formal Model-Based Study

OpenAIRE

Smith, Rebekah E.; Bayen, Ute Johanna; Martin, Claudia

2010-01-01

Fifty 7-year-olds (29 female), 53 10-year-olds (29 female), and 36 young adults (19 female), performed a computerized event-based prospective memory task. All three groups differed significantly in prospective memory performance with adults showing the best performance and 7-year-olds the poorest performance. We used a formal multinomial process tree model of event-based prospective memory to decompose age differences in cognitive processes that jointly contribute to prospective memory perfor...

Modeling Stochastic Route Choice Behaviors with Equivalent Impedance

Directory of Open Access Journals (Sweden)

Jun Li

2015-01-01

Full Text Available A Logit-based route choice model is proposed to address the overlapping and scaling problems in the traditional multinomial Logit model. The nonoverlapping links are defined as a subnetwork, and its equivalent impedance is explicitly calculated in order to simply network analyzing. The overlapping links are repeatedly merged into subnetworks with Logit-based equivalent travel costs. The choice set at each intersection comprises only the virtual equivalent route without overlapping. In order to capture heterogeneity in perception errors of different sizes of networks, different scale parameters are assigned to subnetworks and they are linked to the topological relationships to avoid estimation burden. The proposed model provides an alternative method to model the stochastic route choice behaviors without the overlapping and scaling problems, and it still maintains the simple and closed-form expression from the MNL model. A link-based loading algorithm based on Dial’s algorithm is proposed to obviate route enumeration and it is suitable to be applied on large-scale networks. Finally a comparison between the proposed model and other route choice models is given by numerical examples.

Balance in competition in Dutch soccer

NARCIS (Netherlands)

Koning, Ruud H.

2000-01-01

In this paper, we estimate an ordered probit model for soccer results in The Netherlands. The result of a game is assumed to be determined by home advantage and quality differences of the opposing teams. The parameters of the model are used to assess whether competitive balance in Dutch professional

Political instability and country risk : new evidence

NARCIS (Netherlands)

DeHaan, J; Siermann, CLJ; VanLubek, E

1997-01-01

This note presents new estimates of a probit model for the debt rescheduling, using a sample of 65 countries over the period 1984-93. Apart from economic variables, a whole range of indicators for political instability are included in the model as explanatory variables. It turns out, that none is

Generalized outcome-based strategy classification: comparing deterministic and probabilistic choice models.

Science.gov (United States)

Hilbig, Benjamin E; Moshagen, Morten

2014-12-01

Model comparisons are a vital tool for disentangling which of several strategies a decision maker may have used--that is, which cognitive processes may have governed observable choice behavior. However, previous methodological approaches have been limited to models (i.e., decision strategies) with deterministic choice rules. As such, psychologically plausible choice models--such as evidence-accumulation and connectionist models--that entail probabilistic choice predictions could not be considered appropriately. To overcome this limitation, we propose a generalization of Bröder and Schiffer's (Journal of Behavioral Decision Making, 19, 361-380, 2003) choice-based classification method, relying on (1) parametric order constraints in the multinomial processing tree framework to implement probabilistic models and (2) minimum description length for model comparison. The advantages of the generalized approach are demonstrated through recovery simulations and an experiment. In explaining previous methods and our generalization, we maintain a nontechnical focus--so as to provide a practical guide for comparing both deterministic and probabilistic choice models.

Incidence and Severity of Poverty among Oyan Lake Host ...

African Journals Online (AJOL)

It is recommended that development oriented policies aimed at discouraging early marriages, polygamy, having improved family planning, housing scheme, education and access to loans should be given consideration for these communities. Keywords: Host communities, Poverty, Household, Multivariate Probit Model, ...

Socio-Economic Factors Affecting Adoption of Modern Information and Communication Technology by Farmers in India: Analysis Using Multivariate Probit Model

Science.gov (United States)

Mittal, Surabhi; Mehar, Mamta

2016-01-01

Purpose: The paper analyzes factors that affect the likelihood of adoption of different agriculture-related information sources by farmers. Design/Methodology/Approach: The paper links the theoretical understanding of the existing multiple sources of information that farmers use, with the empirical model to analyze the factors that affect the…

Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling.

Science.gov (United States)

Lorenz, Alyson; Dhingra, Radhika; Chang, Howard H; Bisanzio, Donal; Liu, Yang; Remais, Justin V

2014-01-01

Extrapolating landscape regression models for use in assessing vector-borne disease risk and other applications requires thoughtful evaluation of fundamental model choice issues. To examine implications of such choices, an analysis was conducted to explore the extent to which disparate landscape models agree in their epidemiological and entomological risk predictions when extrapolated to new regions. Agreement between six literature-drawn landscape models was examined by comparing predicted county-level distributions of either Lyme disease or Ixodes scapularis vector using Spearman ranked correlation. AUC analyses and multinomial logistic regression were used to assess the ability of these extrapolated landscape models to predict observed national data. Three models based on measures of vegetation, habitat patch characteristics, and herbaceous landcover emerged as effective predictors of observed disease and vector distribution. An ensemble model containing these three models improved precision and predictive ability over individual models. A priori assessment of qualitative model characteristics effectively identified models that subsequently emerged as better predictors in quantitative analysis. Both a methodology for quantitative model comparison and a checklist for qualitative assessment of candidate models for extrapolation are provided; both tools aim to improve collaboration between those producing models and those interested in applying them to new areas and research questions.

Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling.

Directory of Open Access Journals (Sweden)

Alyson Lorenz

Full Text Available Extrapolating landscape regression models for use in assessing vector-borne disease risk and other applications requires thoughtful evaluation of fundamental model choice issues. To examine implications of such choices, an analysis was conducted to explore the extent to which disparate landscape models agree in their epidemiological and entomological risk predictions when extrapolated to new regions. Agreement between six literature-drawn landscape models was examined by comparing predicted county-level distributions of either Lyme disease or Ixodes scapularis vector using Spearman ranked correlation. AUC analyses and multinomial logistic regression were used to assess the ability of these extrapolated landscape models to predict observed national data. Three models based on measures of vegetation, habitat patch characteristics, and herbaceous landcover emerged as effective predictors of observed disease and vector distribution. An ensemble model containing these three models improved precision and predictive ability over individual models. A priori assessment of qualitative model characteristics effectively identified models that subsequently emerged as better predictors in quantitative analysis. Both a methodology for quantitative model comparison and a checklist for qualitative assessment of candidate models for extrapolation are provided; both tools aim to improve collaboration between those producing models and those interested in applying them to new areas and research questions.

Don’t Work, Work at Home, or Commute? Discrete Choice Models of the Decision for San Francisco Bay Area Residents

OpenAIRE

Ory, D T; Mokhtarian, Patricia L

2005-01-01

Using socio-demographic, personality, and attitudinal data from 1,680 residents of the San Francisco Bay Area, we develop and estimate binary, multinomial, and nested logit models of the choice to work or not, whether or not to work at home, and whether to commute all of the time or some of the time (either by only working part time, or by working a compressed work week, or by telecommuting some of the time). To our knowledge, these are the first models of all these choices simultaneously. Th...

A measurement theory of illusory conjunctions.

Science.gov (United States)

Prinzmetal, William; Ivry, Richard B; Beck, Diane; Shimizu, Naomi

2002-04-01

Illusory conjunctions refer to the incorrect perceptual combination of correctly perceived features, such as color and shape. Research on the phenomenon has been hampered by the lack of a measurement theory that accounts for guessing features, as well as the incorrect combination of correctly perceived features. Recently, several investigators have suggested using multinomial models as a tool for measuring feature integration. The authors examined the adequacy of these models in 2 experiments by testing whether model parameters reflect changes in stimulus factors. In a third experiment, confidence ratings were used as a tool for testing the model. Multinomial models accurately reflected both variations in stimulus factors and observers' trial-by-trial confidence ratings.

An Ordered Regression Model to Predict Transit Passengers’ Behavioural Intentions

Energy Technology Data Exchange (ETDEWEB)

Oña, J. de; Oña, R. de; Eboli, L.; Forciniti, C.; Mazzulla, G.

2016-07-01

Passengers’ behavioural intentions after experiencing transit services can be viewed as signals that show if a customer continues to utilise a company’s service. Users’ behavioural intentions can depend on a series of aspects that are difficult to measure directly. More recently, transit passengers’ behavioural intentions have been just considered together with the concepts of service quality and customer satisfaction. Due to the characteristics of the ways for evaluating passengers’ behavioural intentions, service quality and customer satisfaction, we retain that this kind of issue could be analysed also by applying ordered regression models. This work aims to propose just an ordered probit model for analysing service quality factors that can influence passengers’ behavioural intentions towards the use of transit services. The case study is the LRT of Seville (Spain), where a survey was conducted in order to collect the opinions of the passengers about the existing transit service, and to have a measure of the aspects that can influence the intentions of the users to continue using the transit service in the future. (Author)

Incorporating Latent Variables into Discrete Choice Models - A Simultaneous Estimation Approach Using SEM Software

Directory of Open Access Journals (Sweden)

Dirk Temme

2008-12-01

Full Text Available Integrated choice and latent variable (ICLV models represent a promising new class of models which merge classic choice models with the structural equation approach (SEM for latent variables. Despite their conceptual appeal, applications of ICLV models in marketing remain rare. We extend previous ICLV applications by first estimating a multinomial choice model and, second, by estimating hierarchical relations between latent variables. An empirical study on travel mode choice clearly demonstrates the value of ICLV models to enhance the understanding of choice processes. In addition to the usually studied directly observable variables such as travel time, we show how abstract motivations such as power and hedonism as well as attitudes such as a desire for flexibility impact on travel mode choice. Furthermore, we show that it is possible to estimate such a complex ICLV model with the widely available structural equation modeling package Mplus. This finding is likely to encourage more widespread application of this appealing model class in the marketing field.

Modelling Preference Heterogeneity for Theatre Tickets

DEFF Research Database (Denmark)

Baldin, Andrea; Bille, Trine

This paper analyzes the behavioural choice for theatre tickets using a rich dataset for 2010-2013 from the sale system of the Royal Danish National Theatre. A consumer who decides to attend a theater production faces multiple sources of price variation that depends on: socio-economic characterist......This paper analyzes the behavioural choice for theatre tickets using a rich dataset for 2010-2013 from the sale system of the Royal Danish National Theatre. A consumer who decides to attend a theater production faces multiple sources of price variation that depends on: socio......-economic characteristics, quality of the seat, day of the performance and timing of purchase. Except for the first case, factors of price differentiation involves a choice by the consumer among different ticket alternatives. Two modelling approaches, namely multinomial logit (with socio-demographic characteristics......) and latent class are proposed in order to model ticket purchase behaviour. These models allow us explicitly to take into account consumers' preference heterogeneity with respect to the attributes associated to each ticket alternative In addition, the distribution of the willingness-to-pay (WTP) of choice...

The Dirichet-Multinomial model for multivariate randomized response data and small samples

NARCIS (Netherlands)

Avetisyan, Marianna; Fox, Gerardus J.A.

2012-01-01

In survey sampling the randomized response (RR) technique can be used to obtain truthful answers to sensitive questions. Although the individual answers are masked due to the RR technique, individual (sensitive) response rates can be estimated when observing multivariate response data. The

A Multinomial Model of Fertility Choice and Offspring Sex-Ratios in India

OpenAIRE

Rubiana Chamarbagwala; Martin Ranger

2007-01-01

Fertility decline in developing countries may have unexpected demographic consequences. Although lower fertility improves nutrition, health, and human capital investments for surviving children, little is known about the relationship between fertility outcomes and female-male offspring sex-ratios. Particularly in countries with a cultural preference for sons, like India and China, fertility decline may deteriorate the already imbalanced sex-ratios. We use the fertility histories of over 90,00...

Grades as Information

Science.gov (United States)

Grant, Darren

2007-01-01

We determine how much observed student performance in microeconomics principles can be attributed, inferentially, to three kinds of student academic "productivity," the instructor, demographics, and unmeasurables. The empirical approach utilizes an ordered probit model that relates student performance in micro to grades in prior…

A comparison between Daphnia pulex and Hydra vulgaris as ...

African Journals Online (AJOL)

2017-04-02

Apr 2, 2017 ... dependence thereon, has led not only to valuable products ... effects, estimating environmental risk based on measured ... The LC50-values were statistically determined using the EPA Probit Analysis Model ..... and organic contaminants. ..... green algae on the response of Hydra viridissima (Pallas 1776).

Factors influencing the stay-exit intention of small livestock farmers

NARCIS (Netherlands)

Carter-Leal, Luis M.; Oude-Lansink, Alfons; Saatkamp, Helmut

2018-01-01

This study analyses the factors driving the stay-exit intention of small livestock farmers located in southern Chile. Technical, economic, and social characteristics from 212 farmers were included in this study. Through an empirical probit model we identified the variables that should be considered

Getting stuck, falling behind or moving forward

DEFF Research Database (Denmark)

Walelign, Solomon Zena

2017-01-01

Research on household livelihood dynamics is central to rural poverty reduction. In this paper, we adopt a three-wave panel dataset to explore the persistence of and transitions in household livelihoods in three districts of Nepal using duration and dynamic probit models. The results demonstrate ...

Airport terminal choice model

Directory of Open Access Journals (Sweden)

Claudia Helena Muñoz-Hoyos

2014-01-01

Full Text Available La mayoría de los estudios del modo aéreo han tratado individua lmente los aspectos de tarifas, demoras y demás variables inher entes a este medio de transporte, así como la elección del modo aéreo f rente a otros modos, pero poco se ha hecho por modelar cómo un viajero elige un aeropuerto entre dos opciones disponibles en una gran ciudad. En la actualidad un pasajero que parte de la ciudad de Medellín - Colombia a algunos destinos nacionales, tiene la opción de v iajar por alguno de los dos aeropuertos, el José María Córdova (JMC o el Enriqu e Olaya Herrera (EOH; esta investigación presenta los resultad os de una encuesta de preferencias declaradas en un experimento de elecci ón discreta, y partiendo de esto se obtiene un modelo por desti no; para cada uno de estos se hallaron modelos logit multinomial y logit mixt o; en cada trayecto evaluado se eligió el logit multinomial com o el mejor.

Vocational and General Education of Girls and Boys in Tunisia: The Effects of Income and Parental Education

Directory of Open Access Journals (Sweden)

Mohamed Siala

2014-05-01

Full Text Available Throughout Tunisia, basic education is compulsory. Children are required to enroll for at least 9 years from age 6. This paper examines gender differences in education choice of upper basic education of youths aged 15–24 in Tunisia. To investigate the factors that influence an individual’s choice between vocational education, general education (secondary and high education and leaving school, the paper estimates a multinomial probit model of education choice. We focus on the impact of household income, parental education, sector of economic activity of father, household size, urban location and region of residence on investments in children. These issues are addressed using data from the 2010 National PopulationEmployment Survey that provided information on educational attainment and vocational training of more than 55,000 youths aged 15-24. The findings of this paper suggest that there are gender differences in education choice. Increases in permanent income contribute more to the probabilities of the two types of education of girls than of boys. Parental education has a positive significant effect on their attitudes towards children education and the impact of mother’s higher education was more important for the education of boys than of girls. While, father’s coefficient estimates show the relative benefit to girls general education. Children whose fathers work in agriculture are at disadvantage. The negative effect on girls’ education was larger than on boys’ at the two streams of education. The coefficient estimates on the manufacturing sector increase the probabilities of receiving general education and decrease the probabilities of undertaking vocational education for both girls and boys.

The Smoothed Dirichlet Distribution: Understanding Cross-Entropy Ranking in Information Retrieval

National Research Council Canada - National Science Library

Nallapati, Ramesh

2006-01-01

.... Another related and interesting observation is that the naive Bayes model for text classification uses the same multinomial distribution to model documents but in contrast, employs document-log...

Firm-Level Determinants of Exporting Behaviour: Evidence from ...

African Journals Online (AJOL)

This paper uses firm-level panel data to investigate the exporting behaviour of the Kenyan manufacturing firms. Using probit and tobit regression models, the results obtained show that factors determining the decision to export are different from those affecting the share exported. Likewise, factors determining exporting ...

Determinants of firms' investment behaviour : a multilevel approach

NARCIS (Netherlands)

Farla, K.

2013-01-01

This paper investigates micro and macro determinants of firms' investment behaviour using firm data from 101 developing and emerging economies. A substantial number of firms in our sample does not invest in fixed capital or invests little relative to sales revenue. Using a multilevel probit model we

Determinants of farmers' perception of land degradation and ...

African Journals Online (AJOL)

The study investigated farmers' perception of land degradation, and adoption of soil conservation practices using a two-stage decision making process. The data for the study were collected with the aid of structured questionnaire and analyzed with descriptive analysis and simultaneous probit model. The results show ...

Explaining Preferences and Actual Involvement in Self-Employment: New Insights into the Role of Gender

NARCIS (Netherlands)

I. Verheul (Ingrid); A.R. Thurik (Roy); I. Grilo (Isabel)

2008-01-01

textabstractThis paper investigates why women’s self-employment rates are consistently lower than those of men. It has three focal points. It discriminates between the preference for self-employment and actual involvement in self-employment using a two (probit) equation model. It makes a systematic

Credit Default Risk and its Determinants of Microfinance Industry in ...

African Journals Online (AJOL)

user

limitations prevailed in the selected 6 MFIs in Ethiopia schemed by determining the ... the probit model show that education, income, loan supervision, suitability of repayment ..... of Ethiopia as Large, Medium, and Small MFIs. ..... /sigma. 0.5177089. 0.0467682. *significant at 1%. ***significant at 10% .... businesses default.

Land degradation and adoption of soil conservation technologies ...

African Journals Online (AJOL)

The study investigates the causes of land degradation, and adoption of soil conservation practices using a two-stage decision making process. The data for the study were collected with the aid of structured questionnaire and analyzed with descriptive analysis, difference regression equation and simultaneous probit model.

Factors leading to inflation targeting : The impact of adoption

NARCIS (Netherlands)

Samarina, Anna; Sturm, Jan-Egbert

2013-01-01

This paper examines how the analysis of inflation targeting (IT) adoption is affected by allowing for a structural change after adoption, using panel probit models for 60 countries over the period 1985-2008. Our findings suggest that there is a structural change after IT adoption. Including the

Factors leading to inflation targeting - the impact of adoption

NARCIS (Netherlands)

Samarina, Anna; Sturm, Jan-Egbert

2013-01-01

This paper examines how the analysis of inflation targeting (IT) adoption is affected by the choice of the analyzed period. We test whether country characteristics influence the decision to apply IT differently before and after its adoption, using panel probit models for 60 countries over the period

Education choices in Ethiopia: what determines whether poor households send their children to school?

NARCIS (Netherlands)

Woldehanna, T.; Mekonnen, A.; Jones, N.

2008-01-01

The paper uses data from a 2002 survey of 1000 rural and urban households with eight-year old children sampled from food insecure communities in Tigray, Amhara, Oromia, SNNP and Addis Ababa Regional States. Using a probit regression model, we investigated external factors associated with child

Video games playing: A substitute for cultural consumptions?

DEFF Research Database (Denmark)

Borowiecki, Karol Jan; Prieto-Rodriguez, Juan

2015-01-01

This article provides an applied investigation of video game usage. Using data for Spain, we estimate zero-inflated ordered probit models to control for an excess of zeros in our ordinal dependent variable. We find that the probability of game playing increases with the consumption of other...

EXTENDING THE DEEP PACKET INSPECTION MODEL TO THE GCC/MENA REGION

Directory of Open Access Journals (Sweden)

Alfred H. Miller

2013-12-01

Full Text Available This study seeks to explore extending the technology acceptance model (DPAM from a 2011 quantitative study—Modeling Intention to Use Deep Packet Inspection Technology in the United Arab Emirates, to the cyber security practitioner community of the Gulf Cooperation Council (GCC and greater Middle East North Africa (MENA Region. Analysis of regression between independent variable model factors of computer self efficacy, attitude toward ICT, perceived usefulness of ecommerce, intention to use ecommerce, societal trust and Internet filtration toward the dependent variable intention to use deep packet inspection, to determine parsimony, using confirmatory factor analysis (CFA, multinomial regression to assess correlation of independent and dependent variables, and assessment of the cross-suitability of DPAM across the MENA/GCC states through a MANOVA assessment. A qualitative component of the instrument enables collection of data about specific hardware and software deployed for deep packet inspection and cyber security systems.

Modelos de predição para sobrevivência de plantas de Eucalyptus grandis Prediction models of Eucalyptus grandis plant survival

Directory of Open Access Journals (Sweden)

Telde Natel Custódio

2009-01-01

Full Text Available Objetivou-se com este trabalho comparar modelos de predição de plantas sobreviventes de Eucalyptus grandis. Utilizaram-se os seguintes modelos: modelo linear misto com os dados transformados, utilizando-se as transformações angular e BOX-COX; modelo linear generalizado misto com distribuição binomial e funções de ligação logística, probit e complemento log-log; modelo linear generalizado misto com distribuição Poisson e função de ligação logarítmica. Os dados são provenientes de um experimento em blocos ao acaso, para avaliação de progênies maternas de Eucalyptus grandis, aos 5 anos de idade, em que a variável resposta são plantas sobreviventes. Para comparação dos efeitos entre os modelos foram estimadas as correlações de Spearman e aplicado o teste de permutação de Fisher. Foi possível concluir que, o modelo linear generalizado misto com distribuição Poisson e função de ligação logarítmica se ajustou mal aos dados e que as estimativas para os efeitos fixos e predição para os efeitos aleatórios, não se diferenciaram entre os demais modelos estudados.The objective of this work was to compare models for prediction of the survival of plants of Eucalyptus grandis. The following models were used: linear mixed model with the transformed data, by utilizing the angular transformations and BOX-COX; generalized linear mixed model with binomial distribution and logistic functions, probit and complement log-log links; generalized linear mixed model with Poisson distribution and logarithmic link function. The data came from a randomized block experiment for evaluation of Eucalyptus grandis maternal progenies at five years old, in which the variable response are surviving plants. For comparison of the effects among the models the correlations of Spearman were estimated and the test of permutation of Fisher was applied. It was possible to conclude that: the generalized linear mixed model with Poisson distribution and

Still under the ancestors' shadow? Ancestor worship and family formation in contemporary China

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Anning Hu

2018-01-01

Full Text Available Background: Ancestor worship in China used to be an indispensable component of marriage and family life because it fostered an orientation toward perpetuating the family line. However, whether or not ancestor worship still matters in contemporary China is an open question. Objective: This article presents a comprehensive study of the association between ancestor worship practices and 1 the timing of transition to first marriage, 2 the pattern of childbearing, and 3 the orientation toward son preference. Methods: Drawing on the adult sample from the Chinese Family Panel Studies 2010, several multivariate models (Cox proportional hazard model, probit regression model, negative binomial regression models, and ordered probit model were fitted, corresponding to different types of outcome. Results: All else being equal, involvement in ancestor worship practices is correlated with 1 an early transition to marriage, 2 a larger number of children, 3 a higher probability of having at least one son, and 4 a larger number of sons. Conclusions: The relevance of the kinship tradition to family formation persists in contemporary China and has not faded away. Contribution: By highlighting the demographic implications of ancestor worship, this study illustrates the ongoing connection between culture and demography.

Global innovation networks and university-firm interactions: an exploratory survey analysis

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Gustavo Britto

2015-02-01

Full Text Available The literature on Global Innovation Networks has contributed to identify changes in the innovation activities of multinational corporations. Although university-firm interactions are seen as an important factor for the emergence of GINs, their role has received limited attention. This paper aims to fill this gap in two ways. First, it carries out an exploratory analysis of an original survey dataset, of firms in three industrial sectors from nine developed and developing countries. Second, the paper analyses whether the role of universities in global innovation networks is related to national systems of innovation with varying degrees of maturity. Multiple correspondence analysis and a Probit model are used to establish the relevance of key factors in driving GINs. The results identify distinctive profiles constructed mainly according to firm characteristics, but reflecting country specific patterns of association. The Probit model confirms that internationalization processes and the existence of local interactions substantially increase the probability of interactions with international institutions.

Remarques concernant la probité scientifique

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Roxana Iordache

2015-08-01

Full Text Available On constate, ces derniers temps, que certains jeunes enseignants expérimentent le plagiat sur des théories déjà publiées et généralement acceptées par les milieux scientifiques. D'autres enseignants faussent le contenu d'idées d'un travail, ou de l'autre, dans l'espoir d'augmenter leurs propres mérites. C' est avec stupeur que nous découvrons dans les Actes du IXe Colloque de linguistique Latine (Madrid, 1998, vol. 1, les pages de Mme Mirka Maraldi (Université de Bologne, intitulées «Concessive ut: parataxis, hypotaxis and correlation» (pages 487- 500. Mme M. Maraldi critique à la page 493 du volume supra mentionné notre étude sur le ut concessif du Jatin, en oubliant complètement d 'indiquer dans le texte et dans les notes de cette page, ainsi que de toutes les autres pages, le litre de notre étude, le lieu de parution et la page (ou les pages de nos soi-disant erreurs. On critique plusieurs fois «Jordache's analysis» - un syntagme vague, en fait! Précisons en mȇme temps que Mme M. Maraldi mentionne avec beaucoup de souci, à chaque page, les données des autres articles (Iieu d'apparition, page etc., etc, quoique, pour la plupart, il s'agisse de travaux peu importants pour le sujet en discussion.

Remarques concernant la probité scientifique

Directory of Open Access Journals (Sweden)

Roxana Iordache

2000-12-01

Full Text Available On constate, ces derniers temps, que certains jeunes enseignants expérimentent le plagiat sur des théories déjà publiées et généralement acceptées par les milieux scientifiques. D'autres enseignants faussent le contenu d'idees d'un travail, ou de l'autre, dans l'espoir d'augmenter leurs propres mérites.

Effectiveness of monetary and macroprudential shocks on consumer credit growth and volatility in Turkey

Directory of Open Access Journals (Sweden)

Meltem Gulenay Chadwick

2018-06-01

Full Text Available This paper proposes a panel VAR model to uncover the effect of monetary policy and macroprudential tightening probability on general purpose loans, housing loans, vehicle loans, credit cards and their respective volatilities in Turkey. To conduct our analysis, first, we compare a number of stochastic volatility models using our loan and credit card series in a formal Bayesian model comparison exercise, in order to determine the best volatility model for our series. Second we disclose the latent probability of macroprudential tightening from the binary information of policy episodes, using an instrumental variable probit model estimated by conditional maximum likelihood with heteroscedasticity robust standard errors. Lastly we estimate the dynamic impact of monetary policy and macroprudential measures using a panel VAR, incorporating the latent probability of tightening episodes, credit growth, industrial production growth, loan rates, inflation and credit growth volatilities into the endogenous system of equations. We conclude that macroprudential tightening is effective in dampening credit growth, credit growth volatility and reducing consumer price inflation. Besides, this effect is more prominent when macroprudential tools are administered in coordination with monetary policy. Keywords: Consumer loans, Monetary policy, Macroprudential policy, Stochastic volatility models, Credit growth volatility, IV probit model, Panel VAR model, JEL classification: C54, E44, E52

Classifying emotion in Twitter using Bayesian network

Science.gov (United States)

Surya Asriadie, Muhammad; Syahrul Mubarok, Mohamad; Adiwijaya

2018-03-01

Language is used to express not only facts, but also emotions. Emotions are noticeable from behavior up to the social media statuses written by a person. Analysis of emotions in a text is done in a variety of media such as Twitter. This paper studies classification of emotions on twitter using Bayesian network because of its ability to model uncertainty and relationships between features. The result is two models based on Bayesian network which are Full Bayesian Network (FBN) and Bayesian Network with Mood Indicator (BNM). FBN is a massive Bayesian network where each word is treated as a node. The study shows the method used to train FBN is not very effective to create the best model and performs worse compared to Naive Bayes. F1-score for FBN is 53.71%, while for Naive Bayes is 54.07%. BNM is proposed as an alternative method which is based on the improvement of Multinomial Naive Bayes and has much lower computational complexity compared to FBN. Even though it’s not better compared to FBN, the resulting model successfully improves the performance of Multinomial Naive Bayes. F1-Score for Multinomial Naive Bayes model is 51.49%, while for BNM is 52.14%.

Single toxin dose-response models revisited

Energy Technology Data Exchange (ETDEWEB)

Demidenko, Eugene, E-mail: eugened@dartmouth.edu [Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH03756 (United States); Glaholt, SP, E-mail: sglaholt@indiana.edu [Indiana University, School of Public & Environmental Affairs, Bloomington, IN47405 (United States); Department of Biological Sciences, Dartmouth College, Hanover, NH03755 (United States); Kyker-Snowman, E, E-mail: ek2002@wildcats.unh.edu [Department of Natural Resources and the Environment, University of New Hampshire, Durham, NH03824 (United States); Shaw, JR, E-mail: joeshaw@indiana.edu [Indiana University, School of Public & Environmental Affairs, Bloomington, IN47405 (United States); Chen, CY, E-mail: Celia.Y.Chen@dartmouth.edu [Department of Biological Sciences, Dartmouth College, Hanover, NH03755 (United States)

2017-01-01

The goal of this paper is to offer a rigorous analysis of the sigmoid shape single toxin dose-response relationship. The toxin efficacy function is introduced and four special points, including maximum toxin efficacy and inflection points, on the dose-response curve are defined. The special points define three phases of the toxin effect on mortality: (1) toxin concentrations smaller than the first inflection point or (2) larger then the second inflection point imply low mortality rate, and (3) concentrations between the first and the second inflection points imply high mortality rate. Probabilistic interpretation and mathematical analysis for each of the four models, Hill, logit, probit, and Weibull is provided. Two general model extensions are introduced: (1) the multi-target hit model that accounts for the existence of several vital receptors affected by the toxin, and (2) model with a nonzero mortality at zero concentration to account for natural mortality. Special attention is given to statistical estimation in the framework of the generalized linear model with the binomial dependent variable as the mortality count in each experiment, contrary to the widespread nonlinear regression treating the mortality rate as continuous variable. The models are illustrated using standard EPA Daphnia acute (48 h) toxicity tests with mortality as a function of NiCl or CuSO{sub 4} toxin. - Highlights: • The paper offers a rigorous study of a sigmoid dose-response relationship. • The concentration with highest mortality rate is rigorously defined. • A table with four special points for five morality curves is presented. • Two new sigmoid dose-response models have been introduced. • The generalized linear model is advocated for estimation of sigmoid dose-response relationship.

Dose-response regressions for algal growth and similar continuous endpoints: Calculation of effective concentrations

DEFF Research Database (Denmark)

Christensen, Erik R.; Kusk, Kresten Ole; Nyholm, Niels

2009-01-01

We derive equations for the effective concentration giving 10% inhibition (EC10) with 95% confidence limits for probit (log-normal), Weibull, and logistic dose -responsemodels on the basis of experimentally derived median effective concentrations (EC50s) and the curve slope at the central point (50......% inhibition). For illustration, data from closed, freshwater algal assays are analyzed using the green alga Pseudokirchneriella subcapitata with growth rate as the response parameter. Dose-response regressions for four test chemicals (tetraethylammonium bromide, musculamine, benzonitrile, and 4...... regression program with variance weighting and proper inverse estimation. The Weibull model provides the best fit to the data for all four chemicals. Predicted EC10s (95% confidence limits) from our derived equations are quite accurate; for example, with 4-4-(trifluoromethyl)phenoxy-phenol and the probit...

Reasons for U.S. Producer Selection of a Goat Enterprise

OpenAIRE

Dunn, Brittany; Nyaupane, Narayan; Gillispie, Jeffrey; McMillan, Kenneth

2014-01-01

This paper addresses 14 possible reasons why meat goat producers selected to engage in meat goat production, with results having implications for research, extension, and teaching efforts. A survey of meat goat producers was conducted. Reasons for entering meat goat production were assessed and analyzed using ordered probit models.

To drill or not to drill? An econometric analysis of US public opinion

International Nuclear Information System (INIS)

Mukherjee, Deep; Rahman, Mohammad Arshad

2016-01-01

Offshore drilling in the United States (US) has been the subject of public and political discourse due to multiple reasons which include economic impact, energy security, and environmental hazard. Consequently, several polls have been conducted over time to gauge public attitude towards offshore drilling. Nevertheless, the economic literature on this issue is sparse. This paper contributes to the literature and analyzes support for offshore drilling based on demographic, economic, social, belief, and shock (e.g. spill) factors. The data is taken from ten nationwide surveys conducted before, during and after the British Petroleum (BP) oil spill and analyzed within the framework of discrete choice model. The results from an ordinal probit model demonstrate that age, annual household income, affiliation to Republican Party, and residence in oil-rich states positively affect the probability of strong support and reduce the probability of strong opposition for offshore drilling. In contrast, the female gender, higher education, association to Democratic Party, and environmental concern affect opinion in opposite direction. Marginal effects show that belief about environmental consequences of drilling has the highest impact on opinion. Binary probit model also yields a similar result and suggests that BP oil disaster resulted in a transient decrease in support for offshore drilling. - Highlights: •US public opinion on offshore drilling is analyzed based on ten national polls. •Ordinal and binary probit models are utilized to identify the underlying factors that shape public opinion. •Belief about environmental cost of drilling and educational attainment have the highest negative impact on opinion. •Age, income, affiliation to Republican party and oil-rich states positively affect support for drilling. •BP oil spill resulted in a transient decrease in support for offshore drilling.

Spatial occupancy models for large data sets

Science.gov (United States)

Johnson, Devin S.; Conn, Paul B.; Hooten, Mevin B.; Ray, Justina C.; Pond, Bruce A.

2013-01-01

Since its development, occupancy modeling has become a popular and useful tool for ecologists wishing to learn about the dynamics of species occurrence over time and space. Such models require presence–absence data to be collected at spatially indexed survey units. However, only recently have researchers recognized the need to correct for spatially induced overdisperison by explicitly accounting for spatial autocorrelation in occupancy probability. Previous efforts to incorporate such autocorrelation have largely focused on logit-normal formulations for occupancy, with spatial autocorrelation induced by a random effect within a hierarchical modeling framework. Although useful, computational time generally limits such an approach to relatively small data sets, and there are often problems with algorithm instability, yielding unsatisfactory results. Further, recent research has revealed a hidden form of multicollinearity in such applications, which may lead to parameter bias if not explicitly addressed. Combining several techniques, we present a unifying hierarchical spatial occupancy model specification that is particularly effective over large spatial extents. This approach employs a probit mixture framework for occupancy and can easily accommodate a reduced-dimensional spatial process to resolve issues with multicollinearity and spatial confounding while improving algorithm convergence. Using open-source software, we demonstrate this new model specification using a case study involving occupancy of caribou (Rangifer tarandus) over a set of 1080 survey units spanning a large contiguous region (108 000 km2) in northern Ontario, Canada. Overall, the combination of a more efficient specification and open-source software allows for a facile and stable implementation of spatial occupancy models for large data sets.

Exploring National Concerted Practices in an Open Small Economy : What Does the Change in the Competition Law in the Netherlands Reveal?

NARCIS (Netherlands)

Ozbugday, F.C.

2011-01-01

The present study examines the impact of several industry characteristics on the propensity to collude using a dataset on the existence of collusion across Dutch industries during the late 1990s and early 2000s. The results of the Probit model with sample selection indicate that our sample of Dutch

A multivariate analysis of factors affecting adoption of improved ...

African Journals Online (AJOL)

This paper analyzes the synergies/tradeoffs involved in the adoption of improved varieties of multiple crops in the mixed crop-livestock production systems of the highlands of Ethiopia A multivariate probit (MVP) model involving a system of four equations for the adoption decision of improved varieties of barley, potatoes, ...

Gender differences, HIV risk perception and condom use

NARCIS (Netherlands)

Lammers, J.; van Wijnbergen, S.; Willebrands, D.

This paper analyzes how different types of HIV-knowledge influence condom use across the sexes. The empirical work is based on a household survey conducted among 1,979 households of a representative group of market persons in Lagos in 2008. Last time condom use is analyzed based on a Probit model

Scientific Productivity and Academic Promotion: A Study on French and Italian Physicists. NBER Working Paper No. 16341

Science.gov (United States)

Lissoni, Francesco; Mairesse, Jacques; Montobbio, Fabio; Pezzoni, Michele

2010-01-01

The paper examines the determinants of scientific productivity (number of articles and journals' impact factor) for a panel of about 3600 French and Italian academic physicists active in 2004-05. Endogeneity problems concerning promotion and productivity are addressed by specifying a generalized Tobit model, in which a selection probit equation…

Clustering disaggregated load profiles using a Dirichlet process mixture model

International Nuclear Information System (INIS)

Granell, Ramon; Axon, Colin J.; Wallom, David C.H.

2015-01-01

Highlights: • We show that the Dirichlet process mixture model is scaleable. • Our model does not require the number of clusters as an input. • Our model creates clusters only by the features of the demand profiles. • We have used both residential and commercial data sets. - Abstract: The increasing availability of substantial quantities of power-use data in both the residential and commercial sectors raises the possibility of mining the data to the advantage of both consumers and network operations. We present a Bayesian non-parametric model to cluster load profiles from households and business premises. Evaluators show that our model performs as well as other popular clustering methods, but unlike most other methods it does not require the number of clusters to be predetermined by the user. We used the so-called ‘Chinese restaurant process’ method to solve the model, making use of the Dirichlet-multinomial distribution. The number of clusters grew logarithmically with the quantity of data, making the technique suitable for scaling to large data sets. We were able to show that the model could distinguish features such as the nationality, household size, and type of dwelling between the cluster memberships

An imprecise Dirichlet model for Bayesian analysis of failure data including right-censored observations

International Nuclear Information System (INIS)

Coolen, F.P.A.

1997-01-01

This paper is intended to make researchers in reliability theory aware of a recently introduced Bayesian model with imprecise prior distributions for statistical inference on failure data, that can also be considered as a robust Bayesian model. The model consists of a multinomial distribution with Dirichlet priors, making the approach basically nonparametric. New results for the model are presented, related to right-censored observations, where estimation based on this model is closely related to the product-limit estimator, which is an important statistical method to deal with reliability or survival data including right-censored observations. As for the product-limit estimator, the model considered in this paper aims at not using any information other than that provided by observed data, but our model fits into the robust Bayesian context which has the advantage that all inferences can be based on probabilities or expectations, or bounds for probabilities or expectations. The model uses a finite partition of the time-axis, and as such it is also related to life-tables

Modeling Fuel Choice among Households in Northern Cameroon

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Jean Hugues Nlom

2015-07-01

Full Text Available The present study aims to explore economic and socio-demographic factors that influence a household’s probability to switch from firewood to cleaner fuels (kerosene and LPG in northern Cameroon. The paper employs an ordered probit model to construct cooking patterns and fuel choices. Three main cooking sources are considered: firewood, kerosene, and liquefied petroleum gas. Utilized data are derived from a national survey conducted in 2004 by the Cameroonian National Institute of Statistics. The study analyzes the data related to the Sudano-Sahelian agro-ecological zone, which is one of the most affected by land degradation and decertification. While results indicate that there is a potential for a transition from traditional to cleaner fuels in the studied region, this transition is still in its earlier stage. The research demonstrates that firewood and kerosene prices, age of household heads, educational level of household heads and willingness to have a gas cylinder, as well as type of dwelling have a statistically significant impact on fuel-switching decisions.

A disaggregate model to predict the intercity travel demand

Energy Technology Data Exchange (ETDEWEB)

Damodaran, S.

1988-01-01

This study was directed towards developing disaggregate models to predict the intercity travel demand in Canada. A conceptual framework for the intercity travel behavior was proposed; under this framework, a nested multinomial model structure that combined mode choice and trip generation was developed. The CTS (Canadian Travel Survey) data base was used for testing the structure and to determine the viability of using this data base for intercity travel-demand prediction. Mode-choice and trip-generation models were calibrated for four modes (auto, bus, rail and air) for both business and non-business trips. The models were linked through the inclusive value variable, also referred to as the long sum of the denominator in the literature. Results of the study indicated that the structure used in this study could be applied for intercity travel-demand modeling. However, some limitations of the data base were identified. It is believed that, with some modifications, the CTS data could be used for predicting intercity travel demand. Future research can identify the factors affecting intercity travel behavior, which will facilitate collection of useful data for intercity travel prediction and policy analysis.

PBODL : Parallel Bayesian Online Deep Learning for Click-Through Rate Prediction in Tencent Advertising System

OpenAIRE

Liu, Xun; Xue, Wei; Xiao, Lei; Zhang, Bo

2017-01-01

We describe a parallel bayesian online deep learning framework (PBODL) for click-through rate (CTR) prediction within today's Tencent advertising system, which provides quick and accurate learning of user preferences. We first explain the framework with a deep probit regression model, which is trained with probabilistic back-propagation in the mode of assumed Gaussian density filtering. Then we extend the model family to a variety of bayesian online models with increasing feature embedding ca...

The British American Tobacco out growers scheme: Determinants of ...

African Journals Online (AJOL)

The study analyzed the operation and performance of Tobacco Out grower Scheme in Oyo State, Nigeria. The data for the analysis came from a random sample survey of the area of study. The treatment effect model was adopted in analyzing the data. Evidence from the probit analysis indicates that membership of the ...

The Role of Education Pathways in the Relationship between Job Mismatch, Wages and Job Satisfaction: A Panel Estimation Approach

Science.gov (United States)

Mavromaras, Kostas; Sloane, Peter; Wei, Zhang

2012-01-01

This paper examines the outcome of over-skilling and over-education on wages and job satisfaction of full-time employees in Australia between 2001 and 2008. We employ a random effects probit model with Mundlak corrections. We find differences by type of mismatch, education pathway, and gender. We categorise reported mismatches as genuine…

Weather shocks and cropland decisions in rural Mozambique

DEFF Research Database (Denmark)

Salazar Espinoza, César Antonio; Jones, Edward Samuel; Tarp, Finn

2015-01-01

to examine the effect of weather shocks on cropland decisions. We account for the bounded nature of land shares and estimate a Pooled Fractional Probit model for panel data. Our results show that crop choice is sensitive to past weather shocks. Farmers shift land use away from cash and permanent crops one...

Effect of dietary fatty acid intake on prospective weight change in the Heidelberg cohort of the European Prospective Investigation into Cancer and Nutrition

DEFF Research Database (Denmark)

Nimptsch, Katharina; Berg-Beckhoff, Gabi; Linseisen, Jakob

2010-01-01

OBJECTIVE: To evaluate the association between fatty acid (alpha-linolenic acid (ALA), EPA, DHA, palmitic, stearic, oleic, linoleic and arachidonic acids) intake and prospective weight change in the Heidelberg cohort of the European Prospective Investigation into Cancer and Nutrition. DESIGN....... RESULTS: Stearic acid intake was linearly associated with weight gain (P acid intake, significantly so in women. In multinomial models, women in the highest tertile of ALA and stearic acid intake showed increased OR (95 % CI......) and categorised into four groups (weight loss, or =2.5 to or =7.5%/5 years). Energy-adjusted dietary fatty acid intake data were estimated from the FFQ completed at baseline. Multivariate linear regression models as well as multinomial logistic regression analyses (carbohydrate replacement models) were conducted...

A dynamic analysis of motorcycle ownership and usage: a panel data modeling approach.

Science.gov (United States)

Wen, Chieh-Hua; Chiou, Yu-Chiun; Huang, Wan-Ling

2012-11-01

This study aims to develop motorcycle ownership and usage models with consideration of the state dependence and heterogeneity effects based on a large-scale questionnaire panel survey on vehicle owners. To account for the independence among alternatives and heterogeneity among individuals, the modeling structure of motorcycle ownership adopts disaggregate choice models considering the multinomial, nested, and mixed logit formulations. Three types of panel data regression models--ordinary, fixed, and random effects--are developed and compared for motorcycle usage. The estimation results show that motorcycle ownership in the previous year does exercise a significantly positive effect on the number of motorcycles owned by households in the current year, suggesting that the state dependence effect does exist in motorcycle ownership decisions. In addition, the fixed effects model is the preferred specification for modeling motorcycle usage, indicating strong evidence for existence of heterogeneity. Among various management strategies evaluated under different scenarios, increasing gas prices and parking fees will lead to larger reductions in total kilometers traveled. Copyright © 2011 Elsevier Ltd. All rights reserved.

Dental Care Coverage and Use: Modeling Limitations and Opportunities

Science.gov (United States)

Moeller, John F.; Chen, Haiyan

2014-01-01

Objectives. We examined why older US adults without dental care coverage and use would have lower use rates if offered coverage than do those who currently have coverage. Methods. We used data from the 2008 Health and Retirement Study to estimate a multinomial logistic model to analyze the influence of personal characteristics in the grouping of older US adults into those with and those without dental care coverage and dental care use. Results. Compared with persons with no coverage and no dental care use, users of dental care with coverage were more likely to be younger, female, wealthier, college graduates, married, in excellent or very good health, and not missing all their permanent teeth. Conclusions. Providing dental care coverage to uninsured older US adults without use will not necessarily result in use rates similar to those with prior coverage and use. We have offered a model using modifiable factors that may help policy planners facilitate programs to increase dental care coverage uptake and use. PMID:24328635

Development of discrete choice model considering internal reference points and their effects in travel mode choice context

Science.gov (United States)

Sarif; Kurauchi, Shinya; Yoshii, Toshio

2017-06-01

In the conventional travel behavior models such as logit and probit, decision makers are assumed to conduct the absolute evaluations on the attributes of the choice alternatives. On the other hand, many researchers in cognitive psychology and marketing science have been suggesting that the perceptions of attributes are characterized by the benchmark called “reference points” and the relative evaluations based on them are often employed in various choice situations. Therefore, this study developed a travel behavior model based on the mental accounting theory in which the internal reference points are explicitly considered. A questionnaire survey about the shopping trip to the CBD in Matsuyama city was conducted, and then the roles of reference points in travel mode choice contexts were investigated. The result showed that the goodness-of-fit of the developed model was higher than that of the conventional model, indicating that the internal reference points might play the major roles in the choice of travel mode. Also shown was that the respondents seem to utilize various reference points: some tend to adopt the lowest fuel price they have experienced, others employ fare price they feel in perceptions of the travel cost.

Use of negative multinomial linear models to investigate environmental effects on community structure.

Science.gov (United States)

A frequent goal in ecology is to understand the relationships between biological communities and their environment. Anderson and McCardle (2001) provided a nonparametric method, known as Permanova, that is often used for this purpose. Permanova represents a significant advance,...

Multi-disciplinary decision making in general practice.

Science.gov (United States)

Kirby, Ann; Murphy, Aileen; Bradley, Colin

2018-04-09

Purpose Internationally, healthcare systems are moving towards delivering care in an integrated manner which advocates a multi-disciplinary approach to decision making. Such an approach is formally encouraged in the management of Atrial Fibrillation patients through the European Society of Cardiology guidelines. Since the emergence of new oral anticoagulants switching between oral anticoagulants (OACs) has become prevalent. This case study considers the role of multi-disciplinary decision making, given the complex nature of the agents. The purpose of this paper is to explore Irish General Practitioners' (GPs) experience of switching between all OACs for Arial Fibrillation (AF) patients; prevalence of multi-disciplinary decision making in OAC switching decisions and seeks to determine the GP characteristics that appear to influence the likelihood of multi-disciplinary decision making. Design/methodology/approach A probit model is used to determine the factors influencing multi-disciplinary decision making and a multinomial logit is used to examine the factors influencing who is involved in the multi-disciplinary decisions. Findings Results reveal that while some multi-disciplinary decision-making is occurring (64 per cent), it is not standard practice despite international guidelines on integrated care. Moreover, there is a lack of patient participation in the decision-making process. Female GPs and GPs who have initiated prescriptions for OACs are more likely to engage in multi-disciplinary decision-making surrounding switching OACs amongst AF patients. GPs with training practices were less likely to engage with cardiac consultants and those in urban areas were more likely to engage with other (non-cardiac) consultants. Originality/value For optimal decision making under uncertainty multi-disciplinary decision-making is needed to make a more informed judgement and to improve treatment decisions and reduce the opportunity cost of making the wrong decision.

The Impact of Income and Taxation in a Price-Tiered Cigarette Market - findings from the ITC Bangladesh Surveys.

Science.gov (United States)

Huq, Iftekharul; Nargis, Nigar; Lkhagvasuren, Damba; Hussain, Akm Ghulam; Fong, Geoffrey T

2018-04-25

Taxing tobacco is among the most effective measures of tobacco control. However, in a tiered market structure where multiple tiers of taxes coexist, the anticipated impact of tobacco taxes on consumption is complex. This paper investigates changing smoking behaviour in lieu of changing prices and changing income. The objective of the paper is to evaluate the effectiveness of change in prices (through taxes) and change in income in a price-tiered cigarette market. A panel dataset from the International Tobacco Control Bangladesh surveys is used for analysis. For preliminary analysis transition matrices are developed. Next, probit and multinomial logit regression models are used to identify the effects of changes in prices and changes in income along with other control variables. Transition matrices show significant movement of smokers across price tiers from one wave to another. Regression results show that higher income raises the probability to up-trade and decreases the probability to down-trade. Results also show that higher prices raises the probability to up-trade and reduces the probability to down-trade. Although not significant, there exists a negative relationship between the probability to down-trade and the probability to intend to quit. It is evident from the results that a price-tiered market provides smokers more opportunities to accommodate their smoking behaviour when faced with price and income change. Therefore, tiered structure of the tax system should be replaced with uniform taxes. Moreover, overall cigarette taxes need to be raised to an extent so that it off-sets any positive effects of income growth. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Clinical implications of alternative TCP models for nonuniform dose distributions

International Nuclear Information System (INIS)

Deasy, J. O.

1995-01-01

Several tumor control probability (TCP) models for nonuniform dose distributions were compared, including: (a) a logistic/inter-patient-heterogeneity model, (b) a probit/inter-patient-heterogeneity model, (c) a Poisson/radioresistant-strain/identical-patients model, (d) a Poisson/inter-patient-heterogeneity model and (e) a Poisson/intra-tumor- and inter-patient-heterogeneity model. The models were analyzed in terms of the probability of controlling a single tumor voxel (the voxel control probability, or VCP), as a function of voxel volume and dose. Alternatively, the VCP surface can be thought of as the effect of a small cold spot. The models based on the Poisson equation which include inter-patient heterogeneity ((d) and (e)) have VCP surfaces (VCP as a function of dose and volume) which have a threshold 'waterfall' shape: below the waterfall (in dose), VCP is nearly zero. The threshold dose decreases with decreasing voxel volume. However, models (a), (b), and (c) all show a high probability of controlling a voxel (VCP>50%) with very low dose (e.g., 1 Gy) if the voxel is small (smaller than about 10 -3 of the tumor volume). Model (c) does not have the waterfall shape at low volumes due to the assumption of patient uniformity and a neglect of the effect of the clonogens which are more radiosensitive (and more numerous). Models (a) and (b) deviate from the waterfall shape at low volumes due to numerical differences between the functions used and the Poisson function. Hence, the Possion models which include inter-patient heterogeneities ((d) and (e)) are more sensitive to the effects of small cold spots than the other models considered

Testing for the Endogenous Nature between Women's Empowerment and Antenatal Health Care Utilization: Evidence from a Cross-Sectional Study in Egypt

Science.gov (United States)

Hussein, Mohamed Ali

2014-01-01

Women's relative lack of decision-making power and their unequal access to employment, finances, education, basic health care, and other resources are considered to be the root causes of their ill-health and that of their children. The main purpose of this paper is to examine the interactive relation between women's empowerment and the use of maternal health care. Two model specifications are tested. One assumes no correlation between empowerment and antenatal care while the second specification allows for correlation. Both the univariate and the recursive bivariate probit models are tested. The data used in this study is EDHS 2008. Factor Analysis Technique is also used to construct some of the explanatory variables such as the availability and quality of health services indicators. The findings show that women's empowerment and receiving regular antenatal care are simultaneously determined and the recursive bivariate probit is a better approximation to the relationship between them. Women's empowerment has significant and positive impact on receiving regular antenatal care. The availability and quality of health services do significantly increase the likelihood of receiving regular antenatal care. PMID:25140310

Probabilistic models for 2D active shape recognition using Fourier descriptors and mutual information

CSIR Research Space (South Africa)

Govender, N

2014-08-01

Full Text Available information to improve the initial shape recognition results. We propose an initial system which performs shape recognition using the euclidean distances of Fourier descriptors. To improve upon these results we build multinomial and Gaussian probabilistic...

A Multinomial Logit Approach to Estimating Regional Inventories by Product Class

Science.gov (United States)

Lawrence Teeter; Xiaoping Zhou

1998-01-01

Current timber inventory projections generally lack information on inventory by product classes. Most models available for inventory projection and linked to supply analyses are limited to projecting aggregate softwood and hardwood. The objective of this research is to develop a methodology to distribute the volume on each FIA survey plot to product classes and...

The Effect of Overskilling Dynamics on Wages

Science.gov (United States)

Mavromaras, Kostas; Mahuteau, Stephane; Sloane, Peter; Wei, Zhang

2013-01-01

We use a random-effects dynamic probit model to estimate the effect of overskilling dynamics on wages. We find that overskilling mismatch is common and more likely among those who have been overskilled in the past. It is also highly persistent, in a manner that is inversely related to educational level. Yet, the wages of university graduates are…

Labour market transitions and job satisfaction

NARCIS (Netherlands)

G.E. Bijwaard (Govert); A. van Dijk (Bram); J. de Koning (Jaap)

2003-01-01

textabstractThe paper investigates the relationship between job satisfaction and labour market transitions. Using a multinomial logit model, a model is estimated on the basis of individual data in which transitions are explained from individual characteristics, job characteristics, dissatisfaction

Seeking treatment for symptomatic malaria in Papua New Guinea

Directory of Open Access Journals (Sweden)

Siba Peter

2010-10-01

Full Text Available Abstract Background Malaria places a significant burden on the limited resources of many low income countries. Knowing more about why and where people seek treatment will enable policy makers to better allocate the limited resources. This study aims to better understand what influences treatment-seeking behaviour for malaria in one such low-income country context, Papua New Guinea (PNG. Methods Two culturally, linguistically and demographically different regions in PNG were selected as study sites. A cross sectional household survey was undertaken in both sites resulting in the collection of data on 928 individuals who reported suffering from malaria in the previous four weeks. A probit model was then used to identify the factors determining whether or not people sought treatment for presumptive malaria. Multinomial logit models also assisted in identifying the factors that determined where people sought treatments. Results Results in this study build upon findings from other studies. For example, while distance in PNG has previously been seen as the primary factor in influencing whether any sort of treatment will be sought, in this study cultural influences and whether it was the first, second or even third treatment for a particular episode of malaria were also important. In addition, although formal health care facilities were the most popular treatment sources, it was also found that traditional healers were a common choice. In turn, the reasons why participants chose a particular type of treatment differed according to the whether they were seeking an initial or subsequent treatments. Conclusions Simply bringing health services closer to where people live may not always result in a greater use of formal health care facilities. Policy makers in PNG need to consider within-country variation in treatment-seeking behaviour, the important role of traditional healers and also ensure that the community fully understands the potential implications

Mortalidad infantil en Uruguay: un análisis de supervivencia

Directory of Open Access Journals (Sweden)

Jewell R. Todd

2010-12-01

Full Text Available A partir de todos los nacimientos ocurridos en el Uruguay entre 2002 y 2003 y las defunciones ocurridas en el primer año de vida, se estima la tasa de mortalidad infantil a través de modelos probit y hazard. Debido a que las muertes se concentran en los primeros días y semanas de vida, el modelo hazard es preferible al probit, encontrándose que la estimación probit sobreestima los efectos de las covariables. Los resultados muestran que las variables más importantes son la edad y la educación de la madre, los cuidados prenatales y los denominados predictores de la mortalidad (bajo peso al nacer, semanas de gestación y APGAR.

Generalised Partially Linear Regression with Misclassified Data and an Application to Labour Market Transitions

DEFF Research Database (Denmark)

Dlugosz, Stephan; Mammen, Enno; Wilke, Ralf

We consider the semiparametric generalised linear regression model which has mainstream empirical models such as the (partially) linear mean regression, logistic and multinomial regression as special cases. As an extension to related literature we allow a misclassified covariate to be interacted...

The Homogeneity of Social Selection in Accessing Higher Ranked Universities

DEFF Research Database (Denmark)

Munk, Martin David; Baklanov, Nikita

2018-01-01

This paper demonstrates the persistence of social selectivity throughout the educational ladder, with evident social reproduction at the top. By jointly modelling multiple choices of high school, university, field of study, and institutional rank of university using a multinomial transition model...

Genetic parameter estimation of reproductive traits of Litopenaeus vannamei

Science.gov (United States)

Tan, Jian; Kong, Jie; Cao, Baoxiang; Luo, Kun; Liu, Ning; Meng, Xianhong; Xu, Shengyu; Guo, Zhaojia; Chen, Guoliang; Luan, Sheng

2017-02-01

In this study, the heritability, repeatability, phenotypic correlation, and genetic correlation of the reproductive and growth traits of L. vannamei were investigated and estimated. Eight traits of 385 shrimps from forty-two families, including the number of eggs (EN), number of nauplii (NN), egg diameter (ED), spawning frequency (SF), spawning success (SS), female body weight (BW) and body length (BL) at insemination, and condition factor (K), were measured,. A total of 519 spawning records including multiple spawning and 91 no spawning records were collected. The genetic parameters were estimated using an animal model, a multinomial logit model (for SF), and a sire-dam and probit model (for SS). Because there were repeated records, permanent environmental effects were included in the models. The heritability estimates for BW, BL, EN, NN, ED, SF, SS, and K were 0.49 ± 0.14, 0.51 ± 0.14, 0.12 ± 0.08, 0, 0.01 ± 0.04, 0.06 ± 0.06, 0.18 ± 0.07, and 0.10 ± 0.06, respectively. The genetic correlation was 0.99 ± 0.01 between BW and BL, 0.90 ± 0.19 between BW and EN, 0.22 ± 0.97 between BW and ED, -0.77 ± 1.14 between EN and ED, and -0.27 ± 0.36 between BW and K. The heritability of EN estimated without a covariate was 0.12 ± 0.08, and the genetic correlation was 0.90 ± 0.19 between BW and EN, indicating that improving BW may be used in selection programs to genetically improve the reproductive output of L. vannamei during the breeding. For EN, the data were also analyzed using body weight as a covariate (EN-2). The heritability of EN-2 was 0.03 ± 0.05, indicating that it is difficult to improve the reproductive output by genetic improvement. Furthermore, excessive pursuit of this selection is often at the expense of growth speed. Therefore, the selection of high-performance spawners using BW and SS may be an important strategy to improve nauplii production.

An optimal hierarchical decision model for a regional logistics network with environmental impact consideration.

Science.gov (United States)

Zhang, Dezhi; Li, Shuangyan; Qin, Jin

2014-01-01

This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users' demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators' service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level.

An Optimal Hierarchical Decision Model for a Regional Logistics Network with Environmental Impact Consideration

Directory of Open Access Journals (Sweden)

Dezhi Zhang

2014-01-01

Full Text Available This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users’ demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators’ service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level.

An Optimal Hierarchical Decision Model for a Regional Logistics Network with Environmental Impact Consideration

Science.gov (United States)

Zhang, Dezhi; Li, Shuangyan

2014-01-01

This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users' demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators' service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level. PMID:24977209

Environmental Sound Perception: Metadescription and Modeling Based on Independent Primary Studies

Directory of Open Access Journals (Sweden)

Stephen McAdams

2010-01-01

Full Text Available The aim of the study is to transpose and extend to a set of environmental sounds the notion of sound descriptors usually used for musical sounds. Four separate primary studies dealing with interior car sounds, air-conditioning units, car horns, and closing car doors are considered collectively. The corpus formed by these initial stimuli is submitted to new experimental studies and analyses, both for revealing metacategories and for defining more precisely the limits of each of the resulting categories. In a second step, the new structure is modeled: common and specific dimensions within each category are derived from the initial results and new investigations of audio features are performed. Furthermore, an automatic classifier based on two audio descriptors and a multinomial logistic regression procedure is implemented and validated with the corpus.

Disentangling stereotype activation and stereotype application in the stereotype misperception task.

Science.gov (United States)

Krieglmeyer, Regina; Sherman, Jeffrey W

2012-08-01

When forming impressions about other people, stereotypes about the individual's social group often influence the resulting impression. At least 2 distinguishable processes underlie stereotypic impression formation: stereotype activation and stereotype application. Most previous research has used implicit measures to assess stereotype activation and explicit measures to assess stereotype application, which has several disadvantages. The authors propose a measure of stereotypic impression formation, the stereotype misperception task (SMT), together with a multinomial model that quantitatively disentangles the contributions of stereotype activation and application to responses in the SMT. The validity of the SMT and of the multinomial model was confirmed in 5 studies. The authors hope to advance research on stereotyping by providing a measurement tool that separates multiple processes underlying impression formation.

SHORT COMMUNICATIONS

African Journals Online (AJOL)

The relationships between S. mansoni, hookworm and S. mansoni + hookworm with P. falciparum were investigated by fitting logistic regression model taking P. falciparum as response variable. Furthermore, co-infections analysis was investigated by fitting multinomial logistic regression model where all combination of ...

Transaction cost determinants of credit governance structures of ...

African Journals Online (AJOL)

This paper explores transaction cost determinants of credit governance structures (CGS) of commercial banks in Tanzania. Descriptive statistics, linear regression model, binary and multinomial logistic regression models were employed for analysis. Findings revealed four modes of credit governance structures that are ...

Modelling parking behaviour considering heterogeneity

Energy Technology Data Exchange (ETDEWEB)

San Martin, G.A.; Ibeas Portilla, A.; Alonso Oreña, B.; Olio, L. del

2016-07-01

Most of motorized trips in cities of middle and small size are made in public transport and mainly in private vehicle, this has caused a saturation in parking systems of the cities, causing important problems to society, one of the most important problems is high occupancy of public space by parking systems. Thus, is required the estimation of models that reproduce users’ behaviour when they are choosing for parking in cities, to carry out transport policies to improve transport efficiency and parking systems in the cities. The aim of this paper is the specification and estimation of models that simulate users’ behaviour when they are choosing among alternatives of parking that there are in the city: free on street parking, paid on street parking, paid on underground parking and Park and Ride (now there isn´t). For this purpose, is proposed a multinomial logit model that consider systematic and random variations in tastes. Data of users’ behaviour from the different alternatives of parking have been obtained with a stated preference surveys campaign which have been done in May 2015 in the principal parking zones of the city of Santander. In this paper, we provide a number of improvements to previously developed methodologies because of we consider much more realism to create the scenarios stated preference survey, obtaining better adjustments. (Author)

Maximum likelihood estimation of semiparametric mixture component models for competing risks data.

Science.gov (United States)

Choi, Sangbum; Huang, Xuelin

2014-09-01

In the analysis of competing risks data, the cumulative incidence function is a useful quantity to characterize the crude risk of failure from a specific event type. In this article, we consider an efficient semiparametric analysis of mixture component models on cumulative incidence functions. Under the proposed mixture model, latency survival regressions given the event type are performed through a class of semiparametric models that encompasses the proportional hazards model and the proportional odds model, allowing for time-dependent covariates. The marginal proportions of the occurrences of cause-specific events are assessed by a multinomial logistic model. Our mixture modeling approach is advantageous in that it makes a joint estimation of model parameters associated with all competing risks under consideration, satisfying the constraint that the cumulative probability of failing from any cause adds up to one given any covariates. We develop a novel maximum likelihood scheme based on semiparametric regression analysis that facilitates efficient and reliable estimation. Statistical inferences can be conveniently made from the inverse of the observed information matrix. We establish the consistency and asymptotic normality of the proposed estimators. We validate small sample properties with simulations and demonstrate the methodology with a data set from a study of follicular lymphoma. © 2014, The International Biometric Society.

Making sense of sparse rating data in collaborative filtering via topographic organization of user preference patterns.

Science.gov (United States)

Polcicová, Gabriela; Tino, Peter

2004-01-01

We introduce topographic versions of two latent class models (LCM) for collaborative filtering. Latent classes are topologically organized on a square grid. Topographic organization of latent classes makes orientation in rating/preference patterns captured by the latent classes easier and more systematic. The variation in film rating patterns is modelled by multinomial and binomial distributions with varying independence assumptions. In the first stage of topographic LCM construction, self-organizing maps with neural field organized according to the LCM topology are employed. We apply our system to a large collection of user ratings for films. The system can provide useful visualization plots unveiling user preference patterns buried in the data, without loosing potential to be a good recommender model. It appears that multinomial distribution is most adequate if the model is regularized by tight grid topologies. Since we deal with probabilistic models of the data, we can readily use tools from probability and information theories to interpret and visualize information extracted by our system.

Maternal union instability and childhood mortality risk in the Global South, 2010-14.

Science.gov (United States)

DeRose, Laurie F; Salazar-Arango, Andrés; Corcuera García, Paúl; Gas-Aixendri, Montserrat; Rivera, Reynaldo

2017-07-01

Efforts to improve child survival in lower-income countries typically focus on fundamental factors such as economic resources and infrastructure provision, even though research from post-industrial countries confirms that family instability has important health consequences. We tested the association between maternal union instability and children's mortality risk in Africa, Latin America and the Caribbean, and Asia using children's actual experience of mortality (discrete-time probit hazard models) as well as their experience of untreated morbidity (probit regression). Children of divorced/separated mothers experience compromised survival chances, but children of mothers who have never been in a union generally do not. Among children of partnered women, those whose mothers have experienced prior union transitions have a higher mortality risk. Targeting children of mothers who have experienced union instability-regardless of current union status-may augment ongoing efforts to reduce childhood mortality, especially in Africa and Latin America where union transitions are common.

Causal mediation analysis with a binary outcome and multiple continuous or ordinal mediators: Simulations and application to an alcohol intervention.

Science.gov (United States)

Nguyen, Trang Quynh; Webb-Vargas, Yenny; Koning, Ina M; Stuart, Elizabeth A

We investigate a method to estimate the combined effect of multiple continuous/ordinal mediators on a binary outcome: 1) fit a structural equation model with probit link for the outcome and identity/probit link for continuous/ordinal mediators, 2) predict potential outcome probabilities, and 3) compute natural direct and indirect effects. Step 2 involves rescaling the latent continuous variable underlying the outcome to address residual mediator variance/covariance. We evaluate the estimation of risk-difference- and risk-ratio-based effects (RDs, RRs) using the ML, WLSMV and Bayes estimators in Mplus. Across most variations in path-coefficient and mediator-residual-correlation signs and strengths, and confounding situations investigated, the method performs well with all estimators, but favors ML/WLSMV for RDs with continuous mediators, and Bayes for RRs with ordinal mediators. Bayes outperforms WLSMV/ML regardless of mediator type when estimating RRs with small potential outcome probabilities and in two other special cases. An adolescent alcohol prevention study is used for illustration.

Networks and Selection in International Migration to Spain

DEFF Research Database (Denmark)

Neubecker, Nina; Smolka, Marcel; Steinbacher, Anne

This paper provides new evidence on migrant networks as determinants of the scale and skill structure of migration, using aggregate data from a recent migration boom to Spain. We develop a three-level nested multinomial logit migration model. Our model accommodates varying degrees of similarity...

The Effects of Designated Pollutants on Plants

Science.gov (United States)

1978-11-01

Persea americana Mill. Haas and Bacon Barley Hordeum vulgare L. CM 67 Bean Phaseolus vulgaris L. Pinto, U.I. III Briza Briza maxima L. Ornamental...Tagetes patula L. French dwarf double goldie Marigold Tagetes erecta L. American ,Senator Dirksen Petunia Petunia hybrida Vilm. White cascade Radish...Probit analysis of five plant species: citrus seedlings, lemon, orange,, grape, French marigold, American marigold. Probit scale is the probability that a

Econometric modelling of risk adverse behaviours of entrepreneurs in the provision of house fittings in China

Directory of Open Access Journals (Sweden)

Rita Yi Man Li

2012-03-01

Full Text Available Entrepreneurs have always born the risk of running their business. They reap a profit in return for their risk taking and work. Housing developers are no different. In many countries, such as Australia, the United Kingdom and the United States, they interpret the tastes of the buyers and provide the dwellings they develop with basic fittings such as floor and wall coverings, bathroom fittings and kitchen cupboards. In mainland China, however, in most of the developments, units or houses are sold without floor or wall coverings, kitchen  or bathroom fittings. What is the motive behind this choice? This paper analyses the factors affecting housing developers’ decisions to provide fittings based on 1701 housing developments in Hangzhou, Chongqing and Hangzhou using a Probit model. The results show that developers build a higher proportion of bare units in mainland China when: 1 there is shortage of housing; 2 land costs are high so that the comparative costs of providing fittings become relatively low.

CONSUMER DEMAND FOR AND ATTITUDES TOWARD ALTERNATIVE BEEF LABELING STRATEGIES IN FRANCE, GERMANY, AND THE UK

OpenAIRE

Roosen, Jutta; Lusk, Jayson L.; Fox, John A.

2001-01-01

A wide array of food safety scares and breakdowns have led to loss of consumer confidence in the quality and safety of beef products. To counteract such concerns, firms and regulators have the ability to utilize brands or labels to signal quality. Utilizing a mail survey in France, Germany, and the United Kingdom, we analyzed consumer preferences for alternative beef labeling strategies. Using an ordered probit model and a double bounded logit model, we estimate consumer preferences for alter...

Modeling marrow damage from response data: Morphallaxis from radiation biology to benzene toxicity

Energy Technology Data Exchange (ETDEWEB)

Jones, T.D.; Morris, M.D.; Hasan, J.S.

1995-12-01

Consensus principles from radiation biology were used to describe a generic set of nonlinear, first-order differential equations for modeling of toxicity-induced compensatory cell kinetics in terms of sublethal injury, repair, direct killing, killing of cells with unrepaired sublethal injury, and repopulation. This cellular model was linked to a probit model of hematopoietic mortality that describes death from infection and/or hemorrhage between {approximately} 5 and 30 days. Mortality data from 27 experiments with 851 doseresponse groups, in which doses were protracted by rate and/or fractionation, were used to simultaneously estimate all rate constants by maximum-likelihood methods. Data used represented 18,940 test animals distributed according to: (mice, 12,827); (rats, 2,925); (sheep, 1,676); (swine, 829); (dogs, 479); and (burros, 204). Although a long-term, repopulating hematopoietic stem cell is ancestral to all lineages needed to restore normal homeostasis, the dose-response data from the protracted irradiations indicate clearly that the particular lineage that is ``critical`` to hematopoietic recovery does not resemble stem-like cells with regard to radiosensitivity and repopulation rates. Instead, the weakest link in the chain of hematopoiesis was found to have an intrinsic radioresistance equal to or greater than stromal cells and to repopulate at the same rates. Model validation has been achieved by predicting the LD{sub 50} and/or fractional group mortality in 38 protracted-dose experiments (rats and mice) that were not used in the fitting of model coefficients.

Modeling marrow damage from response data: Morphallaxis from radiation biology to benzene toxicity

International Nuclear Information System (INIS)

Jones, T.D.; Morris, M.D.; Hasan, J.S.

1995-01-01

Consensus principles from radiation biology were used to describe a generic set of nonlinear, first-order differential equations for modeling of toxicity-induced compensatory cell kinetics in terms of sublethal injury, repair, direct killing, killing of cells with unrepaired sublethal injury, and repopulation. This cellular model was linked to a probit model of hematopoietic mortality that describes death from infection and/or hemorrhage between ∼ 5 and 30 days. Mortality data from 27 experiments with 851 doseresponse groups, in which doses were protracted by rate and/or fractionation, were used to simultaneously estimate all rate constants by maximum-likelihood methods. Data used represented 18,940 test animals distributed according to: (mice, 12,827); (rats, 2,925); (sheep, 1,676); (swine, 829); (dogs, 479); and (burros, 204). Although a long-term, repopulating hematopoietic stem cell is ancestral to all lineages needed to restore normal homeostasis, the dose-response data from the protracted irradiations indicate clearly that the particular lineage that is ''critical'' to hematopoietic recovery does not resemble stem-like cells with regard to radiosensitivity and repopulation rates. Instead, the weakest link in the chain of hematopoiesis was found to have an intrinsic radioresistance equal to or greater than stromal cells and to repopulate at the same rates. Model validation has been achieved by predicting the LD 50 and/or fractional group mortality in 38 protracted-dose experiments (rats and mice) that were not used in the fitting of model coefficients

630 understanding farmers' response to climate variability in nigeria

African Journals Online (AJOL)

Osondu

Data were analyzed using descriptive statistics, and multinomial logit models. Farmers used multiple adaptation strategies; Crop Diversification (CD), Soil ... Increases in temperature, cloud ... and the effect of climate elements and their extreme ...

Testing for the Endogenous Nature between Women’s Empowerment and Antenatal Health Care Utilization: Evidence from a Cross-Sectional Study in Egypt

Directory of Open Access Journals (Sweden)

Hassan H. M. Zaky

2014-01-01

Full Text Available Women’s relative lack of decision-making power and their unequal access to employment, finances, education, basic health care, and other resources are considered to be the root causes of their ill-health and that of their children. The main purpose of this paper is to examine the interactive relation between women’s empowerment and the use of maternal health care. Two model specifications are tested. One assumes no correlation between empowerment and antenatal care while the second specification allows for correlation. Both the univariate and the recursive bivariate probit models are tested. The data used in this study is EDHS 2008. Factor Analysis Technique is also used to construct some of the explanatory variables such as the availability and quality of health services indicators. The findings show that women’s empowerment and receiving regular antenatal care are simultaneously determined and the recursive bivariate probit is a better approximation to the relationship between them. Women’s empowerment has significant and positive impact on receiving regular antenatal care. The availability and quality of health services do significantly increase the likelihood of receiving regular antenatal care.

Children's emotional and behavioral problems and their mothers' labor supply.

Science.gov (United States)

Richard, Patrick; Gaskin, Darrell J; Alexandre, Pierre K; Burke, Laura S; Younis, Mustafa

2014-01-01

It has been documented that about 20% of children and adolescents suffer from a diagnosable mental or addictive disorder in the United States. The high prevalence of children's emotional and behavioral problems (EBP) might have a negative effect on their mothers' labor market outcomes because children with EBP require additional time for treatment. However, these children may require additional financial resources, which might promote mothers' labor supply. Previous studies have only considered chronic conditions in analyzing the impact of children's health on parental work activities. Moreover, most of these studies have not accounted for endogeneity in children's health. This article estimates the effects of children's EBP on their mothers' labor supply by family structure while accounting for endogeneity in children's health. We used the 1997 and 2002 Child Development Supplements (CDS) to the Panel Study of Income Dynamics (PSID). We used probit and bivariate probit models to estimate mothers' probability of employment, and tobit and instrumental variable tobit models to estimate the effects of children's EBP on their mothers' work hours. Findings show negative effects of children's EBP on their married mothers' employment and on their single mothers' work hours. © The Author(s) 2014.

Gasto catastrófico en salud en México y sus factores determinantes, 2002-2014.

Science.gov (United States)

Rodríguez-Aguilar, Román; Rivera-Peña, Gustavo

2017-01-01

To assess the financial protection of public health insurance by analyzing the percentage of households with catastrophic health expenditure (HCHE) in Mexico and its relationship with poverty status, size of locality, federal entity, insurance status and items of health spending. Mexican National Survey of Income and Expenditures 2002-2014 was used to estimate the percentage of HCHE. Through a probit model, factors associated with the occurrence of catastrophic spending are identified. Analysis was performed using Stata-SE 12. In 2014 there were 2.08% of HCHE (1.82-2.34%; N = 657,474). The estimated probit model correctly classified 98.2% of HCHE (Pr (D) ≥ 0.5). Factors affecting the catastrophic expenditures were affiliation, presence of chronic disease, hospitalization expenditure, rural condition and that the household is below the food poverty line. The percentage of HCHE decreased in recent years, improving financial protection in health. This decline seems to have stalled, keeping inequities in access to health services, especially in rural population without affiliation to any health institution, below the food poverty line and suffering from chronic diseases. Copyright: © 2017 SecretarÍa de Salud

Cumulative t-link threshold models for the genetic analysis of calving ease scores

Directory of Open Access Journals (Sweden)

Tempelman Robert J

2003-09-01

Full Text Available Abstract In this study, a hierarchical threshold mixed model based on a cumulative t-link specification for the analysis of ordinal data or more, specifically, calving ease scores, was developed. The validation of this model and the Markov chain Monte Carlo (MCMC algorithm was carried out on simulated data from normally and t4 (i.e. a t-distribution with four degrees of freedom distributed populations using the deviance information criterion (DIC and a pseudo Bayes factor (PBF measure to validate recently proposed model choice criteria. The simulation study indicated that although inference on the degrees of freedom parameter is possible, MCMC mixing was problematic. Nevertheless, the DIC and PBF were validated to be satisfactory measures of model fit to data. A sire and maternal grandsire cumulative t-link model was applied to a calving ease dataset from 8847 Italian Piemontese first parity dams. The cumulative t-link model was shown to lead to posterior means of direct and maternal heritabilities (0.40 ± 0.06, 0.11 ± 0.04 and a direct maternal genetic correlation (-0.58 ± 0.15 that were not different from the corresponding posterior means of the heritabilities (0.42 ± 0.07, 0.14 ± 0.04 and the genetic correlation (-0.55 ± 0.14 inferred under the conventional cumulative probit link threshold model. Furthermore, the correlation (> 0.99 between posterior means of sire progeny merit from the two models suggested no meaningful rerankings. Nevertheless, the cumulative t-link model was decisively chosen as the better fitting model for this calving ease data using DIC and PBF.

New Evidence on Cross-Country Differences in Job Satisfaction Using Anchoring Vignettes

DEFF Research Database (Denmark)

Kristensen, Nicolai; Johansson, Edvard

2006-01-01

This paper presents results on cross-country comparison of job satisfaction across seven EU countries taking into account that people in different countries may perceive subjective questions differently. We apply a chopit model approach where the threshold parameters in an ordered probit model...... somewhat lower while workers from the Netherlands are found to have the highest level of job satisfaction. These results suggest that cultural di¤erences in the way people perceive subjective questions about satisfaction make simple cross-country comparison misleading....

Water Reuse in Brazilian Manufacturing Firms

OpenAIRE

José Féres; Arnaud Reynaud; Alban Thomas

2015-01-01

This paper examines the factors influencing water reuse in manufacturing firms and analyzes whether the structure of intake water demand differs between firms that adopt water reuse practices and those which do not. To this purpose, we estimate a two-stage econometric model based on a sample of 447 industrial facilities located in the Paraíba do Sul river basin. The first stage applies a probit model for the water reuse decision and the second stage employs an endogenous switching regression ...

Determinants of agricultural micro-credit repayment-evidence from ...

African Journals Online (AJOL)

Agro-Science ... for the study while descriptive statistics and multinomial binary logit model were employed for data analyses. ... on long term funds (without prejudice to the existing revolving loan mechanism) such as the pension contributions, ...

West African Journal of Industrial and Academic Research - Vol 13 ...

African Journals Online (AJOL)

On The Comparison of Artificial Neural Network (ANN) and Multinomial Logistic ... ICT-Based Framework for Improved Food Security in Nigeria · EMAIL FREE FULL ... towards HIV/AIDS Patients in Zambia: A Generalized Additive Mixed Model ...

Calculating the Probability of Returning a Loan with Binary Probability Models

Directory of Open Access Journals (Sweden)

Julian Vasilev

2014-12-01

Full Text Available The purpose of this article is to give a new approach in calculating the probability of returning a loan. A lot of factors affect the value of the probability. In this article by using statistical and econometric models some influencing factors are proved. The main approach is concerned with applying probit and logit models in loan management institutions. A new aspect of the credit risk analysis is given. Calculating the probability of returning a loan is a difficult task. We assume that specific data fields concerning the contract (month of signing, year of signing, given sum and data fields concerning the borrower of the loan (month of birth, year of birth (age, gender, region, where he/she lives may be independent variables in a binary logistics model with a dependent variable “the probability of returning a loan”. It is proved that the month of signing a contract, the year of signing a contract, the gender and the age of the loan owner do not affect the probability of returning a loan. It is proved that the probability of returning a loan depends on the sum of contract, the remoteness of the loan owner and the month of birth. The probability of returning a loan increases with the increase of the given sum, decreases with the proximity of the customer, increases for people born in the beginning of the year and decreases for people born at the end of the year.

Simplificação da equação de viabilidade para predizer a longevidade de sementes de milho e soja Simplification of viability equation to predict seed longevity of corn and soybean

Directory of Open Access Journals (Sweden)

Claudinei Andreoli

2004-09-01

Full Text Available A equação de Ellis & Roberts utiliza a temperatura, a umidade e a qualidade inicial da semente para predizer sua longevidade, porém exige experimentos complexos e demorados. O objetivo deste trabalho foi simplificar a equação de viabilidade para predizer a longevidade da semente de milho e soja em condições de armazenamento aberto. A equação simplificada é explicada pelo modelo Vp = Vi - (tgbeta.p, em que Vp é a viabilidade em probit no período p, Vi é a germinação inicial do lote e tgb é a taxa de deterioração da semente para cada espécie. Sementes de milho BRS201 e BRS206 e soja cultivar IAC-8 e MG/BR 46 (Conquista foram embaladas em sacos de papel e armazenadas por 0, 30, 60, 90, 120, 150, 180, 240, 300 e 360 dias, em galpões abertos, em Sete Lagoas, MG, e Brasília, DF. Os dados foram transformados em 'probit' e a declividade da reta (tgbeta foi calculada entre 0 e 30 dias. O coeficiente (tgbeta variou de 1,4767.10-3 a 2,687.10-3 em milho e de 2,868.10-3 a 3,617.10-3 em soja, dependendo das condições climáticas do armazém. A germinação da semente de soja declinou mais rapidamente que a de milho. O modelo prediz com precisão a longevidade das sementes de milho e soja em armazém aberto.Temperature, moisture and initial quality are important factors in Ellis and Roberts' equation to predict seed longevity, however it relies on complex experiments of seed aging. The objective of this work was to simplify the equation for predicting changes in seed viability during open warehouse storage. The simplified equation proposed is given by the model Vp = Vi - (tgbeta.p, in which Vp is seed viability in probit for a period p (days, Vi is the initial seed viability in probit and tgbeta is the seed deterioration rate for each specie at determined storage conditions. Seeds of hybrid corn BRS201 and BRS206 and soybean varieties IAC-8 and MG/BR 46 (Conquista were packed in multiwall paper bags and stored for 0, 30, 60, 90, 120, 150

What influence Customer Patronage of Insurance Policies: An Empirical Assessment of Socio-Economic and Demographic Determinants of Insurance Patronage in Ghana

OpenAIRE

Fofie, Gloria A.

2016-01-01

The study attempts to explore and assess the social, economic and demographic factors that are likely to influence the patronage of insurance in Ghana. Employing a cross-sectional and convenient sampling method, 200 respondents were selected to answer semi-structured questionnaires. Using a Probit econometric regression model for analysis, the results indicate these socio-economic and demographic determinants are positively and significantly related to insurance demand, except that of religio...

Traffic accidents: an econometric investigation

OpenAIRE

Tito Moreira; Adolfo Sachsida; Loureiro Paulo

2004-01-01

Based on a sample of drivers in Brasilia's streets, this article investigates whether distraction explains traffic accidents. A probit model is estimated to determine the predictive power of several variables on traffic accidents. The main conclusion drawn from this study is that the proxies used to measure distraction, such as the use of cell phones and cigarette smoking in a moving vehicle, are significant factors in determining traffic accidents.

Scientific productivity and academic promotion: a study on French and Italian physicists

OpenAIRE

Francesco Lissoni; Jacques Mairesse; Fabio Montobbio; Michele Pezzoni

2011-01-01

The article examines the determinants of scientific productivity (number of articles and journals' impact factor) for a panel of about 3600 French and Italian academic physicists active in 2004--2005. Endogeneity problems concerning promotion and productivity are addressed by specifying a generalized Tobit model, in which a selection probit equation accounts for the individual scientist's probability of promotion to her present rank, and a productivity regression estimates the effects of age,...

Energy consumption and economic growth—New evidence from meta analysis

International Nuclear Information System (INIS)

Chen, Ping-Yu; Chen, Sheng-Tung; Chen, Chi-Chung

2012-01-01

The causal relationships between energy consumption and economic growth have given rise to much discussion but remain controversial. Alternative data sets based on different time spans, countries, energy policies and econometric approaches result in diverse outcomes. A meta analysis using a multinomial logit model with 174 samples governing the relationships between GDP and energy consumption is applied here to investigate the major factors that affect these controversial outcomes. The empirical results have demonstrated how the time spans, subject selections including GDP and energy consumption, econometric models, and tools for greenhouse gases emission reduction characteristics significantly affect these controversial outcomes. - Highlights: ► The controversial casual relationships between energy consumption and GDP are investigated. ► A meta analysis using a multinomial logit model is adopted. ► 74 studies governing the relationships between GDP and energy consumption was collected. ► The empirical results show how the probability of major factors affects such relationships.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions.

Science.gov (United States)

Tokuda, Tomoki; Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions.

Directory of Open Access Journals (Sweden)

Tomoki Tokuda

Full Text Available We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions

Science.gov (United States)

Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data. PMID:29049392

Bank Lending Policy, Credit Scoring and Value at Risk

OpenAIRE

Jacobson, Tor; Roszbach, Kasper

1998-01-01

In this paper we apply a bivariate probit model to investigate the implications of bank lending policy. In the first equation we model the bank´s decision to grant a loan, in the second the probability of default. We confirm that banks provide loans in a way that is not consistent with default risk minimization. The lending policy must thus either be inefficient or be the result of some other type of optimizing behavior than expected profit maximization. Value at Risk, being a value weighted ...

Do More Economists Hold Stocks?

DEFF Research Database (Denmark)

Christiansen, Charlotte; Joensen, Juanna Schröter; Rangvid, Jesper

A unique data set enables us to test the hypothesis that more economists than otherwise identical investors hold stocks due to informational advantages. We confirm that economists have a significantly higher probability of participating in the stock market than investors with any other education......, even when controlling for several background characteristics. We make use of a large register-based panel data set containing detailed information on the educational attainments and various financial and socioeconomic variables. We model the stock market participation decision by the probit model...

Avaliação de um modelo de predição para apneia do sono em pacientes submetidos a polissonografia Evaluation of a prediction model for sleep apnea in patients submitted to polysomnography

Directory of Open Access Journals (Sweden)

Silvio Musman

2011-02-01

Full Text Available OBJETIVO: Testar um modelo de predição para apneia do sono a partir de variáveis sociodemográficas e clínicas em uma população com suspeita de distúrbio do sono e submetida à polissonografia. MÉTODOS: Foram incluídos no estudo 323 pacientes consecutivos submetidos à polissonografia por suspeita clínica de distúrbio do sono. Utilizou-se um questionário com questões sociodemográficas e a escala de sonolência de Epworth. Foram medidos pressão arterial, peso, altura e SpO2. A regressão linear múltipla, tendo o índice de apneia-hipopneia (IAH como variável dependente, foi utilizada para construir um modelo de predição de apneia do sono. A regressão logística multinomial foi realizada para verificar fatores associados de forma independente à gravidade da apneia (leve, moderada ou grave em comparação à ausência de apneia. RESULTADOS: A prevalência de apneia do sono na população de estudo foi de 71,2%, e foi mais prevalente nos homens que nas mulheres (81,2% vs. 56,8%; p OBJECTIVE: To test a prediction model for sleep apnea based on clinical and sociodemographic variables in a population suspected of having sleep disorders and submitted to polysomnography. METHODS: We included 323 consecutive patients submitted to polysomnography because of the clinical suspicion of having sleep disorders. We used a questionnaire with sociodemographic questions and the Epworth sleepiness scale. Blood pressure, weight, height, and SpO2 were measured. Multiple linear regression was used in order to create a prediction model for sleep apnea, the apnea-hypopnea index (AHI being the dependent variable. Multinomial logistic regression was used in order to identify factors independently associated with the severity of apnea (mild, moderate, or severe in comparison with the absence of apnea. RESULTS: The prevalence of sleep apnea in the study population was 71.2%. Sleep apnea was more prevalent in men than in women (81.2% vs. 56.8%; p < 0

Legisladores, captadores e assistencialistas: a representação política no nível local

Directory of Open Access Journals (Sweden)

Felix Lopez

Full Text Available Resumo O artigo analisa a representação política local, focando as percepções e práticas cotidianas dos vereadores. Em particular, analisam-se suas escolhas entre estratégias de representação clientelistas e universalistas. Utilizam-se dados originais de entrevistas abertas semiestruturadas com amostra não representativa de 112 vereadores de 12 municípios de Minas Gerais. Por meio de análise qualitativa, classificam-se os vereadores em três tipos, de acordo com sua principal estratégia de representação, a saber: “legislador”, que se dedica mais às funções formais da vereança; “captador”, que prioriza o atendimento de pedidos coletivos dos eleitores; “assistencialista”, que prioriza o atendimento de pedidos particulares. Com base na literatura teórica sobre clientelismo, oferecem-se hipóteses explicativas das estratégias de representação dos vereadores, que são testadas estatisticamente utilizando-se um modelo probit multinomial. Os resultados sugerem que essas estratégias são qualitativamente distintas e que a probabilidade de ocorrência do tipo assistencialista é maior em municípios pequenos, crescente no acirramento da competição política e decrescente na volatilidade eleitoral. Também há evidência fraca de que essa probabilidade é decrescente na escolaridade do vereador e crescente no seu tempo de vereança.

Leaving home and entering the housing market.

NARCIS (Netherlands)

Clark, W.A.V.; Mulder, C.H.

2000-01-01

We use a multinomial choice model of owning a home, owning a trailer, or renting to examine the housing-market entry of young adults in the USA after they have left the parental home. We also model the choice between renting independently and sharing with roommates. We show that the likelihood of

Modeling the Joint Choice Decisions on Urban Shopping Destination and Travel-to-Shop Mode: A Comparative Study of Different Structures

Directory of Open Access Journals (Sweden)

Chuan Ding

2014-01-01

Full Text Available The joint choice of shopping destination and travel-to-shop mode in downtown area is described by making use of the cross-nested logit (CNL model structure that allows for potential interalternative correlation along the both choice dimensions. Meanwhile, the traditional multinomial logit (MNL model and nested logit (NL model are also formulated, respectively. This study uses the data collected in the downtown areas of Maryland-Washington, D.C. region, for shopping trips, considering household, individual, land use, and travel related characteristics. The results of the model reveal the significant influencing factors on joint choice travel behavior between shopping destination and travel mode. A comparison of the different models shows that the proposed CNL model structure offers significant improvements in capturing unobserved correlations between alternatives over MNL model and NL model. Moreover, a Monte Carlo simulation for a group of scenarios assuming that there is an increase in parking fees in downtown area is undertaken to examine the impact of a change in car travel cost on the joint choice of shopping destination and travel mode switching. The results are expected to give a better understanding on the shopping travel behavior.

Editorial

African Journals Online (AJOL)

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Semiparametric accelerated failure time cure rate mixture models with competing risks.

Science.gov (United States)

Choi, Sangbum; Zhu, Liang; Huang, Xuelin

2018-01-15

Modern medical treatments have substantially improved survival rates for many chronic diseases and have generated considerable interest in developing cure fraction models for survival data with a non-ignorable cured proportion. Statistical analysis of such data may be further complicated by competing risks that involve multiple types of endpoints. Regression analysis of competing risks is typically undertaken via a proportional hazards model adapted on cause-specific hazard or subdistribution hazard. In this article, we propose an alternative approach that treats competing events as distinct outcomes in a mixture. We consider semiparametric accelerated failure time models for the cause-conditional survival function that are combined through a multinomial logistic model within the cure-mixture modeling framework. The cure-mixture approach to competing risks provides a means to determine the overall effect of a treatment and insights into how this treatment modifies the components of the mixture in the presence of a cure fraction. The regression and nonparametric parameters are estimated by a nonparametric kernel-based maximum likelihood estimation method. Variance estimation is achieved through resampling methods for the kernel-smoothed likelihood function. Simulation studies show that the procedures work well in practical settings. Application to a sarcoma study demonstrates the use of the proposed method for competing risk data with a cure fraction. Copyright © 2017 John Wiley & Sons, Ltd.

Children’s Emotional and Behavioral Problems and Their Mothers’ Labor Supply

Directory of Open Access Journals (Sweden)

Patrick Richard PhD

2014-11-01

Full Text Available It has been documented that about 20% of children and adolescents suffer from a diagnosable mental or addictive disorder in the United States. The high prevalence of children’s emotional and behavioral problems (EBP might have a negative effect on their mothers’ labor market outcomes because children with EBP require additional time for treatment. However, these children may require additional financial resources, which might promote mothers’ labor supply. Previous studies have only considered chronic conditions in analyzing the impact of children’s health on parental work activities. Moreover, most of these studies have not accounted for endogeneity in children’s health. This article estimates the effects of children’s EBP on their mothers’ labor supply by family structure while accounting for endogeneity in children’s health. We used the 1997 and 2002 Child Development Supplements (CDS to the Panel Study of Income Dynamics (PSID. We used probit and bivariate probit models to estimate mothers’ probability of employment, and tobit and instrumental variable tobit models to estimate the effects of children’s EBP on their mothers’ work hours. Findings show negative effects of children’s EBP on their married mothers’ employment and on their single mothers’ work hours.

Analyzing injury severity factors at highway railway grade crossing accidents involving vulnerable road users: A comparative study.

Science.gov (United States)

Ghomi, Haniyeh; Bagheri, Morteza; Fu, Liping; Miranda-Moreno, Luis F

2016-11-16

The main objective of this study is to identify the main factors associated with injury severity of vulnerable road users (VRUs) involved in accidents at highway railroad grade crossings (HRGCs) using data mining techniques. This article applies an ordered probit model, association rules, and classification and regression tree (CART) algorithms to the U.S. Federal Railroad Administration's (FRA) HRGC accident database for the period 2007-2013 to identify VRU injury severity factors at HRGCs. The results show that train speed is a key factor influencing injury severity. Further analysis illustrated that the presence of illumination does not reduce the severity of accidents for high-speed trains. In addition, there is a greater propensity toward fatal accidents for elderly road users compared to younger individuals. Interestingly, at night, injury accidents involving female road users are more severe compared to those involving males. The ordered probit model was the primary technique, and CART and association rules act as the supporter and identifier of interactions between variables. All 3 algorithms' results consistently show that the most influential accident factors are train speed, VRU age, and gender. The findings of this research could be applied for identifying high-risk hotspots and developing cost-effective countermeasures targeting VRUs at HRGCs.

Pricing behaviour of pharmacies after market deregulation for OTC drugs: the case of Germany.

Science.gov (United States)

Stargardt, Tom; Schreyögg, Jonas; Busse, Reinhard

2007-11-01

To examine the price reactions of German pharmacies to changes made to OTC drug regulations in 2004. Prior to these changes, regulations guaranteed identical prices in all German pharmacies. Two years after market deregulation, 256 pharmacies were surveyed to determine the retail prices of five selected OTC drugs. A probit regression model was used to identify factors that increased the likelihood of price changes. In addition, 409 pharmacy consumers were interviewed to gather information on their knowledge of the regulatory changes and to better explain consumer behaviour. Data was collected on a total of 1215 prices. Two years after deregulation, 23.1% of the participating pharmacies had modified the price of at least one of the five OTCs included in our study. However, in total, only 7.5% of the prices differed from their pre-deregulation level. The probit model showed that population density and the geographic concentration of pharmacies were significantly associated with price changes. Interestingly, the association with the geographic concentration of pharmacies was negative. The consumer survey revealed that 47.1% of those interviewed were aware of the deregulation. Our findings indicate that, two years after deregulation, very few pharmacies had made use of individual pricing strategies; price competition between pharmacies in Germany is thus taking place only a very small scale.

An evaluation of substance misuse treatment providers used by an employee assistance program.

Science.gov (United States)

Miller, N A

1992-05-01

Structural measures of access, continuity, and quality of substance misuse treatment services were compared in 30 fee-for-service (FFS) facilities and nine health maintenance organizations (HMOs). Probit models related effects of the provider system (FFS or HMO) and the system's structural characteristics to 243 employees' access to and outcomes from treatment. Access was decreased in Independent Practice Association (IPA)/network HMOs and in all facilities which did not employ an addictionologist or provide coordinated treatment services. When bivariate correlations were examined, both use of copayments and imposing limits to the levels of treatment covered were negatively related to access, while a facility's provision of ongoing professional development was positively associated with access. These correlations did not remain significant in the multivariate probits. Receiving treatment in a staff model HMO and facing limits to the levels of treatment covered were negatively associated with attaining sufficient progress, while receiving treatment in a facility which provided ongoing professional development was positively related to progress: these effects did not remain significant in multivariate analyses. Implications for employee assistance program (EAP) staff in their role as case managers and for EAP staff and employers in their shared role as purchasers of treatment are discussed.

Modeling the Perceptions and Preferences of Pedestrians on Crossing Facilities

Directory of Open Access Journals (Sweden)

Hongwei Guo

2014-01-01

Full Text Available Pedestrian’s street-crossing behaviour has a significant effect on traffic performance and safety. The crossing behaviour is determined by human factors and environmental factors. Aiming at examining the pedestrian perceptions toward crossing facilities and preferences for crossing locations, an observational study of pedestrian crossing behaviour at urban street is conducted. The perceptions and preferences of pedestrians are collected using stated preference technique. A specific questionnaire is designed to conduct the stated preference survey. A multinomial logit model is proposed to describe the perceptions and preferences of pedestrians on crossing facilities and locations. The sensitivity analysis is performed to discuss the influence of various factors on crossing behaviour. Then the relationship between crossing locations and crossing distances is analyzed by a new proposed method. With the theoretical analysis, the engineering solutions considering pedestrian behaviour are suggested. The results are helpful to design human-centered crossing facilities in urban traffic.

Effectiveness of conservation easements in agricultural regions.

Science.gov (United States)

Braza, Mark

2017-08-01

Conservation easements are a standard technique for preventing habitat loss, particularly in agricultural regions with extensive cropland cultivation, yet little is known about their effectiveness. I developed a spatial econometric approach to propensity-score matching and used the approach to estimate the amount of habitat loss prevented by a grassland conservation easement program of the U.S. federal government. I used a spatial autoregressive probit model to predict tract enrollment in the easement program as of 2001 based on tract agricultural suitability, habitat quality, and spatial interactions among neighboring tracts. Using the predicted values from the model, I matched enrolled tracts with similar unenrolled tracts to form a treatment group and a control group. To measure the program's impact on subsequent grassland loss, I estimated cropland cultivation rates for both groups in 2014 with a second spatial probit model. Between 2001 and 2014, approximately 14.9% of control tracts were cultivated and 0.3% of treated tracts were cultivated. Therefore, approximately 14.6% of the protected land would have been cultivated in the absence of the program. My results demonstrate that conservation easements can significantly reduce habitat loss in agricultural regions; however, the enrollment of tracts with low cropland suitability may constrain the amount of habitat loss they prevent. My results also show that spatial econometric models can improve the validity of control groups and thereby strengthen causal inferences about program effectiveness in situations when spatial interactions influence conservation decisions. © 2017 Society for Conservation Biology.

Mathematical models application for mapping soils spatial distribution on the example of the farm from the North of Udmurt Republic of Russia

Science.gov (United States)

Dokuchaev, P. M.; Meshalkina, J. L.; Yaroslavtsev, A. M.

2018-01-01

Comparative analysis of soils geospatial modeling using multinomial logistic regression, decision trees, random forest, regression trees and support vector machines algorithms was conducted. The visual interpretation of the digital maps obtained and their comparison with the existing map, as well as the quantitative assessment of the individual soil groups detection overall accuracy and of the models kappa showed that multiple logistic regression, support vector method, and random forest models application with spatial prediction of the conditional soil groups distribution can be reliably used for mapping of the study area. It has shown the most accurate detection for sod-podzolics soils (Phaeozems Albic) lightly eroded and moderately eroded soils. In second place, according to the mean overall accuracy of the prediction, there are sod-podzolics soils - non-eroded and warp one, as well as sod-gley soils (Umbrisols Gleyic) and alluvial soils (Fluvisols Dystric, Umbric). Heavy eroded sod-podzolics and gray forest soils (Phaeozems Albic) were detected by methods of automatic classification worst of all.

Generalized Partial Least Squares Approach for Nominal Multinomial Logit Regression Models with a Functional Covariate

Science.gov (United States)

Albaqshi, Amani Mohammed H.

2017-01-01

Functional Data Analysis (FDA) has attracted substantial attention for the last two decades. Within FDA, classifying curves into two or more categories is consistently of interest to scientists, but multi-class prediction within FDA is challenged in that most classification tools have been limited to binary response applications. The functional…

PREMIM and EMIM: tools for estimation of maternal, imprinting and interaction effects using multinomial modelling

Directory of Open Access Journals (Sweden)

Howey Richard

2012-06-01

Full Text Available Abstract Background Here we present two new computer tools, PREMIM and EMIM, for the estimation of parental and child genetic effects, based on genotype data from a variety of different child-parent configurations. PREMIM allows the extraction of child-parent genotype data from standard-format pedigree data files, while EMIM uses the extracted genotype data to perform subsequent statistical analysis. The use of genotype data from the parents as well as from the child in question allows the estimation of complex genetic effects such as maternal genotype effects, maternal-foetal interactions and parent-of-origin (imprinting effects. These effects are estimated by EMIM, incorporating chosen assumptions such as Hardy-Weinberg equilibrium or exchangeability of parental matings as required. Results In application to simulated data, we show that the inference provided by EMIM is essentially equivalent to that provided by alternative (competing software packages such as MENDEL and LEM. However, PREMIM and EMIM (used in combination considerably outperform MENDEL and LEM in terms of speed and ease of execution. Conclusions Together, EMIM and PREMIM provide easy-to-use command-line tools for the analysis of pedigree data, giving unbiased estimates of parental and child genotype relative risks.

Endogenous Women's Autonomy and the Use of Reproductive Health Services: Empirical Evidence from Tajikistan

OpenAIRE

Yusuke Kamiya

2010-01-01

Though gender equity is widely considered to be a key to improving maternal health in developing countries, little empirical evidence has been presented to support this claim. This paper investigates whether or not and how female autonomy within the household affects women's use of reproductive health care in Tajikistan, where the situation of maternal health and gender equity is worse compared with neighbouring countries. Estimation is performed using bivariate probit models in which woman's...

Determinants of flexible work arrangements

OpenAIRE

Sarbu, Miruna

2014-01-01

Flexible work arrangements such as allowing employees to work at home are used in firms, especially since information and communication technologies have become so widespread. Using individual-level data from 10,884 German employees, this paper analyses the determinants of working at home as a form of flexible work arrangements. The analysis is based on descriptive analyses and a discrete choice model using a probit estimation approach. The results reveal that men have a higher...

Moment-to-Moment Optimal Branding in TV Commercials: Preventing Avoidance by Pulsing

OpenAIRE

Thales S. Teixeira; Michel Wedel; Rik Pieters

2010-01-01

We develop a conceptual framework about the impact that branding activity (the audiovisual representation of brands) and consumers' focused versus dispersed attention have on consumer moment-to-moment avoidance decisions during television advertising. We formalize this framework in a dynamic probit model and estimate it with Markov chain Monte Carlo methods. Data on avoidance through zapping, along with eye tracking on 31 commercials for nearly 2,000 participants, are used to calibrate the mo...

管理職の仕事満足度の男女間差異に関する実証研究

OpenAIRE

大薗, 陽子

2013-01-01

This study examines differences in job satisfaction among Japanese male and female managers. We used the data set of “FY2004 General Survey on Corporate Strategies and Human Resource Management” conducted by The Japan Institute for Labour Policy and Training. In addition, we employed the Ordered Probit estimation model. The analysis of five parameters related to job satisfaction yielded the following results. Female managers experienced significantly higher job satisfaction than male managers...

Student Performance in Principles of Macroeconomics: the Importance of Gender and Personality Type

OpenAIRE

Leiv Opstad; Lars Fallan

2010-01-01

Several studies indicate that females perform more poorly in economic courses than their male counterparts. Other studies reveal that students' personality types affect their performance in economic courses, as well. The present study explores this issue by testing a number of interactions between gender and the Kersey-Bates temperament types in an ordered probit model explaining students' grades in Principles of Macroeconomics. The results confirm that the interaction of gender and temperame...

Preference diversity and the breadth of employee health insurance options.

OpenAIRE

Moran, J R; Chernew, M E; Hirth, R A

2001-01-01

OBJECTIVE: To examine the effect of worker heterogeneity, firm size, and establishment size on the breadth of employer health insurance offerings. DATA SOURCES: The data were drawn from the 1993 Robert Wood Johnson Foundation Employer Health Insurance Survey of 22,000 business establishments selected randomly from ten states. STUDY DESIGN: The analysis was cross-sectional, using ordered probit models to relate the breadth of plan offerings to firm characteristics. PRINCIPAL FINDINGS: Firms wi...

Interdependencies of Health, Education & Poverty in Egypt, Morocco and Turkey Using Demographic and Health Survey

OpenAIRE

Driouchi, Ahmed; Baijou, Ahmad

2009-01-01

The interdependencies of health, education and poverty that are common knowledge to individuals are also present at the aggregate levels of countries and internationally. The assessment of these interdependencies is the central task of this research but based on the Demographic Health Surveys (DHS) of Egypt, Morocco and Turkey. The results attained through dependency tests and probit models, confirm the existence of major interdependencies at the levels of households. These findings support t...

Changes in Math Prerequisites and Student Performance in Business Statistics: Do Math Prerequisites Really Matter?

OpenAIRE

Jeffrey J. Green; Courtenay C. Stone; Abera Zegeye; Thomas A. Charles

2007-01-01

We use a binary probit model to assess the impact of several changes in math prerequisites on student performance in an undergraduate business statistics course. While the initial prerequisites did not necessarily provide students with the necessary math skills, our study, the first to examine the effect of math prerequisite changes, shows that these changes were deleterious to student performance. Our results helped convince the College of Business to change the math prerequisite again begin...

Gender Wage Gap: Discrimination or Different Preferences of Men and Women? A Case Study of Ostrava, Czech Republic

OpenAIRE

Zuzana Machová; Lenka Filipová

2013-01-01

This paper was written as a part of a research project studying problem of wage determinant measuring and wage discrimination considering different wage requirements of men and women. The wage determinants and gender wage discrimination are analyzed using a probit model. The whole analysis is methodologically based on Mincer’s Wage Regression and Oaxaca-Blinder decomposition of gender wage gap. The wage variables include, aside from standard personal characteristics, dummies for institution...

Significant pre-accession factors predicting success or failure during a Marine Corps officer’s initial service obligation

OpenAIRE

Johnson, Jacob A.

2015-01-01

Approved for public release; distribution is unlimited Increasing diversity and equal opportunity in the military is a congressional and executive priority. At the same time, improving recruiting practices is a priority of the commandant of the Marine Corps. In an effort to provide information to the Marine Corps that may improve recruiting practice and enable retention of a higher quality and more diverse officer corps, probit econometric models are estimated to identify significant facto...

The Role of Credit in Predicting US Recessions

DEFF Research Database (Denmark)

Pönkä, Harri

are useful predictors of US recessions over and above the control variables both in and out of sample. Especially the excess bond premium, capturing the cyclical changes in the relationship between default risk and credit spreads, is found to be a powerful predictor. Overall, models that combine credit......We study the role of credit in forecasting US recession periods with probit models. We employ both classical recession predictors and common factors based on a large panel of financial and macroeconomic variables as control variables. Our findings suggest that a number of credit variables...

Users evaluation of transport mode characteristics with special attention to public transport

NARCIS (Netherlands)

Waerden, van der P.J.H.J.; Borgers, A.W.J.; Timmermans, H.J.P.; Berenos, M.

2007-01-01

The paper focuses on the influence of public transport use on the evaluation of transport mode characteristics. Based on stated choice data, several multinomial logit models that include parameters representing differences between users and non-users of public transport are estimated. The estimation

Treatment choices for fevers in children under-five years in a rural Ghanaian district

Directory of Open Access Journals (Sweden)

Gyapong Margaret

2010-06-01

Full Text Available Abstract Background Health care demand studies help to examine the behaviour of individuals and households during illnesses. Few of existing health care demand studies examine the choice of treatment services for childhood illnesses. Besides, in their analyses, many of the existing studies compare alternative treatment options to a single option, usually self-medication. This study aims at examining the factors that influence the choices that caregivers of children under-five years make regarding treatment of fevers due to malaria and pneumonia in a rural setting. The study also examines how the choice of alternative treatment options compare with each other. Methods The study uses data from a 2006 household socio-economic survey and health and demographic surveillance covering caregivers of 529 children under-five years of age in the Dangme West District and applies a multinomial probit technique to model the choice of treatment services for fevers in under-fives in rural Ghana. Four health care options are considered: self-medication, over-the-counter providers, public providers and private providers. Results The findings indicate that longer travel, waiting and treatment times encourage people to use self-medication and over-the-counter providers compared to public and private providers. Caregivers with health insurance coverage also use care from public providers compared to over-the-counter or private providers. Caregivers with higher incomes use public and private providers over self-medication while higher treatment charges and longer times at public facilities encourage caregivers to resort to private providers. Besides, caregivers of female under-fives use self-care while caregivers of male under-fives use public providers instead of self-care, implying gender disparity in the choice of treatment. Conclusions The results of this study imply that efforts at curbing under-five mortality due to malaria and pneumonia need to take into

A Typology of Work-Family Arrangements among Dual-Earner Couples in Norway

Science.gov (United States)

Kitterod, Ragni Hege; Lappegard, Trude

2012-01-01

A symmetrical family model of two workers or caregivers is a political goal in many western European countries. We explore how common this family type is in Norway, a country with high gender-equality ambitions, by using a multinomial latent class model to develop a typology of dual-earner couples with children based on the partners' allocations…

An anatomic risk model to screen post endovascular aneurysm repair patients for aneurysm sac enlargement.

Science.gov (United States)

Png, Chien Yi M; Tadros, Rami O; Beckerman, William E; Han, Daniel K; Tardiff, Melissa L; Torres, Marielle R; Marin, Michael L; Faries, Peter L

2017-09-01

Follow-up computed tomography angiography (CTA) scans add considerable postimplantation costs to endovascular aneurysm repairs (EVARs) of abdominal aortic aneurysms (AAAs). By building a risk model, we hope to identify patients at low risk for aneurysm sac enlargement to minimize unnecessary CTAs. 895 consecutive patients who underwent EVAR for AAA were reviewed, of which 556 met inclusion criteria. A Probit model was created for aneurysm sac enlargement, with preoperative aneurysm morphology, patient demographics, and operative details as variables. Our final model included 287 patients and had a sensitivity of 100%, a specificity of 68.9%, and an accuracy of 70.4%. Ninety-nine (35%) of patients were assigned to the high-risk group, whereas 188 (65%) of patients were assigned to the low-risk group. Notably, regarding anatomic variables, our model reported that age, pulmonary comorbidities, aortic neck diameter, iliac artery length, and aneurysms were independent predictors of post-EVAR sac enlargement. With the exception of age, all statistically significant variables were qualitatively supported by prior literature. With regards to secondary outcomes, the high-risk group had significantly higher proportions of AAA-related deaths (5.1% versus 1.1%, P = 0.037) and Type 1 endoleaks (9.1% versus 3.2%, P = 0.033). Our model is a decent predictor of patients at low risk for post AAA EVAR aneurysm sac enlargement and associated complications. With additional validation and refinement, it could be applied to practices to cut down on the overall need for postimplantation CTA. Copyright © 2017 Elsevier Inc. All rights reserved.

Evaluation of the laboratory mouse model for screening topical mosquito repellents.

Science.gov (United States)

Rutledge, L C; Gupta, R K; Wirtz, R A; Buescher, M D

1994-12-01

Eight commercial repellents were tested against Aedes aegypti 0 and 4 h after application in serial dilution to volunteers and laboratory mice. Results were analyzed by multiple regression of percentage of biting (probit scale) on dose (logarithmic scale) and time. Empirical correction terms for conversion of values obtained in tests on mice to values expected in tests on human volunteers were calculated from data obtained on 4 repellents and evaluated with data obtained on 4 others. Corrected values from tests on mice did not differ significantly from values obtained in tests on volunteers. Test materials used in the study were dimethyl phthalate, butopyronoxyl, butoxy polypropylene glycol, MGK Repellent 11, deet, ethyl hexanediol, Citronyl, and dibutyl phthalate.

How much do incentives affect car purchase? Agent-based microsimulation of consumer choice of new cars. Part 1. Model structure, simulation of bounded rationality, and model validation

Energy Technology Data Exchange (ETDEWEB)

Mueller, Michel G.; Haan, Peter de [ETH Zurich, Institute for Environmental Decisions, Natural and Social Science Interface, Universitaetstr. 22, CHN J 73.2, 8092 Zurich (Switzerland)

2009-03-15

This article presents an agent-based microsimulation capable of forecasting the effects of policy levers that influence individual choices of new passenger cars. The fundamental decision-making units are households distinguished by sociodemographic characteristics and car ownership. A two-stage model of individual decision processes is employed. In the first stage, individual choice sets are constructed using simple, non-compensatory rules that are based on previously owned cars. Second, decision makers evaluate alternatives in their individual choice set using a multi-attributive weighting rule. The attribute weights are based on a multinomial logit model for cross-country policy analysis in European countries. Additionally, prospect theory and the notion of mental accounting are used to model the perception of monetary values. The microsimulation forecasts actual market observations with high accuracy, both on the level of aggregate market characteristics as well as on a highly resolved level of distributions of market shares. The presented approach is useful for the assessment of policies that influence individual purchase decisions of new passenger cars; it allows accounting for a highly resolved car fleet and differentiated consumer segments. As a result, the complexity of incentive schemes can be represented and detailed structural changes can be investigated. (author)

How much do incentives affect car purchase? Agent-based microsimulation of consumer choice of new cars-Part I: Model structure, simulation of bounded rationality, and model validation

Energy Technology Data Exchange (ETDEWEB)

Mueller, Michel G. [ETH Zurich, Institute for Environmental Decisions, Natural and Social Science Interface, Universitaetstr. 22, CHN J 73.2, 8092 Zurich (Switzerland); Haan, Peter de [ETH Zurich, Institute for Environmental Decisions, Natural and Social Science Interface, Universitaetstr. 22, CHN J 73.2, 8092 Zurich (Switzerland)], E-mail: peter.dehaan@env.ethz.ch

2009-03-15

This article presents an agent-based microsimulation capable of forecasting the effects of policy levers that influence individual choices of new passenger cars. The fundamental decision-making units are households distinguished by sociodemographic characteristics and car ownership. A two-stage model of individual decision processes is employed. In the first stage, individual choice sets are constructed using simple, non-compensatory rules that are based on previously owned cars. Second, decision makers evaluate alternatives in their individual choice set using a multi-attributive weighting rule. The attribute weights are based on a multinomial logit model for cross-country policy analysis in European countries. Additionally, prospect theory and the notion of mental accounting are used to model the perception of monetary values. The microsimulation forecasts actual market observations with high accuracy, both on the level of aggregate market characteristics as well as on a highly resolved level of distributions of market shares. The presented approach is useful for the assessment of policies that influence individual purchase decisions of new passenger cars; it allows accounting for a highly resolved car fleet and differentiated consumer segments. As a result, the complexity of incentive schemes can be represented and detailed structural changes can be investigated.

How much do incentives affect car purchase? Agent-based microsimulation of consumer choice of new cars-Part I: Model structure, simulation of bounded rationality, and model validation

International Nuclear Information System (INIS)

Mueller, Michel G.; Haan, Peter de

2009-01-01

This article presents an agent-based microsimulation capable of forecasting the effects of policy levers that influence individual choices of new passenger cars. The fundamental decision-making units are households distinguished by sociodemographic characteristics and car ownership. A two-stage model of individual decision processes is employed. In the first stage, individual choice sets are constructed using simple, non-compensatory rules that are based on previously owned cars. Second, decision makers evaluate alternatives in their individual choice set using a multi-attributive weighting rule. The attribute weights are based on a multinomial logit model for cross-country policy analysis in European countries. Additionally, prospect theory and the notion of mental accounting are used to model the perception of monetary values. The microsimulation forecasts actual market observations with high accuracy, both on the level of aggregate market characteristics as well as on a highly resolved level of distributions of market shares. The presented approach is useful for the assessment of policies that influence individual purchase decisions of new passenger cars; it allows accounting for a highly resolved car fleet and differentiated consumer segments. As a result, the complexity of incentive schemes can be represented and detailed structural changes can be investigated

Approximating The DCM

DEFF Research Database (Denmark)

Madsen, Rasmus Elsborg

2005-01-01

The Dirichlet compound multinomial (DCM), which has recently been shown to be well suited for modeling for word burstiness in documents, is here investigated. A number of conceptual explanations that account for these recent results, are provided. An exponential family approximation of the DCM...

Ordering the preference hierarchies for internal finance, bank laons, bond and share issue: evidence from Dutch firms

NARCIS (Netherlands)

de Haan, L.; Hinloopen, J.

2003-01-01

We estimate the incremental financing decision for a sample of some 150 Dutch companies for the years 1984 through 1997, thereby distinguishing internal finance and three types of external finance: bank borrowing, bond issues, and share issues. First, we estimate a multinomial logit model, which

The joint choice of tenure, dwelling type, size and location: the effect of home-oriented versus culture-oriented lifestyle

DEFF Research Database (Denmark)

Frenkel, Amnon; Kaplan, Sigal

2014-01-01

This study investigates knowledge-workers’ housing preferences in terms of tenure, dwelling type, location and size. The analysis is conducted by applying a joint multinomial-logit ordered-response model to the housing choices of knowledgeworkers residing in the Tel-Aviv metropolitan region and w...

The role of agri-business incentive on under-five child immunization ...

African Journals Online (AJOL)

A multinomial logistic regression model used to analyze the determinant of partial or noneimmunized. Maternal health practices and access to a motivating intervention are significant factors that ensure a parent/guardian's compliance to their child immunization. The study recommends sustainability and diversification of ...

Patterns and correlates of solid waste disposal practices in Dar es ...

African Journals Online (AJOL)

This study examines the patterns and correlations of solid waste disposal practices among households in urbanized and populated Dar es Salaam city in Tanzania. The Tanzanian Household Budget Survey (HBS) data covering many households' characteristics was used. Multinomial Logit (MNL) model was applied to ...

Evaluating the Locational Attributes of Education Management Organizations (EMOs)

Science.gov (United States)

Gulosino, Charisse; Miron, Gary

2017-01-01

This study uses logistic and multinomial logistic regression models to analyze neighborhood factors affecting EMO (Education Management Organization)-operated schools' locational attributes (using census tracts) in 41 states for the 2014-2015 school year. Our research combines market-based school reform, institutional theory, and resource…

Multinomial malware classification based on call graphs

OpenAIRE

Østbye, Morten Oscar

2017-01-01

Ever since the computer was invented, people have found ways to evolve interaction or simplify tasks with computational resources, this for both good and bad. For the known lifespan of the digital age, malicious software (malware) has been a constant threat to computer systems. Malware has been the cause of enormous damage related to both governmental and private sectors, but also for individuals. Malware has evolved to target different systems and environments and therefore there exists a va...

The mental health cost of corruption: evidence from Sub-Saharan Africa

OpenAIRE

Gillanders, Robert

2011-01-01

This paper examines the effect that experiencing corruption has on an individual’s mental health using microeconomic data from the Afrobarometer surveys. The results show a statistically significant and economically meaningful effect in both binary and ordered probit models using both an experience of corruption index and a simple binary variable. Having to pay a bribe to obtain documents and permits, to avoid problems with the police or to access medical care emerge as the arenas in which co...

Crime and Young Men: The Role of Arrest, Criminal Experience, and Heterogeneity

OpenAIRE

Susumu Imai; Hajime Katayama; Kala Krishna

2006-01-01

Using National Youth Survey (NYS) data, we examine the relationship of current criminal activity and past arrests using an ordered probit model with unobserved heterogeneity. Past arrests raise current criminal activity only for the non-criminal type, while past criminal experience raises current criminal activity for both types. Also, the age crime profile peaks at age 18 for non-criminal type individuals, but for criminal type individuals, it continues to rise with age. Past research indica...

I Can't Get No Satisfaction: The Power of Perceived Differences in Employee Retention and Turnover

OpenAIRE

Gevrek, Deniz; Spencer, Marilyn; Hudgins, David; Chambers, Valrie

2017-01-01

This study explores the role of salary raises and the perception of employees of these salary raises on employees' intended retention and turnover. By using a unique survey data set from an American university, this study investigates a novel hypothesis that faculty perceptions of salary raises, relative to their perceptions of other faculty members' assessments of the raises, influences their labor supply. Using both Ordered Probit and OLS modelling frameworks, we focus on the impact of sala...

Taxes and Bribes in Uganda

OpenAIRE

Jagger, Pamela; Shively, Gerald

2014-01-01

Using data from 433 firms operating along Uganda’s charcoal and timber supply chains we investigate patterns of bribe payment and tax collection between supply chain actors and government officials responsible for collecting taxes and fees. We examine the factors associated with the presence and magnitude of bribe and tax payments using a series of bivariate probit and Tobit regression models. We find empirical support for a number of hypotheses related to payments, highlighting the role of q...

The determinants of part-time work in Metropolitan Lima

OpenAIRE

Manuel Enrique Saavedra Martinez

2011-01-01

The following paper examines the part-time work in Metropolitan Lima in 2008. The overall objective is to identify the determinants of the incidence of part-time work in Lima. We worked with one Probit econometric model, measured by the National Survey of Households (NSH), which explores the job characteristics of people. This will determine the presence of part-time workers in the areas of trade, health, education and communication; also realized that this group has completed university stud...

The determinants of part-time work in Metropolitan Lima

OpenAIRE

Saavedra Martinez, Manuel Enrique

2012-01-01

The following paper examines the part-time work in Metropolitan Lima in 2008. The overall objective is to identify the determinants of the incidence of part-time work in Lima. We worked with one Probit econometric model, measured by the National Survey of Households (NSH), which explores the job characteristics of people. This will determine the presence of part-time workers in the areas of trade, health, education and communication; also realized that this group has completed university stud...

The Diffusion of Military Dictatorships

OpenAIRE

Raul Caruso; Ilaria Petrarca; Roberto Ricciuti

2012-01-01

We show the existence of a diffusion process of military dictatorships in Sub-Saharan Africa from 1972 through 2007, using panel data probit estimation and a Markov chain transition model. This process is shortly-lived, since we observe an overall trend that reduces the number of military regimes. We also find that Manufacturing share of GDP, Primary share of GDP positively affect the probability of military dictatorship, and Openness to trade, whereas the British colonial origin are negative...

Consumer Acceptance of Eco-Labeled Fish: A Mexican Case Study

OpenAIRE

Pérez-Ramírez, Mónica; Almendarez-Hernández, Marco; Avilés-Polanco, Gerzaín; Beltrán-Morales, Luis

2015-01-01

Fish eco-labeling is a market-based incentive program for sustainable fisheries. This paper examines consumers’ acceptance of eco-labeled fish by using data from a pilot study conducted in a coastal area of northwestern Mexico. An ordered probit model was applied, using 364 observations. The results show that most respondents favor the idea of eco-labeled fish as a sustainable option and know that this is a costlier option. Income level, consumers’ occupation and frequency of fish consumption...

Menarcheal age of girls from dysfunctional families.

Science.gov (United States)

Toromanović, Alma; Tahirović, Husref

2004-07-01

The objective of the present study was to determine median age at menarche and the influence of familial instability on maturation. The sample included 7047 girls between the ages of 9 and 17 years from Tuzla Canton. The girls were divided into two groups. Group A (N=5230) comprised girls who lived in families free of strong traumatic events. Group B (N=1817) included girls whose family dysfunction exposed them to prolonged distress. Probit analysis was performed to estimate mean menarcheal age using the Probit procedure of SAS package. The mean menarcheal age calculated by probit analysis for all the girls studied was 13.07 years. In girls from dysfunctional families a very clear shift toward earlier maturation was observed. The mean age at menarche for group B was 13.0 years, which was significantly lower that that for group A, 13.11 years (t=2.92, Pdysfunctional families mature not later but even earlier than girls from normal families. This supports the hypothesis that stressful childhood life events accelerate maturation of girls.

A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

Science.gov (United States)

Karabatsos, George

2017-02-01

Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected

Factors influencing the planning of social activities : empirical analysis of social interaction diary data

NARCIS (Netherlands)

Berg, van den P.E.W.; Arentze, T.A.; Timmermans, H.J.P.

2010-01-01

Results of a study on the planning of social activities are reported. Data collected in the Netherlands from social interaction diaries were used to estimate a multinomial logistic regression model to analyze whether a social activity is prearranged, routine, or spontaneous as a function of personal

Multidimensional Computerized Adaptive Testing for Indonesia Junior High School Biology

Science.gov (United States)

Kuo, Bor-Chen; Daud, Muslem; Yang, Chih-Wei

2015-01-01

This paper describes a curriculum-based multidimensional computerized adaptive test that was developed for Indonesia junior high school Biology. In adherence to the Indonesian curriculum of different Biology dimensions, 300 items was constructed, and then tested to 2238 students. A multidimensional random coefficients multinomial logit model was…

Relating cost-benefit analysis results with transport project decisions in the Netherlands

NARCIS (Netherlands)

Annema, Jan Anne; Frenken, Koen|info:eu-repo/dai/nl/207145253; Koopmans, Carl; Kroesen, Maarten

2017-01-01

This paper relates the cost-benefit analysis (CBA) results of transportation policy proposals in the Netherlands with the decision to implement or abandon the proposal. The aim of this study is to explore the relation between the CBA results and decision-making. Multinomial logit regression models

The determinants of the location of foreign direct investment in UK regions

NARCIS (Netherlands)

Dimitropoulou, Dimitra; McCann, Philip; Burke, Simon P.

2013-01-01

This article employs a database of over 2000 observations of Foreign Direct Investment (FDI) projects in UK regions. We analyse this data by means of various multinomial and conditional logit models in order to identify the major determinants of the location choices of these inward investments.

Entrepreneurial engagement levels in the European Union

NARCIS (Netherlands)

I. Grilo (Isabel); A.R. Thurik (Roy)

2005-01-01

textabstractA multinomial logit model and survey data from the 25 EU member states and the US are used to establish the effect of demographic and other variables on various entrepreneurial engagement levels. These engagement levels range from never thought about starting a business to thinking

Study of Machine-Learning Classifier and Feature Set Selection for Intent Classification of Korean Tweets about Food Safety

Directory of Open Access Journals (Sweden)

Yeom, Ha-Neul

2014-09-01

Full Text Available In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.

ADOPT: A Historically Validated Light Duty Vehicle Consumer Choice Model

Energy Technology Data Exchange (ETDEWEB)

Brooker, A.; Gonder, J.; Lopp, S.; Ward, J.

2015-05-04

The Automotive Deployment Option Projection Tool (ADOPT) is a light-duty vehicle consumer choice and stock model supported by the U.S. Department of Energy’s Vehicle Technologies Office. It estimates technology improvement impacts on U.S. light-duty vehicles sales, petroleum use, and greenhouse gas emissions. ADOPT uses techniques from the multinomial logit method and the mixed logit method estimate sales. Specifically, it estimates sales based on the weighted value of key attributes including vehicle price, fuel cost, acceleration, range and usable volume. The average importance of several attributes changes nonlinearly across its range and changes with income. For several attributes, a distribution of importance around the average value is used to represent consumer heterogeneity. The majority of existing vehicle makes, models, and trims are included to fully represent the market. The Corporate Average Fuel Economy regulations are enforced. The sales feed into the ADOPT stock model. It captures key aspects for summing petroleum use and greenhouse gas emissions This includes capturing the change in vehicle miles traveled by vehicle age, the creation of new model options based on the success of existing vehicles, new vehicle option introduction rate limits, and survival rates by vehicle age. ADOPT has been extensively validated with historical sales data. It matches in key dimensions including sales by fuel economy, acceleration, price, vehicle size class, and powertrain across multiple years. A graphical user interface provides easy and efficient use. It manages the inputs, simulation, and results.

The transaction costs driving captive power generation: Evidence from India

International Nuclear Information System (INIS)

Ghosh, Ranjan; Kathuria, Vinish

2014-01-01

The 2003 Indian Electricity Act incentivizes captive power production through open access in an attempt to harness all sources of generation. Yet, we observe that only some firms self-generate while others do not. In this paper we give a transaction cost explanation for such divergent behavior. Using a primary survey of 107 firms from India, we construct a distinct variable to measure the transaction-specificity of electricity use. The ‘make or buy’ decision is then econometrically tested using probit model. Results are highly responsive to transaction-specificity and the likelihood of captive power generation is positively related to it. At the industrial level, this explains why food and chemical firms are more likely to make their own electricity. Since the burden of poor grid supply is highest on smaller sized and high transaction-specific firms, the grid access policies need to account for firm-level characteristics if government wants to incentivize captive power generation. - Highlights: • We analyze why some firms opt for captive power generation while others do not. • We examine the role of transaction costs in this decision making using probit model. • Unique data from a primary survey of manufacturing firms in Andhra Pradesh, India. • Transaction-specificity significantly determines who installs captive power plant (CPP). • Firm-level characteristics crucial in policies incentivizing captive generation

Finding A Minimally Informative Dirichlet Prior Using Least Squares

International Nuclear Information System (INIS)

Kelly, Dana

2011-01-01

In a Bayesian framework, the Dirichlet distribution is the conjugate distribution to the multinomial likelihood function, and so the analyst is required to develop a Dirichlet prior that incorporates available information. However, as it is a multiparameter distribution, choosing the Dirichlet parameters is less straightforward than choosing a prior distribution for a single parameter, such as p in the binomial distribution. In particular, one may wish to incorporate limited information into the prior, resulting in a minimally informative prior distribution that is responsive to updates with sparse data. In the case of binomial p or Poisson λ, the principle of maximum entropy can be employed to obtain a so-called constrained noninformative prior. However, even in the case of p, such a distribution cannot be written down in the form of a standard distribution (e.g., beta, gamma), and so a beta distribution is used as an approximation in the case of p. In the case of the multinomial model with parametric constraints, the approach of maximum entropy does not appear tractable. This paper presents an alternative approach, based on constrained minimization of a least-squares objective function, which leads to a minimally informative Dirichlet prior distribution. The alpha-factor model for common-cause failure, which is widely used in the United States, is the motivation for this approach, and is used to illustrate the method. In this approach to modeling common-cause failure, the alpha-factors, which are the parameters in the underlying multinomial model for common-cause failure, must be estimated from data that are often quite sparse, because common-cause failures tend to be rare, especially failures of more than two or three components, and so a prior distribution that is responsive to updates with sparse data is needed.

Finding a minimally informative Dirichlet prior distribution using least squares

International Nuclear Information System (INIS)

Kelly, Dana; Atwood, Corwin

2011-01-01

In a Bayesian framework, the Dirichlet distribution is the conjugate distribution to the multinomial likelihood function, and so the analyst is required to develop a Dirichlet prior that incorporates available information. However, as it is a multiparameter distribution, choosing the Dirichlet parameters is less straightforward than choosing a prior distribution for a single parameter, such as p in the binomial distribution. In particular, one may wish to incorporate limited information into the prior, resulting in a minimally informative prior distribution that is responsive to updates with sparse data. In the case of binomial p or Poisson λ, the principle of maximum entropy can be employed to obtain a so-called constrained noninformative prior. However, even in the case of p, such a distribution cannot be written down in the form of a standard distribution (e.g., beta, gamma), and so a beta distribution is used as an approximation in the case of p. In the case of the multinomial model with parametric constraints, the approach of maximum entropy does not appear tractable. This paper presents an alternative approach, based on constrained minimization of a least-squares objective function, which leads to a minimally informative Dirichlet prior distribution. The alpha-factor model for common-cause failure, which is widely used in the United States, is the motivation for this approach, and is used to illustrate the method. In this approach to modeling common-cause failure, the alpha-factors, which are the parameters in the underlying multinomial model for common-cause failure, must be estimated from data that are often quite sparse, because common-cause failures tend to be rare, especially failures of more than two or three components, and so a prior distribution that is responsive to updates with sparse data is needed.

Finding a Minimally Informative Dirichlet Prior Distribution Using Least Squares

International Nuclear Information System (INIS)

Kelly, Dana; Atwood, Corwin

2011-01-01

In a Bayesian framework, the Dirichlet distribution is the conjugate distribution to the multinomial likelihood function, and so the analyst is required to develop a Dirichlet prior that incorporates available information. However, as it is a multiparameter distribution, choosing the Dirichlet parameters is less straight-forward than choosing a prior distribution for a single parameter, such as p in the binomial distribution. In particular, one may wish to incorporate limited information into the prior, resulting in a minimally informative prior distribution that is responsive to updates with sparse data. In the case of binomial p or Poisson, the principle of maximum entropy can be employed to obtain a so-called constrained noninformative prior. However, even in the case of p, such a distribution cannot be written down in closed form, and so an approximate beta distribution is used in the case of p. In the case of the multinomial model with parametric constraints, the approach of maximum entropy does not appear tractable. This paper presents an alternative approach, based on constrained minimization of a least-squares objective function, which leads to a minimally informative Dirichlet prior distribution. The alpha-factor model for common-cause failure, which is widely used in the United States, is the motivation for this approach, and is used to illustrate the method. In this approach to modeling common-cause failure, the alpha-factors, which are the parameters in the underlying multinomial aleatory model for common-cause failure, must be estimated from data that is often quite sparse, because common-cause failures tend to be rare, especially failures of more than two or three components, and so a prior distribution that is responsive to updates with sparse data is needed.

Street Choice Logit Model for Visitors in Shopping Districts

Science.gov (United States)

Kawada, Ko; Yamada, Takashi; Kishimoto, Tatsuya

2014-01-01

In this study, we propose two models for predicting people’s activity. The first model is the pedestrian distribution prediction (or postdiction) model by multiple regression analysis using space syntax indices of urban fabric and people distribution data obtained from a field survey. The second model is a street choice model for visitors using multinomial logit model. We performed a questionnaire survey on the field to investigate the strolling routes of 46 visitors and obtained a total of 1211 street choices in their routes. We proposed a utility function, sum of weighted space syntax indices, and other indices, and estimated the parameters for weights on the basis of maximum likelihood. These models consider both street networks, distance from destination, direction of the street choice and other spatial compositions (numbers of pedestrians, cars, shops, and elevation). The first model explains the characteristics of the street where many people tend to walk or stay. The second model explains the mechanism underlying the street choice of visitors and clarifies the differences in the weights of street choice parameters among the various attributes, such as gender, existence of destinations, number of people, etc. For all the attributes considered, the influences of DISTANCE and DIRECTION are strong. On the other hand, the influences of Int.V, SHOPS, CARS, ELEVATION, and WIDTH are different for each attribute. People with defined destinations tend to choose streets that “have more shops, and are wider and lower”. In contrast, people with undefined destinations tend to choose streets of high Int.V. The choice of males is affected by Int.V, SHOPS, WIDTH (positive) and CARS (negative). Females prefer streets that have many shops, and couples tend to choose downhill streets. The behavior of individual persons is affected by all variables. The behavior of people visiting in groups is affected by SHOP and WIDTH (positive). PMID:25379274

Street Choice Logit Model for Visitors in Shopping Districts

Directory of Open Access Journals (Sweden)

Ko Kawada

2014-07-01

Full Text Available In this study, we propose two models for predicting people’s activity. The first model is the pedestrian distribution prediction (or postdiction model by multiple regression analysis using space syntax indices of urban fabric and people distribution data obtained from a field survey. The second model is a street choice model for visitors using multinomial logit model. We performed a questionnaire survey on the field to investigate the strolling routes of 46 visitors and obtained a total of 1211 street choices in their routes. We proposed a utility function, sum of weighted space syntax indices, and other indices, and estimated the parameters for weights on the basis of maximum likelihood. These models consider both street networks, distance from destination, direction of the street choice and other spatial compositions (numbers of pedestrians, cars, shops, and elevation. The first model explains the characteristics of the street where many people tend to walk or stay. The second model explains the mechanism underlying the street choice of visitors and clarifies the differences in the weights of street choice parameters among the various attributes, such as gender, existence of destinations, number of people, etc. For all the attributes considered, the influences of DISTANCE and DIRECTION are strong. On the other hand, the influences of Int.V, SHOPS, CARS, ELEVATION, and WIDTH are different for each attribute. People with defined destinations tend to choose streets that “have more shops, and are wider and lower”. In contrast, people with undefined destinations tend to choose streets of high Int.V. The choice of males is affected by Int.V, SHOPS, WIDTH (positive and CARS (negative. Females prefer streets that have many shops, and couples tend to choose downhill streets. The behavior of individual persons is affected by all variables. The behavior of people visiting in groups is affected by SHOP and WIDTH (positive.

Test Design Project: Studies in Test Adequacy. Annual Report.

Science.gov (United States)

Wilcox, Rand R.

These studies in test adequacy focus on two problems: procedures for estimating reliability, and techniques for identifying ineffective distractors. Fourteen papers are presented on recent advances in measuring achievement (a response to Molenaar); "an extension of the Dirichlet-multinomial model that allows true score and guessing to be…

Has the Euro Affected the Choice of Invoicing Currency?

NARCIS (Netherlands)

Ligthart, J.E.; Werner, S.E.V.

2010-01-01

We present a new approach to study empirically the effect of the introduction of the euro on currency invoicing. Our approach uses a compositional multinomial logit model, in which currency choice depends on the characteristics of both the currency and the country. We use unique quarterly panel data

Heterogeneity in urban park use of aging visitors: a latent class analysis

NARCIS (Netherlands)

Kemperman, A.D.A.M.; Timmermans, H.J.P.

2006-01-01

This study describes and predicts urban parks use patterns for various age groups, with specific attention to the growing group of older adults. Park use intensity of various age groups is described. Subsequently, a multinomial logit model is estimated to describe urban park choice as a function of

Mobility in the Urban Labor Market : A Panel Data Analysis for Mexico

NARCIS (Netherlands)

Gong, X.; van Soest, A.H.O.; Villagomez, E.

2000-01-01

We analyze mobility in urban Mexico between three labor market states: working in the formal sector, working in the informal sector, and not working. We use a dynamic multinomial logit panel data model with random effects, explaining the labor market state of each individual during each time period.

Consumer fair prices for less pesticide in potato

Directory of Open Access Journals (Sweden)

C. SEREFOGLU

2016-03-01

Full Text Available This study estimates Turkish citizens’ willingness to pay (WTP for reduced pesticides on potatoes.These estimates rely on data collected from 393 persons covering all regions in Turkey throughan online survey during the period from June 22 - July 21, 2014. The average WTP was found to be about TL 1.68 for all observations including zero bids and TL 2.91 excluding zero bids. The results of the probit model show that cosmetic defects, free-pesticide potatoes with insect damages, age, and gender were identified by the model to have significant impacts on the probability of WTP.

A Dynamic Electricity Tariff Survey for Smart Grid in South Korea

Directory of Open Access Journals (Sweden)

Eunjoo Kim

2017-02-01

Full Text Available In this paper, an analysis for consumer perception of the level of electricity price, the amount of household electricity consumption and consumer perception on dynamic electricity pricing system in South Korea are investigated. A survey was conducted between July 24 and August 17, 2015 and then for the preference analysis, Binary Logistic Model is applied for the acceptance, Ordered Probit Model is applied. The major findings say that the less they have monthly income, the more satisfied dynamic pricing. In dynamic electricity tariff, real time pricing is most preferred dynamic pricing system and it reaches about 40% of respondents.

Testing indirect effect of consumer attitudes toward a product

DEFF Research Database (Denmark)

Hrubá, Renata; Sudzina, Frantisek

2016-01-01

a questionnaire in 2010-2011. The model is estimated using probit analysis to predict relationship between producer and consumer in decision-making when buying a new type of cheese and to examine consumer attitudes toward food origins and nutrient food security. It can be concluded that the indirect effect (e...... and on the indirect and direct effects of the perception of information through information behavior and the use of the model ordered. It is proposed that consumer levels of product familiarity of attributes affects behavior. Consumer attitudes towards agri-food products and behaviour were analyzed through...

Cultural consensus modeling to measure transactional sex in Swaziland: Scale building and validation.

Science.gov (United States)

Fielding-Miller, Rebecca; Dunkle, Kristin L; Cooper, Hannah L F; Windle, Michael; Hadley, Craig

2016-01-01

Transactional sex is associated with increased risk of HIV and gender based violence in southern Africa and around the world. However the typical quantitative operationalization, "the exchange of gifts or money for sex," can be at odds with a wide array of relationship types and motivations described in qualitative explorations. To build on the strengths of both qualitative and quantitative research streams, we used cultural consensus models to identify distinct models of transactional sex in Swaziland. The process allowed us to build and validate emic scales of transactional sex, while identifying key informants for qualitative interviews within each model to contextualize women's experiences and risk perceptions. We used logistic and multinomial logistic regression models to measure associations with condom use and social status outcomes. Fieldwork was conducted between November 2013 and December 2014 in the Hhohho and Manzini regions. We identified three distinct models of transactional sex in Swaziland based on 124 Swazi women's emic valuation of what they hoped to receive in exchange for sex with their partners. In a clinic-based survey (n = 406), consensus model scales were more sensitive to condom use than the etic definition. Model consonance had distinct effects on social status for the three different models. Transactional sex is better measured as an emic spectrum of expectations within a relationship, rather than an etic binary relationship type. Cultural consensus models allowed us to blend qualitative and quantitative approaches to create an emicly valid quantitative scale grounded in qualitative context. Copyright © 2015 Elsevier Ltd. All rights reserved.

Emotionally enhanced memory for negatively arousing words: storage or retrieval advantage?

Science.gov (United States)

Nadarevic, Lena

2017-12-01

People typically remember emotionally negative words better than neutral words. Two experiments are reported that investigate whether emotionally enhanced memory (EEM) for negatively arousing words is based on a storage or retrieval advantage. Participants studied non-word-word pairs that either involved negatively arousing or neutral target words. Memory for these target words was tested by means of a recognition test and a cued-recall test. Data were analysed with a multinomial model that allows the disentanglement of storage and retrieval processes in the present recognition-then-cued-recall paradigm. In both experiments the multinomial analyses revealed no storage differences between negatively arousing and neutral words but a clear retrieval advantage for negatively arousing words in the cued-recall test. These findings suggest that EEM for negatively arousing words is driven by associative processes.

International Sign Predictability of Stock Returns: The Role of the United States

DEFF Research Database (Denmark)

Nyberg, Henri; Pönkä, Harri

from the U.S. to foreign markets. We introduce a new bivariate probit model that allows for such a contemporaneous predictive linkage from one market to the other. Our in-sample and out-of-sample forecasting results indicate superior predictive performance of the new model over the competing models...... by statistical measures and market timing performance, suggesting gradual diffusion of predictive information from the U.S. to the other markets.......We study the directional predictability of monthly excess stock market returns in the U.S. and ten other markets using univariate and bivariate binary response models. Our main interest is on the potential benefits of predicting the signs of the returns jointly, focusing on the predictive power...

An integrated model to simulate sown area changes for major crops at a global scale

Institute of Scientific and Technical Information of China (English)

SHIBASAKI; Ryosuke

2008-01-01

Dynamics of land use systems have attracted much attention from scientists around the world due to their ecological and socio-economic implications. An integrated model to dynamically simulate future changes in sown areas of four major crops (rice, maize, wheat and soybean) on a global scale is pre- sented. To do so, a crop choice model was developed on the basis of Multinomial Logit (Logit) model to model land users’ decisions on crop choices among a set of available alternatives with using a crop utility function. A GIS-based Environmental Policy Integrated Climate (EPIC) model was adopted to simulate the crop yields under a given geophysical environment and farming management conditions, while the International Food Policy and Agricultural Simulation (IFPSIM) model was utilized to estimate crop price in the international market. The crop choice model was linked with the GIS-based EPIC model and the IFPSIM model through data exchange. This integrated model was then validated against the FAO statistical data in 2001-2003 and the Moderate Resolution Imaging Spectroradiometer (MODIS) global land cover product in 2001. Both validation approaches indicated reliability of the model for ad- dressing the dynamics in agricultural land use and its capability for long-term scenario analysis. Finally, the model application was designed to run over a time period of 30 a, taking the year 2000 as baseline. The model outcomes can help understand and explain the causes, locations and consequences of land use changes, and provide support for land use planning and policy making.

An integrated model to simulate sown area changes for major crops at a global scale

Institute of Scientific and Technical Information of China (English)

WU WenBin; YANG Peng; MENG ChaoYing; SHIBASAKI Ryosuke; ZHOU QingBo; TANG HuaJun; SHI Yun

2008-01-01

Dynamics of land use systems have attracted much attention from scientists around the world due to their ecological and socio-economic implications. An integrated model to dynamically simulate future changes in sown areas of four major crops (rice, maize, wheat and soybean) on a global scale is presented. To do so, a crop choice model was developed on the basis of Multinomial Logit (Logit) model to model land users' decisions on crop choices among a set of available alternatives with using a crop utility function. A GIS-based Environmental Policy Integrated Climate (EPIC) model was adopted to simulate the crop yields under a given geophysical environment and farming management conditions,while the International Food Policy and Agricultural Simulation (IFPSIM) model was utilized to estimate crop price in the international market. The crop choice model was linked with the GIS-based EPIC model and the IFPSIM model through data exchange. This integrated model was then validated against the FAO statistical data in 2001-2003 and the Moderate Resolution Imaging Spectroradiometer (MODIS)global land cover product in 2001. Both validation approaches indicated reliability of the model for addressing the dynamics in agricultural land use and its capability for long-term scenario analysis. Finally,the model application was designed to run over a time period of 30 a, taking the year 2000 as baseline.The model outcomes can help understand and explain the causes, locations and consequences of land use changes, and provide support for land use planning and policy making.

Railway and road discrete choice model for foreign trade freight between Antioquia and the Port of Cartagena

Directory of Open Access Journals (Sweden)

J. D. Pineda-Jaramillo

2016-09-01

Full Text Available Most Colombian freight is transported on roads with barely acceptable conditions, and although there is a speculation about the need for a railway for freight transportation, there is not a study in Colombia showing the variables that influence the modal choice by the companies that generate freight transportation. This article presents the calculation of demand for a hypothetical railway through a discrete choice model. It begins with a qualitative research through focus group techniques to identify the variables that influence the choice of persons responsible for the transportation of large commercial companies in Antioquia (Colombia. The influential variables in the election were the cost and service frequency, and these variables were used to apply a Stated Preference (SP and Revealed Preference (RP survey, then to calibrate a Multinomial Logit Model (MNL, and to estimate the influence of each of them. We show that the probability of railway choice by the studied companies varies between 67% and 93%, depending on differences in these variables.

Culture, Privacy Conception and Privacy Concern: Evidence from Europe before PRISM

OpenAIRE

Omrani, Nessrine; Soulié, Nicolas

2017-01-01

This article analyses individuals’ online privacy concerns between cultural country groups. We use a dataset of more than 14 000 Internet users collected by the European Union in 2010 in 26 EU countries. We use a probit model to examine the variables associated with the probability of being concerned about privacy, in order to draw policy and regulatory implications. The results show that women and poor people are more concerned than their counterparts. People who often use Internet are not p...

Family Friendly Policies and Performance-Based Pay in Companies' Employment Management in Japan : Its Effect on Work Life Balance Satisfaction and Willingness to Job continuity

OpenAIRE

菅原, 佑香

2012-01-01

The Purpose of this paper is to examine the influence of firm's family friendly policies and also the recent introduction of as performance based wage system on employee's motivation. The motivation was measured in terms of work life balance satisfaction and willingness to continuity to work at the same workplace. To analyze these effects, I used probit model and ordinary least squares (OLS), and two step least squares. The main findings is as follows (1) Introduction of various family friend...

Do study grants help refugees find jobs? A case study of the effects of the voluntary sector grants on the education, training and employment of refugees in the United Kingdom

OpenAIRE

Ilmolelian, Peter

2005-01-01

Using the Africa Educational Trust (AET) as a case study, the primary aim of the research was to investigate whether or not the employment outcomes of those refugees who received financial grants to enable them attend their education/training courses were different from those who did not. 122 individuals who applied to AET for grants in 1993/94 were interviewed and data analysed using the Probit model and McNemar's Chi- squared test of significance. The study found that grant holders were mor...

New Men and New Women? A Comparison of Paid Work Propensities from a Panel Data Perspective

OpenAIRE

Booth, Alison L; Jenkins, Stephen P; Serrano, Carlos

1997-01-01

The paper uses BHPS waves 1–5 (1991–5) to compare paid work participation rates of men and women. Year-on-year persistence in paid work propensities is high, but greater for men than women. Non-work persistence is higher for women. Using panel data probit regression models, the paper also investigates why men’s and women’s participation rates differ, comparing the roles of differences in observable characteristics and differences in rates of return to these characteristics, while also control...

INCLUSION OF THE LATENT PERSONALITY VARIABLE IN MULTINOMIAL LOGIT MODELS USING THE 16PF PSYCHOMETRIC TEST

Directory of Open Access Journals (Sweden)

JORGE E. CÓRDOBA MAQUILÓN

2012-01-01

Full Text Available Los modelos de demanda de viajes utilizan principalmente los atributos modales y las características socioeconómicas como variables explicativas. También se ha establecido que las actitudes y percepciones influyen en el comportamiento de los usuarios. Sin embargo, las variables psicológicas del individuo condicionan la conducta del usuario. En este estudio se incluyó la variable latente personalidad, en la estimación del modelo híbrido de elección discreta, el cual constituye una buena alternativa para incorporar los efectos de los factores subjetivos. La variable latente personalidad se evaluó con la prueba psicométrica 16PF de validez internacional. El artículo analiza los resultados de la aplicación de este modelo a una población de empleados y docentes universitarios, y también propone un camino para la utilización de pruebas psicométricas en los modelos híbridos de elección discreta. Nuestros resultados muestran que los modelos híbridos que incluyen variables latentes psicológicas son superiores a los modelos tradicionales que ignoran los efectos de la conducta de los usuarios.

Analysing the Severity and Frequency of Traffic Crashes in Riyadh City Using Statistical Models

Directory of Open Access Journals (Sweden)

Saleh Altwaijri

2012-12-01

Full Text Available Traffic crashes in Riyadh city cause losses in the form of deaths, injuries and property damages, in addition to the pain and social tragedy affecting families of the victims. In 2005, there were a total of 47,341 injury traffic crashes occurred in Riyadh city (19% of the total KSA crashes and 9% of those crashes were severe. Road safety in Riyadh city may have been adversely affected by: high car ownership, migration of people to Riyadh city, high daily trips reached about 6 million, high rate of income, low-cost of petrol, drivers from different nationalities, young drivers and tremendous growth in population which creates a high level of mobility and transport activities in the city. The primary objective of this paper is therefore to explore factors affecting the severity and frequency of road crashes in Riyadh city using appropriate statistical models aiming to establish effective safety policies ready to be implemented to reduce the severity and frequency of road crashes in Riyadh city. Crash data for Riyadh city were collected from the Higher Commission for the Development of Riyadh (HCDR for a period of five years from 1425H to 1429H (roughly corresponding to 2004-2008. Crash data were classified into three categories: fatal, serious-injury and slight-injury. Two nominal response models have been developed: a standard multinomial logit model (MNL and a mixed logit model to injury-related crash data. Due to a severe underreporting problem on the slight injury crashes binary and mixed binary logistic regression models were also estimated for two categories of severity: fatal and serious crashes. For frequency, two count models such as Negative Binomial (NB models were employed and the unit of analysis was 168 HAIs (wards in Riyadh city. Ward-level crash data are disaggregated by severity of the crash (such as fatal and serious injury crashes. The results from both multinomial and binary response models are found to be fairly consistent but

False Memory for Orthographically versus Semantically Similar Words in Adolescents with Dyslexia: A Fuzzy-Trace Theory Perspective

Science.gov (United States)

Obidzinski, Michal; Nieznanski, Marek

2017-01-01

The presented research was conducted in order to investigate the connections between developmental dyslexia and the functioning of verbatim and gist memory traces--assumed in the fuzzy-trace theory. The participants were 71 high school students (33 with dyslexia and 38 without learning difficulties). The modified procedure and multinomial model of…

Predictive features of CT for risk stratifications in patients with primary gastrointestinal stromal tumour

International Nuclear Information System (INIS)

Zhou, Cuiping; Zhang, Xiang; Duan, Xiaohui; Hu, Huijun; Wang, Dongye; Shen, Jun

2016-01-01

To determine the predictive CT imaging features for risk stratifications in patients with primary gastrointestinal stromal tumours (GISTs). One hundred and twenty-nine patients with histologically confirmed primary GISTs (diameter >2 cm) were enrolled. CT imaging features were reviewed. Tumour risk stratifications were determined according to the 2008 NIH criteria where GISTs were classified into four categories according to the tumour size, location, mitosis count, and tumour rupture. The association between risk stratifications and CT features was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis. CT imaging features including tumour margin, size, shape, tumour growth pattern, direct organ invasion, necrosis, enlarged vessels feeding or draining the mass (EVFDM), lymphadenopathy, and contrast enhancement pattern were associated with the risk stratifications, as determined by univariate analysis (P < 0.05). Only lesion size, growth pattern and EVFDM remained independent risk factors in multinomial logistic regression analysis (OR = 3.480-100.384). ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.806 (95 % CI: 0.727-0.885). CT features including lesion size, tumour growth pattern, and EVFDM were predictors of the risk stratifications for GIST. (orig.)

Dietary Fiber Intake Is Inversely Associated with Periodontal Disease among US Adults.

Science.gov (United States)

Nielsen, Samara Joy; Trak-Fellermeier, Maria Angelica; Joshipura, Kaumudi; Dye, Bruce A

2016-12-01

Approximately 47% of adults in the United States have periodontal disease. Dietary guidelines recommend a diet providing adequate fiber. Healthier dietary habits, particularly an increased fiber intake, may contribute to periodontal disease prevention. Our objective was to evaluate the relation of dietary fiber intake and its sources with periodontal disease in the US adult population (≥30 y of age). Data from 6052 adults participating in NHANES 2009-2012 were used. Periodontal disease was defined (according to the CDC/American Academy of Periodontology) as severe, moderate, mild, and none. Intake was assessed by 24-h dietary recalls. The relation between periodontal disease and dietary fiber, whole-grain, and fruit and vegetable intakes were evaluated by using multivariate models, adjusting for sociodemographic characteristics and dentition status. In the multivariate logistic model, the lowest quartile of dietary fiber was associated with moderate-severe periodontitis (compared with mild-none) compared with the highest dietary fiber intake quartile (OR: 1.30; 95% CI: 1.00, 1.69). In the multivariate multinomial logistic model, intake in the lowest quartile of dietary fiber was associated with higher severity of periodontitis than dietary fiber intake in the highest quartile (OR: 1.27; 95% CI: 1.00, 1.62). In the adjusted logistic model, whole-grain intake was not associated with moderate-severe periodontitis. However, in the adjusted multinomial logistic model, adults consuming whole grains in the lowest quartile were more likely to have more severe periodontal disease than were adults consuming whole grains in the highest quartile (OR: 1.32; 95% CI: 1.08, 1.62). In fully adjusted logistic and multinomial logistic models, fruit and vegetable intake was not significantly associated with periodontitis. We found an inverse relation between dietary fiber intake and periodontal disease among US adults ≥30 y old. Periodontal disease was associated with low whole

Discrete Choice and Rational Inattention

DEFF Research Database (Denmark)

Fosgerau, Mogens; Melo, Emerson; de Palma, André

2017-01-01

This paper establishes a general equivalence between discrete choice and rational inattention models. Matejka and McKay (2015, AER) showed that when information costs are modelled using the Shannon entropy, the result- ing choice probabilities in the rational inattention model take the multinomial...... logit form. We show that when information costs are modelled using a class of generalized entropies, then the choice probabilities in any rational inattention model are observationally equivalent to some additive random utility discrete choice model and vice versa. This equivalence arises from convex...

The memory state heuristic: A formal model based on repeated recognition judgments.

Science.gov (United States)

Castela, Marta; Erdfelder, Edgar

2017-02-01

The recognition heuristic (RH) theory predicts that, in comparative judgment tasks, if one object is recognized and the other is not, the recognized one is chosen. The memory-state heuristic (MSH) extends the RH by assuming that choices are not affected by recognition judgments per se, but by the memory states underlying these judgments (i.e., recognition certainty, uncertainty, or rejection certainty). Specifically, the larger the discrepancy between memory states, the larger the probability of choosing the object in the higher state. The typical RH paradigm does not allow estimation of the underlying memory states because it is unknown whether the objects were previously experienced or not. Therefore, we extended the paradigm by repeating the recognition task twice. In line with high threshold models of recognition, we assumed that inconsistent recognition judgments result from uncertainty whereas consistent judgments most likely result from memory certainty. In Experiment 1, we fitted 2 nested multinomial models to the data: an MSH model that formalizes the relation between memory states and binary choices explicitly and an approximate model that ignores the (unlikely) possibility of consistent guesses. Both models provided converging results. As predicted, reliance on recognition increased with the discrepancy in the underlying memory states. In Experiment 2, we replicated these results and found support for choice consistency predictions of the MSH. Additionally, recognition and choice latencies were in agreement with the MSH in both experiments. Finally, we validated critical parameters of our MSH model through a cross-validation method and a third experiment. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Menarcheal age of girls from dysfunctional families

Directory of Open Access Journals (Sweden)

Alma Toromanović

2004-08-01

Full Text Available The objective of the present study was to determine median age at menarche and the influence of familial instability on maturation. The sample included 7047 girls between the ages of 9 and 17 years from Tuzla Canton. The girls were divided into two groups. Group A (N=5230 comprised girls who lived in families free of strong traumatic events. Group B (N=1817 included girls whose family dysfunction exposed them to prolonged distress. Probit analysis was performed to estimate mean menarcheal age using the Probit procedure of SAS package. The mean menarcheal age calculated by probit analysis for all the girls studied was 13.07 years. In girls from dysfunctional families a very clear shift toward earlier maturation was observed. The mean age at menarche for group B was 13.0 years, which was significantly lower that that for group A, 13.11 years (t=2.92, P<0.01. The results surveyed here lead to the conclusion that girls from dysfunctional families mature not later but even earlier than girls from normal families. This supports the hypothesis that stressful childhood life events accelerate maturation of girls.

Empirical research on drive mechanism of firms' environmental management

Institute of Scientific and Technical Information of China (English)

Cao Jingshan; Qin Ying

2007-01-01

Firms'transformation from passive envrionmental management to active environmental management is the key to solving environmental problems. This paper empirically studies the impact of environmental management incentives on environmental management through model construction. Based on the data and reality of China, we can build a concept model of environmental management driving mechanism, and put forward theoretical hypothesis that can be tested: take the 13 environmental management behaviors (EMBs) as substitute of the comprehensiveness, introduce counting variables, and use NB model, Possion Model and Ordered Probit model the regression analysis. The theory and methods brought forward in this paper will provide references for firms in China to further implement voluntary environmental management, and offer advises and countertneasures for leaders to implement environmental management effectively.

The Effects of Secondary Special Education Preparation in Reading: Research to Inform State Policy in a New Era

Science.gov (United States)

Knackstedt, Kimberly M.; Leko, Melinda M.; Siuty, Molly Baustien

2018-01-01

In this study, the authors present findings from a survey of 577 secondary special educators in a large Midwestern state regarding their reading pre-service and in-service teacher preparation and its effect on teachers' sense of preparedness for teaching reading to adolescents with disabilities. Six models were fitted using multinomial logistic…

Investigating the adaptive model of thermal comfort for naturally ventilated school buildings in Taiwan

Science.gov (United States)

Hwang, Ruey-Lung; Lin, Tzu-Ping; Chen, Chen-Peng; Kuo, Nai-Jung

2009-03-01

Divergence in the acceptability to people in different regions of naturally ventilated thermal environments raises a concern over the extent to which the ASHRAE Standard 55 may be applied as a universal criterion of thermal comfort. In this study, the ASHRAE 55 adaptive model of thermal comfort was investigated for its applicability to a hot and humid climate through a long-term field survey performed in central Taiwan among local students attending 14 elementary and high schools during September to January. Adaptive behaviors, thermal neutrality, and thermal comfort zones are explored. A probit analysis of thermal acceptability responses from students was performed in place of the conventional linear regression of thermal sensation votes against operative temperature to investigate the limits of comfort zones for 90% and 80% acceptability; the corresponding comfort zones were found to occur at 20.1-28.4°C and 17.6-30.0°C, respectively. In comparison with the yearly comfort zones recommended by the adaptive model for naturally ventilated spaces in the ASHRAE Standard 55, those observed in this study differ in the lower limit for 80% acceptability, with the observed level being 1.7°C lower than the ASHRAE-recommended value. These findings can be generalized to the population of school children, thus providing information that can supplement ASHRAE Standard 55 in evaluating the thermal performance of naturally ventilated school buildings, particularly in hot-humid areas such as Taiwan.

Dose/volume–response relations for rectal morbidity using planned and simulated motion-inclusive dose distributions

International Nuclear Information System (INIS)

Thor, Maria; Apte, Aditya; Deasy, Joseph O.; Karlsdóttir, Àsa; Moiseenko, Vitali; Liu, Mitchell; Muren, Ludvig Paul

2013-01-01

Background and purpose: Many dose-limiting normal tissues in radiotherapy (RT) display considerable internal motion between fractions over a course of treatment, potentially reducing the appropriateness of using planned dose distributions to predict morbidity. Accounting explicitly for rectal motion could improve the predictive power of modelling rectal morbidity. To test this, we simulated the effect of motion in two cohorts. Materials and methods: The included patients (232 and 159 cases) received RT for prostate cancer to 70 and 74 Gy. Motion-inclusive dose distributions were introduced as simulations of random or systematic motion to the planned dose distributions. Six rectal morbidity endpoints were analysed. A probit model using the QUANTEC recommended parameters was also applied to the cohorts. Results: The differences in associations using the planned over the motion-inclusive dose distributions were modest. Statistically significant associations were obtained with four of the endpoints, mainly at high doses (55–70 Gy), using both the planned and the motion-inclusive dose distributions, primarily when simulating random motion. The strongest associations were observed for GI toxicity and rectal bleeding (Rs = 0.12–0.21; Rs = 0.11–0.20). Applying the probit model, significant associations were found for tenesmus and rectal bleeding (Rs = 0.13, p = 0.02). Conclusion: Equally strong associations with rectal morbidity were observed at high doses (>55 Gy), for the planned and the simulated dose distributions including in particular random rectal motion. Future studies should explore patient-specific descriptions of rectal motion to achieve improved predictive power

The impact of diabetes on employment and work productivity.

Science.gov (United States)

Tunceli, Kaan; Bradley, Cathy J; Nerenz, David; Williams, L Keoki; Pladevall, Manel; Elston Lafata, Jennifer

2005-11-01

The purpose of this study was to longitudinally examine the effect of diabetes on labor market outcomes. Using secondary data from the first two waves (1992 and 1994) of the Health and Retirement Study, we identified 7,055 employed respondents (51-61 years of age), 490 of whom reported having diabetes in wave 1. We estimated the effect of diabetes in wave 1 on the probability of working in wave 2 using probit regression. For those working in wave 2, we modeled the relationships between diabetic status in wave 1 and the change in hours worked and work-loss days using ordinary least-squares regressions and modeled the presence of health-related work limitations using probit regression. All models control for health status and job characteristics and are estimated separately by sex. Among individuals with diabetes, the absolute probability of working was 4.4 percentage points less for women and 7.1 percentage points less for men relative to that of their counterparts without diabetes. Change in weekly hours worked was not statistically significantly associated with diabetes. Women with diabetes had 2 more work-loss days per year compared with women without diabetes. Compared with individuals without diabetes, men and women with diabetes were 5.4 and 6 percentage points (absolute increase), respectively, more likely to have work limitations. This article provides evidence that diabetes affects patients, employers, and society not only by reducing employment but also by contributing to work loss and health-related work limitations for those who remain employed.

"Female-Headed Families: Why Are They So Poor?"

OpenAIRE

Joan R. Rodgers

1991-01-01

Over the last few decades in the United States, the poverty rate for female-headed families (with no husband present) has been about three times the poverty rate for male-headed families (with no wife present) and about six times the poverty rate for married- couple families. This paper addresses the question of why, in general, female-headed families are so much poorer than other families. A decomposition of poverty rates and a set of probit models are used to identify the factors which dete...

Factores explicativos de la disposición a pagar por atributos culturales en nuez de Castilla

OpenAIRE

Luna Méndez, Nexeai; Jaramillo Villanueva, José Luis; Ramírez Juárez, Javier

2016-01-01

[EN] The aim of the study was to determine the explanatory factors of the willingness to pay (WTP) a price premium for consuming local Castilla walnut versus the imported one in the region of “Sierra Nevada”, state of Puebla. Data were gathered from 216 questionnaires administered to consumers in three cities: Puebla capital, San Pedro Cholula and Atlixco. To explain the WTP, an econometric probit model with multiple intervals was estimated. A WTP a premium of 10 % for the attribute “local” w...

Influence of Parking Price on Parking Garage Users’ Behaviour

Directory of Open Access Journals (Sweden)

Jelena Simićević

2012-09-01

Full Text Available Parking charge is a powerful tool for solving parking and traffic congestion problems. In order to achieve the expected effects without any adverse impact it is necessary to understand well the users’ responses to this policy. This paper, based on a sample of interviewed parking garage users, has developed binary logit model for identification and quantification of characteristics of users and trips, on which the acceptance of parking price is dependent. In addition, multinomial logit model has been made in order to predict what the users will opt for when faced with an increase in parking price. For the first time the parameter “shorten duration” has been introduced which has shown to be the most significant in making behaviour-related decisions. The results show that the users with the purpose work are the most sensitive to an increase in parking charge, what can be deemed positive for policy makers. However, great sensitivity of the users with the purpose shopping should cause their concern. The results of the multinomial model show that they would not discontinue coming into the area after all.

A novel statistical method for classifying habitat generalists and specialists

DEFF Research Database (Denmark)

Chazdon, Robin L; Chao, Anne; Colwell, Robert K

2011-01-01

in second-growth (SG) and old-growth (OG) rain forests in the Caribbean lowlands of northeastern Costa Rica. We evaluate the multinomial model in detail for the tree data set. Our results for birds were highly concordant with a previous nonstatistical classification, but our method classified a higher......: (1) generalist; (2) habitat A specialist; (3) habitat B specialist; and (4) too rare to classify with confidence. We illustrate our multinomial classification method using two contrasting data sets: (1) bird abundance in woodland and heath habitats in southeastern Australia and (2) tree abundance...... fraction (57.7%) of bird species with statistical confidence. Based on a conservative specialization threshold and adjustment for multiple comparisons, 64.4% of tree species in the full sample were too rare to classify with confidence. Among the species classified, OG specialists constituted the largest...

Bivariate Probit Models for Analysing how “Knowledge” Affects Innovation and Performance in Small and Medium Sized Firms

OpenAIRE

FARACE, Salvatore; MAZZOTTA, Fernanda

2011-01-01

This paper examines the determinants of innovation and its effects on small- and medium-sized firms We use the data from the OPIS databank, which provides a survey on a representative sample of firms from a province of the Southern Italy. We want to study whether small and medium sized firms can have a competitive advantage using their innovative capabilities, regardless of their sectoral and size limits. The main factor influencing the likelihood of innovation is knowledge, which is acquired...

A Multilevel Study of Students' Motivations of Studying Accounting: Implications for Employers

Science.gov (United States)

Law, Philip; Yuen, Desmond

2012-01-01

Purpose: The purpose of this study is to examine the influence of factors affecting students' choice of accounting as a study major in Hong Kong. Design/methodology/approach: Multinomial logistic regression and Hierarchical Generalized Linear Modeling (HGLM) are used to analyze the survey data for the level one and level two data, which is the…

The perception of the relationship between environment and health according to data from Italian Behavioural Risk Factor Surveillance System (PASSI).

Science.gov (United States)

Sampaolo, Letizia; Tommaso, Giulia; Gherardi, Bianca; Carrozzi, Giuliano; Freni Sterrantino, Anna; Ottone, Marta; Goldoni, Carlo Alberto; Bertozzi, Nicoletta; Scaringi, Meri; Bolognesi, Lara; Masocco, Maria; Salmaso, Stefania; Lauriola, Paolo

2017-01-01

"OBJECTIVES: to identify groups of people in relation to the perception of environmental risk and to assess the main characteristics using data collected in the environmental module of the surveillance network Italian Behavioral Risk Factor Surveillance System (PASSI). perceptive profiles were identified using a latent class analysis; later they were included as outcome in multinomial logistic regression models to assess the association between environmental risk perception and demographic, health, socio-economic and behavioural variables. the latent class analysis allowed to split the sample in "worried", "indifferent", and "positive" people. The multinomial logistic regression model showed that the "worried" profile typically includes people of Italian nationality, living in highly urbanized areas, with a high level of education, and with economic difficulties; they pay special attention to their own health and fitness, but they have a negative perception of their own psychophysical state. the application of advanced statistical analysis enable to appraise PASSI data in order to characterize the perception of environmental risk, making the planning of interventions related to risk communication possible. ".

Airport choice model in multiple airport regions

Directory of Open Access Journals (Sweden)

Claudia Muñoz

2017-02-01

Full Text Available Purpose: This study aims to analyze travel choices made by air transportation users in multi airport regions because it is a crucial component when planning passenger redistribution policies. The purpose of this study is to find a utility function which makes it possible to know the variables that influence users’ choice of the airports on routes to the main cities in the Colombian territory. Design/methodology/approach: This research generates a Multinomial Logit Model (MNL, which is based on the theory of maximizing utility, and it is based on the data obtained on revealed and stated preference surveys applied to users who reside in the metropolitan area of Aburrá Valley (Colombia. This zone is the only one in the Colombian territory which has two neighboring airports for domestic flights. The airports included in the modeling process were Enrique Olaya Herrera (EOH Airport and José María Córdova (JMC Airport. Several structure models were tested, and the MNL proved to be the most significant revealing the common variables that affect passenger airport choice include the airfare, the price to travel the airport, and the time to get to the airport. Findings and Originality/value: The airport choice model which was calibrated corresponds to a valid powerful tool used to calculate the probability of each analyzed airport of being chosen for domestic flights in the Colombian territory. This is done bearing in mind specific characteristic of each of the attributes contained in the utility function. In addition, these probabilities will be used to calculate future market shares of the two airports considered in this study, and this will be done generating a support tool for airport and airline marketing policies.

A Bayesian hierarchical model with novel prior specifications for estimating HIV testing rates.

Science.gov (United States)

An, Qian; Kang, Jian; Song, Ruiguang; Hall, H Irene

2016-04-30

Human immunodeficiency virus (HIV) infection is a severe infectious disease actively spreading globally, and acquired immunodeficiency syndrome (AIDS) is an advanced stage of HIV infection. The HIV testing rate, that is, the probability that an AIDS-free HIV infected person seeks a test for HIV during a particular time interval, given no previous positive test has been obtained prior to the start of the time, is an important parameter for public health. In this paper, we propose a Bayesian hierarchical model with two levels of hierarchy to estimate the HIV testing rate using annual AIDS and AIDS-free HIV diagnoses data. At level one, we model the latent number of HIV infections for each year using a Poisson distribution with the intensity parameter representing the HIV incidence rate. At level two, the annual numbers of AIDS and AIDS-free HIV diagnosed cases and all undiagnosed cases stratified by the HIV infections at different years are modeled using a multinomial distribution with parameters including the HIV testing rate. We propose a new class of priors for the HIV incidence rate and HIV testing rate taking into account the temporal dependence of these parameters to improve the estimation accuracy. We develop an efficient posterior computation algorithm based on the adaptive rejection metropolis sampling technique. We demonstrate our model using simulation studies and the analysis of the national HIV surveillance data in the USA. Copyright © 2015 John Wiley & Sons, Ltd.

A bivariate model for analyzing recurrent multi-type automobile failures

Science.gov (United States)

Sunethra, A. A.; Sooriyarachchi, M. R.

2017-09-01

The failure mechanism in an automobile can be defined as a system of multi-type recurrent failures where failures can occur due to various multi-type failure modes and these failures are repetitive such that more than one failure can occur from each failure mode. In analysing such automobile failures, both the time and type of the failure serve as response variables. However, these two response variables are highly correlated with each other since the timing of failures has an association with the mode of the failure. When there are more than one correlated response variables, the fitting of a multivariate model is more preferable than separate univariate models. Therefore, a bivariate model of time and type of failure becomes appealing for such automobile failure data. When there are multiple failure observations pertaining to a single automobile, such data cannot be treated as independent data because failure instances of a single automobile are correlated with each other while failures among different automobiles can be treated as independent. Therefore, this study proposes a bivariate model consisting time and type of failure as responses adjusted for correlated data. The proposed model was formulated following the approaches of shared parameter models and random effects models for joining the responses and for representing the correlated data respectively. The proposed model is applied to a sample of automobile failures with three types of failure modes and up to five failure recurrences. The parametric distributions that were suitable for the two responses of time to failure and type of failure were Weibull distribution and multinomial distribution respectively. The proposed bivariate model was programmed in SAS Procedure Proc NLMIXED by user programming appropriate likelihood functions. The performance of the bivariate model was compared with separate univariate models fitted for the two responses and it was identified that better performance is secured by

Job tenure and self-reported workplace discrimination for cancer survivors 2 years after diagnosis: does employment legislation matter?

Science.gov (United States)

Paraponaris, Alain; Teyssier, Luis Sagaon; Ventelou, Bruno

2010-12-01

To assess the risk of leaving employment for cancer survivors 2 years after diagnosis and the role of workplace discrimination in this risk. A representative sample of 4270 French individuals older than 17 and younger than 58 years when diagnosed with cancer in 2002 were interviewed 2 years later. Their occupational status was analyzed with the help of Probit and IV-Probit models. Overall, 66% of the cancer survivors who were working at the time of diagnosis were still employed 2 years later. Age, education level, income at diagnosis, work contract, professional status, affective support, relative prognosis at diagnosis, tumor site and treatment have contrasting impacts upon the probability of job loss across gender. Even after having controlled for these variables, self-reported workplace discrimination increases the probability of job loss by 15%. Despite protective labor law and favorable health insurance arrangements, French cancer survivors continue to experience problems to stay in or to return to the labor force. Measures targeting only the employment protection of cancer survivors do not seem to be sufficient to end prior social inequalities in job attainment. Intervention for specific populations particularly exposed to job-loss risks would also be needed. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Consumer acceptance of irradiated food products: an apple marketing study

International Nuclear Information System (INIS)

Terry, D.E.; Tabor, R.L.

1990-01-01

This study was exploratory in nature, with emphasis on initial purchases and not repeat purchases or long-term loyalties to either irradiated or non-irradiated produce. The investigation involved the actual sale of irradiated and non-irradiated apples to consumers. Limited information about the process was provided, and apples were sold at roadside stands. Prices for the irradiated apples were varied while the price for the non-irradiated apples was held constant. Of these 228 West-Central Missouri shoppers, 101 (44%) bought no irradiated apples, 86 (38%) bought only irradiated apples, and 41 (18%) bought some of both types, Results of probit regressions indicated three significant independent variables. There was an inverse relationship between the price of irradiated apples and the probability of purchasing irradiated apples. There was a positive relationship between the purchasers’ educational level and the probability of purchasing irradiated apples. Predicted probabilities for belonging to categories in probit models were computed. Depending on particular equation specification, correctly placed were approximately 70 percent of the purchasers of the two categories--bought only non-irradiated apples, or bought some of both irradiated and non-irradiated apples or only irradiated apples. This study suggests that consumers may be interested in food irradiation as a possible alternative or supplement to current preservation techniques

The Effect of Health Insurance on Institutional Delivery in Indonesia

Directory of Open Access Journals (Sweden)

Mazda Novi Mukhlisa

2018-02-01

Pemanfaatan pelayanan persalinan di fasilitas kesehatan berdampak pada menurunnya angka kematian ibu (AKI. Di Indonesia, persalinan di fasilitas kesehatan mengalami peningkatan setiap tahunnya, tetapi masih terdapat sekitar 30% ibu yang bersalin di rumah. Sayangnya, peningkatan pemanfaatan pelayanan persalinan di fasilitas kesehatan tersebut tidak diimbangi dengan penurunan AKI, sehingga Indonesia tidak berhasil mencapai target MDGs. Untuk mencapai Universal Health Coverage, Indonesia mengimplementasikan program Jaminan Kesehatan Nasional (JKN yang mengintegrasikan empat jaminan kesehatan, yaitu Askes/ASABRI, Jamsostek, Jamkesmas, dan Jamkesda. Jaminan kesehatan dapat mengatasi kendala biaya pada persalinan di fasilitas kesehatan. Dengan menggunakan data Riset Kesehatan Dasar 2013 dan data Potensi Desa 2011 sebagai sumber data, penelitian ini bertujuan menganalisis bahwa kepemilikan jaminan kesehatan meningkatkan pemanfaatan pelayanan persalinan di fasilitas kesehatan di Indonesia. Sampel penelitian berjumlah 39.942 perempuan berusia 15-49 tahun yang melahirkan anak terakhir dalam periode waktu 2010-2013. Penelitian ini menggunakan pendekatan ekonometri dengan model estimasi probit dan bivariat probit untuk mengestimasi efek jaminan kesehatan dengan mempertimbangkan isu endogenitas pada jaminan kesehatan. Hasil penelitian menunjukkan bahwa kepemilikan jaminan kesehatan meningkatkan persalinan di fasilitas kesehatan sebesar 39,52%. Sebagai kesimpulan, ibu yang memiliki jaminan kesehatan akan lebih memanfaatkan fasilitas kesehatan saat persalinan dibandingkan dengan ibu yang tidak memiliki jaminan kesehatan.

Estimation of causal mediation effects for a dichotomous outcome in multiple-mediator models using the mediation formula.

Science.gov (United States)

Wang, Wei; Nelson, Suchitra; Albert, Jeffrey M

2013-10-30

Mediators are intermediate variables in the causal pathway between an exposure and an outcome. Mediation analysis investigates the extent to which exposure effects occur through these variables, thus revealing causal mechanisms. In this paper, we consider the estimation of the mediation effect when the outcome is binary and multiple mediators of different types exist. We give a precise definition of the total mediation effect as well as decomposed mediation effects through individual or sets of mediators using the potential outcomes framework. We formulate a model of joint distribution (probit-normal) using continuous latent variables for any binary mediators to account for correlations among multiple mediators. A mediation formula approach is proposed to estimate the total mediation effect and decomposed mediation effects based on this parametric model. Estimation of mediation effects through individual or subsets of mediators requires an assumption involving the joint distribution of multiple counterfactuals. We conduct a simulation study that demonstrates low bias of mediation effect estimators for two-mediator models with various combinations of mediator types. The results also show that the power to detect a nonzero total mediation effect increases as the correlation coefficient between two mediators increases, whereas power for individual mediation effects reaches a maximum when the mediators are uncorrelated. We illustrate our approach by applying it to a retrospective cohort study of dental caries in adolescents with low and high socioeconomic status. Sensitivity analysis is performed to assess the robustness of conclusions regarding mediation effects when the assumption of no unmeasured mediator-outcome confounders is violated. Copyright © 2013 John Wiley & Sons, Ltd.

Estimation of Causal Mediation Effects for a Dichotomous Outcome in Multiple-Mediator Models using the Mediation Formula

Science.gov (United States)

Nelson, Suchitra; Albert, Jeffrey M.

2013-01-01

Mediators are intermediate variables in the causal pathway between an exposure and an outcome. Mediation analysis investigates the extent to which exposure effects occur through these variables, thus revealing causal mechanisms. In this paper, we consider the estimation of the mediation effect when the outcome is binary and multiple mediators of different types exist. We give a precise definition of the total mediation effect as well as decomposed mediation effects through individual or sets of mediators using the potential outcomes framework. We formulate a model of joint distribution (probit-normal) using continuous latent variables for any binary mediators to account for correlations among multiple mediators. A mediation formula approach is proposed to estimate the total mediation effect and decomposed mediation effects based on this parametric model. Estimation of mediation effects through individual or subsets of mediators requires an assumption involving the joint distribution of multiple counterfactuals. We conduct a simulation study that demonstrates low bias of mediation effect estimators for two-mediator models with various combinations of mediator types. The results also show that the power to detect a non-zero total mediation effect increases as the correlation coefficient between two mediators increases, while power for individual mediation effects reaches a maximum when the mediators are uncorrelated. We illustrate our approach by applying it to a retrospective cohort study of dental caries in adolescents with low and high socioeconomic status. Sensitivity analysis is performed to assess the robustness of conclusions regarding mediation effects when the assumption of no unmeasured mediator-outcome confounders is violated. PMID:23650048

Disability and multi-state labour force choices with state dependence

OpenAIRE

Oguzoglu, Umut

2010-01-01

I use a dynamic mixed multinomial logit model with unobserved heterogeneity to study the impact of work limiting disabilities on disaggregated labour choices. The first seven waves of the Household Income and Labour Dynamics in Australia survey are used to investigate this relationship. Findings point out to strong state dependence in employment choices. Further, the impact of disability on employment outcomes is highly significant. Model simulations suggest that high cross and own state depe...

ADDING A NEW STEP WITH SPATIAL AUTOCORRELATION TO IMPROVE THE FOUR-STEP TRAVEL DEMAND MODEL WITH FEEDBACK FOR A DEVELOPING CITY

Directory of Open Access Journals (Sweden)

Xuesong FENG, Ph.D Candidate

2009-01-01

Full Text Available It is expected that improvement of transport networks could give rise to the change of spatial distributions of population-related factors and car ownership, which are expected to further influence travel demand. To properly reflect such an interdependence mechanism, an aggregate multinomial logit (A-MNL model was firstly applied to represent the spatial distributions of these exogenous variables of the travel demand model by reflecting the influence of transport networks. Next, the spatial autocorrelation analysis is introduced into the log-transformed A-MNL model (called SPA-MNL model. Thereafter, the SPA-MNL model is integrated into the four-step travel demand model with feedback (called 4-STEP model. As a result, an integrated travel demand model is newly developed and named as the SPA-STEP model. Using person trip data collected in Beijing, the performance of the SPA-STEP model is empirically compared with the 4-STEP model. It was proven that the SPA-STEP model is superior to the 4-STEP model in accuracy; most of the estimated parameters showed statistical differences in values. Moreover, though the results of the simulations to the same set of assumed scenarios by the 4-STEP model and the SPA-STEP model consistently suggested the same sustainable path for the future development of Beijing, it was found that the environmental sustainability and the traffic congestion for these scenarios were generally overestimated by the 4-STEP model compared with the corresponding analyses by the SPA-STEP model. Such differences were clearly generated by the introduction of the new modeling step with spatial autocorrelation.

Small individual loans and mental health: a randomized controlled trial among South African adults.

Science.gov (United States)

Fernald, Lia C H; Hamad, Rita; Karlan, Dean; Ozer, Emily J; Zinman, Jonathan

2008-12-16

In the developing world, access to small, individual loans has been variously hailed as a poverty-alleviation tool - in the context of "microcredit" - but has also been criticized as "usury" and harmful to vulnerable borrowers. Prior studies have assessed effects of access to credit on traditional economic outcomes for poor borrowers, but effects on mental health have been largely ignored. Applicants who had previously been rejected (n = 257) for a loan (200% annual percentage rate - APR) from a lender in South Africa were randomly assigned to a "second-look" that encouraged loan officers to approve their applications. This randomized encouragement resulted in 53% of applicants receiving a loan they otherwise would not have received. All subjects were assessed 6-12 months later with questions about demographics, socio-economic status, and two indicators of mental health: the Center for Epidemiologic Studies - Depression Scale (CES-D) and Cohen's Perceived Stress scale. Intent-to-treat analyses were calculated using multinomial probit regressions. Randomization into receiving a "second look" for access to credit increased perceived stress in the combined sample of women and men; the findings were stronger among men. Credit access was associated with reduced depressive symptoms in men, but not women. Our findings suggest that a mechanism used to reduce the economic stress of extremely poor individuals can have mixed effects on their experiences of psychological stress and depressive symptomatology. Our data support the notion that mental health should be included as a measure of success (or failure) when examining potential tools for poverty alleviation. Further longitudinal research is needed in South Africa and other settings to understand how borrowing at high interest rates affects gender roles and daily life activities. CCT: ISRCTN 10734925.

The Design and Analysis of Salmonid Tagging Studies in the Columbia Basin; Volume XII; A Multinomial Model for Estimating Ocean Survival from Salmonid Coded Wire-Tag Data.

Energy Technology Data Exchange (ETDEWEB)

Ryding, Kristen E.; Skalski, John R.

1999-06-01

The purpose of this report is to illustrate the development of a stochastic model using coded wire-tag (CWT) release and age-at-return data, in order to regress first year ocean survival probabilities against coastal ocean conditions and climate covariates.

Consequences, norms, and generalized inaction in moral dilemmas: The CNI model of moral decision-making.

Science.gov (United States)

Gawronski, Bertram; Armstrong, Joel; Conway, Paul; Friesdorf, Rebecca; Hütter, Mandy

2017-09-01

Research on moral dilemma judgments has been fundamentally shaped by the distinction between utilitarianism and deontology. According to the principle of utilitarianism, the moral status of behavioral options depends on their consequences; the principle of deontology states that the moral status of behavioral options depends on their consistency with moral norms. To identify the processes underlying utilitarian and deontological judgments, researchers have investigated responses to moral dilemmas that pit one principle against the other (e.g., trolley problem). However, the conceptual meaning of responses in this paradigm is ambiguous, because the central aspects of utilitarianism and deontology-consequences and norms-are not manipulated. We illustrate how this shortcoming undermines theoretical interpretations of empirical findings and describe an alternative approach that resolves the ambiguities of the traditional paradigm. Expanding on this approach, we present a multinomial model that allows researchers to quantify sensitivity to consequences (C), sensitivity to moral norms (N), and general preference for inaction versus action irrespective of consequences and norms (I) in responses to moral dilemmas. We present 8 studies that used this model to investigate the effects of gender, cognitive load, question framing, and psychopathy on moral dilemma judgments. The findings obtained with the proposed CNI model offer more nuanced insights into the determinants of moral dilemma judgments, calling for a reassessment of dominant theoretical assumptions. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Com obtenir un Model de Regressió Logística Binària amb SPSS

Directory of Open Access Journals (Sweden)

Vanesa Berlanga-Silvente

2014-04-01

Full Text Available Els models de regressió logística són models estadístics en què es desitja conèixer la relació entre: una variable dependent qualitativa dicotòmica (regressió logística binària o binomial i una o més variables explicatives independents, o covariables, ja siguin qualitatives o quantitatives. També és possible una variable dependent qualitativa amb més de dos valors (regressió logística multinomial, encara que en aquesta fitxa ens centrarem en la regressió logística binària. En qualsevol cas, l'equació inicial del model és de tipus exponencial, si bé la seva transformació logarítmica (logit permet el seu ús com una funció lineal. L'objectiu primordial que resol aquesta tècnica és el de modelar com influeixen la probabilitat d'aparició d'un succés, habitualment dicotòmic, la presència o no de diversos factors, i el valor o nivell dels mateixos. Aquesta fitxa sobre la Regressió Logística Binària explica les opcions que té el programa estadístic SPSS (mètodes automàtics "per passos" i la interpretació dels principals resultats.

Effects of preference heterogeneity among landowners on spatial conservation prioritization

DEFF Research Database (Denmark)

Nielsen, Anne Sofie Elberg; Strange, Niels; Bruun, Hans Henrik

2017-01-01

The participation of private landowners in conservation is crucial to efficient biodiversity conservation. This is especially the case in settings where the share of private ownership is large and the economic costs associated with land acquisition are high. We used probit regression analysis...... into a spatial prioritization for conservation of unmanaged forests. The choice models are based on sociodemographic data on the entire population of Danish forest owners and historical data on their participation in conservation schemes. Inclusion in the model of information on private landowners' willingness...... to supply land for conservation yielded at intermediate budget levels up to 30% more expected species coverage than the uninformed prioritization scheme. Our landowner-choice model provides an example of moving toward more implementable conservation planning....

Adding a Performance-Based Component to Surface Warfare Officer Bonuses: Will it Affect Retention?

National Research Council Canada - National Science Library

Carman, Aron S; Mudd, Ryan M

2008-01-01

... to Lieutenant Commander (0-4). Probit regressions showed that top performers exhibited higher retention rates than lower-performing peers, though pay had a stronger retention effect among low performers...

Measuring Rice Farmer’s Pesticide Overuse Practice and the Determinants: A Statistical Analysis Based on Data Collected in Jiangsu and Anhui Provinces of China

Directory of Open Access Journals (Sweden)

Jianhua Wang

2018-03-01

Full Text Available Understanding the extent of pesticide overuse and what drives rice farmers to overuse pesticide in agricultural production theoretically and empirically is imperative to increase farmers’ income, promote agricultural transformation and agricultural sustainable development. In this paper, we examined the phenomenon and pattern of pesticides overuse based on the data collected from 861 rice farmers in Jiangsu and Anhui, two provinces in China. By applying the Cobb-Douglas production function (C-D production function and the damage control model, we estimated the marginal productivity of pesticides. We also adopted the Binary Probit model to further explore factors leading to overuse of pesticide among farmers. Our findings suggested that the marginal productivity of pesticides is close to zero, indicating that there is an excessive use of pesticides in the surveyed areas. According to the Binary Probit model, we also discovered that female farmers, farmers with knowledge about pesticide toxicity, pesticide residue and farmers who hold the view that massive use of pesticide is inimical to the environment, and farmers who participate in pesticide training organized by the government, are more likely to overuse pesticide. On the contrary, experienced farmers have a lower chance of overusing pesticides. Possible explanations to the above findings may be that applying pesticides in accordance with the instructions causes overusing and farmers who are loss-averse, in order to avoid the risk of income loss that may be caused by disease and insect pests, and keep its own income stable, will still increase the amount of pesticide application. It also indicates that farmers are insensitive to increased pesticide overuse.

Brief Report: Association of Myositis Autoantibodies, Clinical Features, and Environmental Exposures at Illness Onset With Disease Course in Juvenile Myositis.

Science.gov (United States)

Habers, G Esther A; Huber, Adam M; Mamyrova, Gulnara; Targoff, Ira N; O'Hanlon, Terrance P; Adams, Sharon; Pandey, Janardan P; Boonacker, Chantal; van Brussel, Marco; Miller, Frederick W; van Royen-Kerkhof, Annet; Rider, Lisa G

2016-03-01

To identify early factors associated with disease course in patients with juvenile idiopathic inflammatory myopathies (IIMs). Univariable and multivariable multinomial logistic regression analyses were performed in a large juvenile IIM registry (n = 365) and included demographic characteristics, early clinical features, serum muscle enzyme levels, myositis autoantibodies, environmental exposures, and immunogenetic polymorphisms. Multivariable associations with chronic or polycyclic courses compared to a monocyclic course included myositis-specific autoantibodies (multinomial odds ratio [OR] 4.2 and 2.8, respectively), myositis-associated autoantibodies (multinomial OR 4.8 and 3.5), and a documented infection within 6 months of illness onset (multinomial OR 2.5 and 4.7). A higher overall clinical symptom score at diagnosis was associated with chronic or monocyclic courses compared to a polycyclic course. Furthermore, severe illness onset was associated with a chronic course compared to monocyclic or polycyclic courses (multinomial OR 2.1 and 2.6, respectively), while anti-p155/140 autoantibodies were associated with chronic or polycyclic courses compared to a monocyclic course (multinomial OR 3.9 and 2.3, respectively). Additional univariable associations of a chronic course compared to a monocyclic course included photosensitivity, V-sign or shawl sign rashes, and cuticular overgrowth (OR 2.2-3.2). The mean ultraviolet index and highest ultraviolet index in the month before diagnosis were associated with a chronic course compared to a polycyclic course in boys (OR 1.5 and 1.3), while residing in the Northwest was less frequently associated with a chronic course (OR 0.2). Our findings indicate that myositis autoantibodies, in particular anti-p155/140, and a number of early clinical features and environmental exposures are associated with a chronic course in patients with juvenile IIM. These findings suggest that early factors, which are associated with poorer

Work-Family Conflict and Retirement Preferences

OpenAIRE

Raymo, James M.; Sweeney, Megan M

2005-01-01

Objectives: This study investigates relationships between perceived levels of work-family conflict and retirement preferences. Methods: Using the large sample of 52-54 year-old respondents to the 1992 Wisconsin Longitudinal Study, we estimate multinomial logistic regression models of preferences for partial and full retirement within the next ten years. We examine the association between preferences for retirement and perceived work-family conflict...

Linking apple farmers to markets: Determinants and impacts of marketing contracts in China

OpenAIRE

Ma, Wanglin; Abdulai, Awudu

2015-01-01

This study investigates the determinants of marketing contract choices and the related impact on farm net returns of apple farmers in China. We employ a two-stage selection correction approach (BFG) for the multinomial logit model. On the basis of the BFG estimation, we also use an endogenous switching regression model and a propensity score matching technique to estimate the causal effects of marketing contract choices on net returns. The empirical results reveal that written contracts incre...

Estimating the Open Market Desk's Daily Reaction Function.

OpenAIRE

Feinman, Joshua N

1993-01-01

This paper presents the results of an empirical investigation into the proximate determinants of the Federal Reserve's daily open market operations. Using information available each morning at the Fed conference call, the author models the Open Market Desk's choice of both the quantity and the type of operation, using a friction model for the former and a multinomial logit framework for the latter. Different types of operations are shown to send different signals to the market about the under...

How does aging affect recognition-based inference? A hierarchical Bayesian modeling approach.

Science.gov (United States)

Horn, Sebastian S; Pachur, Thorsten; Mata, Rui

2015-01-01

The recognition heuristic (RH) is a simple strategy for probabilistic inference according to which recognized objects are judged to score higher on a criterion than unrecognized objects. In this article, a hierarchical Bayesian extension of the multinomial r-model is applied to measure use of the RH on the individual participant level and to re-evaluate differences between younger and older adults' strategy reliance across environments. Further, it is explored how individual r-model parameters relate to alternative measures of the use of recognition and other knowledge, such as adherence rates and indices from signal-detection theory (SDT). Both younger and older adults used the RH substantially more often in an environment with high than low recognition validity, reflecting adaptivity in strategy use across environments. In extension of previous analyses (based on adherence rates), hierarchical modeling revealed that in an environment with low recognition validity, (a) older adults had a stronger tendency than younger adults to rely on the RH and (b) variability in RH use between individuals was larger than in an environment with high recognition validity; variability did not differ between age groups. Further, the r-model parameters correlated moderately with an SDT measure expressing how well people can discriminate cases where the RH leads to a correct vs. incorrect inference; this suggests that the r-model and the SDT measures may offer complementary insights into the use of recognition in decision making. In conclusion, younger and older adults are largely adaptive in their application of the RH, but cognitive aging may be associated with an increased tendency to rely on this strategy. Copyright © 2014 Elsevier B.V. All rights reserved.