Using Heteroskedastic Ordered Probit Models to Recover Moments of Continuous Test Score Distributions from Coarsened Data

ERIC Educational Resources Information Center

Reardon, Sean F.; Shear, Benjamin R.; Castellano, Katherine E.; Ho, Andrew D.

2017-01-01

Test score distributions of schools or demographic groups are often summarized by frequencies of students scoring in a small number of ordered proficiency categories. We show that heteroskedastic ordered probit (HETOP) models can be used to estimate means and standard deviations of multiple groups' test score distributions from such data. Because…

A Bayesian Multinomial Probit MODEL FOR THE ANALYSIS OF PANEL CHOICE DATA.

PubMed

Fong, Duncan K H; Kim, Sunghoon; Chen, Zhe; DeSarbo, Wayne S

2016-03-01

A new Bayesian multinomial probit model is proposed for the analysis of panel choice data. Using a parameter expansion technique, we are able to devise a Markov Chain Monte Carlo algorithm to compute our Bayesian estimates efficiently. We also show that the proposed procedure enables the estimation of individual level coefficients for the single-period multinomial probit model even when the available prior information is vague. We apply our new procedure to consumer purchase data and reanalyze a well-known scanner panel dataset that reveals new substantive insights. In addition, we delineate a number of advantageous features of our proposed procedure over several benchmark models. Finally, through a simulation analysis employing a fractional factorial design, we demonstrate that the results from our proposed model are quite robust with respect to differing factors across various conditions.

Time-dependent dose-response relation for absence of vaginal elasticity after gynecological radiation therapy.

PubMed

Alevronta, Eleftheria; Åvall-Lundqvist, Elisabeth; Al-Abany, Massoud; Nyberg, Tommy; Lind, Helena; Waldenström, Ann-Charlotte; Olsson, Caroline; Dunberger, Gail; Bergmark, Karin; Steineck, Gunnar; Lind, Bengt K

2016-09-01

To investigate the dose-response relation between the dose to the vagina and the patient-reported symptom 'absence of vaginal elasticity' and how time to follow-up influences this relation. The study included 78 long-term gynecological cancer survivors treated between 1991 and 2003 with external beam radiation therapy. Of those, 24 experienced absence of vaginal elasticity. A normal tissue complication model is introduced that takes into account the influence of time to follow-up on the dose-response relation and the patient's age. The best estimates of the dose-response parameters were calculated using Probit, Probit-Relative Seriality (RS) and Probit-time models. Log likelihood (LL) values and the Akaike Information Criterion (AIC) were used to evaluate the model fit. The dose-response parameters for 'absence of vaginal elasticity' according to the Probit and Probit-time models with the 68% Confidence Intervals (CI) were: LL=-39.8, D 50 =49.7 (47.2-52.4) Gy, γ 50 =1.40 (1.12-1.70) and LL=-37.4, D 50 =46.9 (43.5-50.9) Gy, γ 50 =1.81 (1.17-2.51) respectively. The proposed model, which describes the influence of time to follow-up on the dose-response relation, fits our data best. Our data indicate that the steepness of the dose-response curve of the dose to the vagina and the symptom 'absence of vaginal elasticity' increases with time to follow-up, while D 50 decreases. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression.

PubMed

Montesinos-López, Osval A; Montesinos-López, Abelardo; Crossa, José; Burgueño, Juan; Eskridge, Kent

2015-08-18

Most genomic-enabled prediction models developed so far assume that the response variable is continuous and normally distributed. The exception is the probit model, developed for ordered categorical phenotypes. In statistical applications, because of the easy implementation of the Bayesian probit ordinal regression (BPOR) model, Bayesian logistic ordinal regression (BLOR) is implemented rarely in the context of genomic-enabled prediction [sample size (n) is much smaller than the number of parameters (p)]. For this reason, in this paper we propose a BLOR model using the Pólya-Gamma data augmentation approach that produces a Gibbs sampler with similar full conditional distributions of the BPOR model and with the advantage that the BPOR model is a particular case of the BLOR model. We evaluated the proposed model by using simulation and two real data sets. Results indicate that our BLOR model is a good alternative for analyzing ordinal data in the context of genomic-enabled prediction with the probit or logit link. Copyright © 2015 Montesinos-López et al.

Achieving universal health coverage through voluntary insurance: what can we learn from the experience of Lao PDR?

PubMed Central

2013-01-01

Background The Government of Lao Peoples’ Democratic Republic (Lao PDR) has embarked on a path to achieve universal health coverage (UHC) through implementation of four risk-protection schemes. One of these schemes is community-based health insurance (CBHI) – a voluntary scheme that targets roughly half the population. However, after 12 years of implementation, coverage through CBHI remains very low. Increasing coverage of the scheme would require expansion to households in both villages where CBHI is currently operating, and new geographic areas. In this study we explore the prospects of both types of expansion by examining household and district level data. Methods Using a household survey based on a case-comparison design of 3000 households, we examine the determinants of enrolment at the household level in areas where the scheme is currently operating. We model the determinants of enrolment using a probit model and predicted probabilities. Findings from focus group discussions are used to explain the quantitative findings. To examine the prospects for geographic scale-up, we use secondary data to compare characteristics of districts with and without insurance, using a combination of univariate and multivariate analyses. The multivariate analysis is a probit model, which models the factors associated with roll-out of CBHI to the districts. Results The household findings show that enrolment is concentrated among the better off and that adverse selection is present in the scheme. The district level findings show that to date, the scheme has been implemented in the most affluent areas, in closest proximity to the district hospitals, and in areas where quality of care is relatively good. Conclusions The household-level findings indicate that the scheme suffers from poor risk-pooling, which threatens financial sustainability. The district-level findings call into question whether or not the Government of Laos can successfully expand to more remote, less affluent districts, with lower population density. We discuss the policy implications of the findings and specifically address whether CBHI can serve as a foundation for a national scheme, while exploring alternative approaches to reaching the informal sector in Laos and other countries attempting to achieve UHC. PMID:24344925

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

PubMed Central

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

2015-01-01

Background: 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. Methods: 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. Results: 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. Conclusion: 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. PMID:26793655

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

PubMed

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.

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

NASA Astrophysics Data System (ADS)

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.

Assessment of Poisson, probit and linear models for genetic analysis of presence and number of black spots in Corriedale sheep.

PubMed

Peñagaricano, F; Urioste, J I; Naya, H; de los Campos, G; Gianola, D

2011-04-01

Black skin spots are associated with pigmented fibres in wool, an important quality fault. Our objective was to assess alternative models for genetic analysis of presence (BINBS) and number (NUMBS) of black spots in Corriedale sheep. During 2002-08, 5624 records from 2839 animals in two flocks, aged 1 through 6 years, were taken at shearing. Four models were considered: linear and probit for BINBS and linear and Poisson for NUMBS. All models included flock-year and age as fixed effects and animal and permanent environmental as random effects. Models were fitted to the whole data set and were also compared based on their predictive ability in cross-validation. Estimates of heritability ranged from 0.154 to 0.230 for BINBS and 0.269 to 0.474 for NUMBS. For BINBS, the probit model fitted slightly better to the data than the linear model. Predictions of random effects from these models were highly correlated, and both models exhibited similar predictive ability. For NUMBS, the Poisson model, with a residual term to account for overdispersion, performed better than the linear model in goodness of fit and predictive ability. Predictions of random effects from the Poisson model were more strongly correlated with those from BINBS models than those from the linear model. Overall, the use of probit or linear models for BINBS and of a Poisson model with a residual for NUMBS seems a reasonable choice for genetic selection purposes in Corriedale sheep. © 2010 Blackwell Verlag GmbH.

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

NASA Astrophysics Data System (ADS)

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.

Nonparametric Bayesian models through probit stick-breaking processes

PubMed Central

Rodríguez, Abel; Dunson, David B.

2013-01-01

We describe a novel class of Bayesian nonparametric priors based on stick-breaking constructions where the weights of the process are constructed as probit transformations of normal random variables. We show that these priors are extremely flexible, allowing us to generate a great variety of models while preserving computational simplicity. Particular emphasis is placed on the construction of rich temporal and spatial processes, which are applied to two problems in finance and ecology. PMID:24358072

Nonparametric Bayesian models through probit stick-breaking processes.

PubMed

Rodríguez, Abel; Dunson, David B

2011-03-01

We describe a novel class of Bayesian nonparametric priors based on stick-breaking constructions where the weights of the process are constructed as probit transformations of normal random variables. We show that these priors are extremely flexible, allowing us to generate a great variety of models while preserving computational simplicity. Particular emphasis is placed on the construction of rich temporal and spatial processes, which are applied to two problems in finance and ecology.

Bayes Factor Covariance Testing in Item Response Models.

PubMed

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.

Socioeconomic Status and Self-Reported Chronic Diseases Among Argentina's Adult Population: Results Based on Multivariate Probability Models

PubMed Central

Viego, Valentina; Temporelli, Karina

2017-01-01

Background Hypertension, diabetes and hypercholesterolemia are the most frequent and diagnosed chronic diseases in Argentina. They contribute largely to the burden of chronic disease and they are strongly influenced by a small number of risk factors. These risk factors are all modifiable at the population and individual level and offer major prospects for their prevention. We are interested in socioeconomic determinants of prevalence of those 3 specific diseases. Design and methods We estimate 3-equation probit model, combined with 3 separate probit estimations and a probit-based Heckman correction considering possible sample selection bias. Estimations were carried out using secondary self-reported data coming from the 2013 Risk Factor National Survey. Results We find a negative association between socioeconomic status and prevalence of hypertension, cholesterolemia and diabetes; main increases concentrate in the transition from low to high SES in hypertension and diabetes. In cholesterol, the major effect takes place when individual crosses from low to middle SES and then vanishes. Anyway, in Argentina SES exhibit and independent effect on chronic diseases apart from those based on habits and body weight. Conclusions Public strategies to prevent chronic diseases must be specially targeted at women, poorest households and the least educated individuals in order to achieve efficacy. Also, as the probability of having a condition related to excessive blood pressure, high levels of cholesterol or glucose in the blood do not increase proportionally with age, so public campaigns promoting healthy diets, physical activity and medical checkups should be focused on young individuals to facilitate prophylaxis. Significance for public health Latin American countries are going through an epidemiological transition where infectious illnesses are being superseded by chronic diseases which, in turn, are related to lifestyles and socioeconomic factors. Specificities in the relationship between chronic diseases and socioeconomic status have been recorded in high income countries, but has not been sufficiently studied in low and middle income countries. We believe that analysis grounded on large scale datasets, recently available in Argentina, and based on proper statistical tools can provide useful guidance for decision making in public health policies as they highlight where population needs and risks do concentrate. PMID:28785549

Cohort profile: The promotion of breastfeeding intervention trial (PROBIT).

PubMed

Patel, Rita; Oken, Emily; Bogdanovich, Natalia; Matush, Lidia; Sevkovskaya, Zinaida; Chalmers, Beverley; Hodnett, Ellen D; Vilchuck, Konstantin; Kramer, Michael S; Martin, Richard M

2014-06-01

The PROmotion of Breastfeeding Intervention Trial (PROBIT) is a multicentre, cluster-randomized controlled trial conducted in the Republic of Belarus, in which the experimental intervention was the promotion of increased breastfeeding duration and exclusivity, modelled on the Baby-friendly hospital initiative. Between June 1996 and December 1997, 17,046 mother-infant pairs were recruited during their postpartum hospital stay from 31 maternity hospitals, of which 16 hospitals and their affiliated polyclinics had been randomly assigned to the arm of PROBIT investigating the promotion of breastfeeding and 15 had been assigned to the control arm, in which breastfeeding practices and policies in effect at the time of randomization was continued. Of the mother-infant pairs originally recruited for the study, 16,492 (96.7%) were followed at regular intervals until the infants were 12 months of age (PROBIT I) for the outcomes of breastfeeding duration and exclusivity; gastrointestinal and respiratory infections; and atopic eczema. Subsequently, 13,889 (81.5%) of the children from these mother-infant pairs were followed-up at age 6.5 years (PROBIT II) for anthropometry, blood pressure (BP), behaviour, dental health, cognitive function, asthma and atopy outcomes, and 13,879 (81.4%) children were followed to the age of 11.5 years (PROBIT III) for anthropometry, body composition, BP, and the measurement of fasted glucose, insulin, adiponectin, insulin-like growth factor-I, and apolipoproteins. The trial registration number for Current Controlled Trials is ISRCTN37687716 and that for ClinicalTrials.gov is NCT01561612. Proposals for collaboration are welcome, and enquires about PROBIT should be made to an executive group of the study steering committee (M.S.K., R.M.M., and E.O.). More information, including information about how to access the trial data, data collection documents, and bibliography, is available at the trial website (http://www.bristol.ac.uk/social-community-medicine/projects/probit/). Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2013; all rights reserved.

[What factors help to explain satisfaction with Primary Health care in Spain?].

PubMed

Arrazola-Vacas, M; de Hevia-Payá, J; Rodríguez-Esteban, L

2015-01-01

To find out the factors that determine satisfaction with public primary health care in Spain. The work has considered a wide group of potential determining factors of that satisfaction, which are organised into 3 blocks of variables: Those related to the perceived quality in the care received, socioeconomic, and those relative to the state of health. The micro data of the Barómetro Sanitario (BS) of 2013, which are representative at a national level, were employed. After a prior first descriptive analysis, 2 multivariate models were estimated: One in which satisfaction is considered as being of a cardinal nature (regression model), and another in which it is contemplated as being of an ordinal nature (ordered probit model). There were practically no differences between the results obtained with one or other of the multivariate models. Not all the variables considered were statistically significant. Of the 3 blocks of variables studied, the one related to the perceived quality in the care received in the health centre exerts the greatest relevance in the explanation of satisfaction. The results obtained show that, by means of the management of the variables related to the perception of quality of care in health centres, public administrators and health professionals may have a highly favourable influence on the levels of satisfaction of primary health care patients. Copyright © 2015 SECA. Published by Elsevier Espana. All rights reserved.

Investigation of factors affecting the injury severity of single-vehicle rollover crashes: A random-effects generalized ordered probit model.

PubMed

Anarkooli, Alireza Jafari; Hosseinpour, Mehdi; Kardar, Adele

2017-09-01

Rollover crashes are responsible for a notable number of serious injuries and fatalities; hence, they are of great concern to transportation officials and safety researchers. However, only few published studies have analyzed the factors associated with severity outcomes of rollover crashes. This research has two objectives. The first objective is to investigate the effects of various factors, of which some have been rarely reported in the existing studies, on the injury severities of single-vehicle (SV) rollover crashes based on six-year crash data collected on the Malaysian federal roads. A random-effects generalized ordered probit (REGOP) model is employed in this study to analyze injury severity patterns caused by rollover crashes. The second objective is to examine the performance of the proposed approach, REGOP, for modeling rollover injury severity outcomes. To this end, a mixed logit (MXL) model is also fitted in this study because of its popularity in injury severity modeling. Regarding the effects of the explanatory variables on the injury severity of rollover crashes, the results reveal that factors including dark without supplemental lighting, rainy weather condition, light truck vehicles (e.g., sport utility vehicles, vans), heavy vehicles (e.g., bus, truck), improper overtaking, vehicle age, traffic volume and composition, number of travel lanes, speed limit, undulating terrain, presence of central median, and unsafe roadside conditions are positively associated with more severe SV rollover crashes. On the other hand, unpaved shoulder width, area type, driver occupation, and number of access points are found as the significant variables decreasing the probability of being killed or severely injured (i.e., KSI) in rollover crashes. Land use and side friction are significant and positively associated only with slight injury category. These findings provide valuable insights into the causes and factors affecting the injury severity patterns of rollover crashes, and thus can help develop effective countermeasures to reduce the severity of rollover crashes. The model comparison results show that the REGOP model is found to outperform the MXL model in terms of goodness-of-fit measures, and also is significantly superior to other extensions of ordered probit models, including generalized ordered probit and random-effects ordered probit (REOP) models. As a result, this research introduces REGOP as a promising tool for future research focusing on crash injury severity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Understanding Activity Engagement Across Weekdays and Weekend Days: A Multivariate Multiple Discrete-Continuous Modeling Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garikapati, Venu; Astroza, Sebastian; Bhat, Prerna C.

This paper is motivated by the increasing recognition that modeling activity-travel demand for a single day of the week, as is done in virtually all travel forecasting models, may be inadequate in capturing underlying processes that govern activity-travel scheduling behavior. The considerable variability in daily travel suggests that there are important complementary relationships and competing tradeoffs involved in scheduling and allocating time to various activities across days of the week. Both limited survey data availability and methodological challenges in modeling week-long activity-travel schedules have precluded the development of multi-day activity-travel demand models. With passive and technology-based data collection methods increasinglymore » in vogue, the collection of multi-day travel data may become increasingly commonplace in the years ahead. This paper addresses the methodological challenge associated with modeling multi-day activity-travel demand by formulating a multivariate multiple discrete-continuous probit (MDCP) model system. The comprehensive framework ties together two MDCP model components, one corresponding to weekday time allocation and the other to weekend activity-time allocation. By tying the two MDCP components together, the model system also captures relationships in activity-time allocation between weekdays on the one hand and weekend days on the other. Model estimation on a week-long travel diary data set from the United Kingdom shows that there are significant inter-relationships between weekdays and weekend days in activity-travel scheduling behavior. The model system presented in this paper may serve as a higher-level multi-day activity scheduler in conjunction with existing daily activity-based travel models.« less

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

PubMed

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.

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

PubMed

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.

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

PubMed

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.

PubMed

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.

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

PubMed Central

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.

PubMed

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.

Sociodemographic, lifestyle and health determinants of suicidal behaviour in Malaysia.

PubMed

Cheah, Yong Kang; Azahadi, Mohd; Phang, Siew Nooi; Abd Manaf, Noor Hazilah

2018-03-01

Suicide has become a serious matter in both developed and developing countries. The objective of the present study is to examine the factors affecting suicidal behaviour among adults in Malaysia. A nationally representative data which consists of 10,141 respondents is used for analysis. A trivariate probit model is utilised to identify the probability of having suicide ideation, suicide plan and suicide attempt. Results of the regression analysis show that to ensure unbiased estimates, a trivariate probit model should be used instead of three separate probit models. The determining factors of suicidal behaviour are income, age, gender, ethnicity, education, marital status, self-rated health and being diagnosed with diabetes and hypercholesterolemia. The likelihood of adopting suicidal behaviour is lower among higher income earners and older individuals. Being male and married significantly reduce the propensity to engage in suicidal behaviour. Of all the ethnic groups, Indian/others displays the highest likelihood of adopting suicidal behaviour. There is a positive relationship between poor health condition and suicide. Policies targeted at individuals who are likely to adopt suicidal behaviour may be effective in lowering the prevalence of suicide. Copyright © 2018 Elsevier B.V. All rights reserved.

POLO: a user's guide to Probit Or LOgit analysis.

Treesearch

Jacqueline L. Robertson; Robert M. Russell; N.E. Savin

1980-01-01

This user's guide provides detailed instructions for the use of POLO (Probit Or LOgit), a computer program for the analysis of quantal response data such as that obtained from insecticide bioassays by the techniques of probit or logit analysis. Dosage-response lines may be compared for parallelism or...

Household income and preschool attendance in china.

PubMed

Gong, Xin; Xu, Di; Han, Wen-Jui

2015-01-01

This article draws upon the literature showing the benefits of high-quality preschools on child well-being to explore the role of household income on preschool attendance for a cohort of 3- to 6-year-olds in China using data from the China Health and Nutrition Survey, 1991-2006. Analyses are conducted separately for rural (N = 1,791) and urban (N = 633) settings. Estimates from a probit model with rich controls suggest a positive association between household income per capita and preschool attendance in both settings. A household fixed-effects model, conducted only on the rural sample, finds results similar to, although smaller than, those from the probit estimates. Policy recommendations are discussed. © 2014 The Authors. Child Development © 2014 Society for Research in Child Development, Inc.

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

PubMed

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.

`Been There Done That': Disentangling Option Value Effects from User Heterogeneity When Valuing Natural Resources with a Use Component

NASA Astrophysics Data System (ADS)

Lyssenko, Nikita; Martínez-Espiñeira, Roberto

2012-11-01

Endogeneity bias arises in contingent valuation studies when the error term in the willingness to pay (WTP) equation is correlated with explanatory variables because observable and unobservable characteristics of the respondents affect both their WTP and the value of those variables. We correct for the endogeneity of variables that capture previous experience with the resource valued, humpback whales, and with the geographic area of study. We consider several endogenous behavioral variables. Therefore, we apply a multivariate Probit approach to jointly model them with WTP. In this case, correcting for endogeneity increases econometric efficiency and substantially corrects the bias affecting the estimated coefficients of the experience variables, by isolating the decreasing effect on option value caused by having already experienced the resource. Stark differences are unveiled between the marginal effects on WTP of previous experience of the resource in an alternative location versus experience in the location studied, Newfoundland and Labrador (Canada).

HIV Testing Among Young People Aged 16-24 in South Africa: Impact of Mass Media Communication Programs.

PubMed

Do, Mai; Figueroa, Maria Elena; Lawrence Kincaid, D

2016-09-01

Knowing one's serostatus is critical in the HIV prevention, care and treatment continuum. This study examines the impact of communication programs on HIV testing in South Africa. Data came from 2204 young men and women aged 16-24 who reported to be sexually active in a population based survey. Structural equation modeling was used to test the directions and causal pathways between communication program exposure, HIV testing discussion, and having a test in the last 12 months. Bivariate and multivariate probit regressions provided evidence of exogeneity of communication exposure and the two HIV-related outcomes. One in three sampled individuals had been tested in the last 12 months. Communication program exposure only had an indirect effect on getting tested by encouraging young people to talk about testing. The study suggests that communication programs may create an environment that supports open HIV-related discussions and may have a long-term impact on behavior change.

'Been there done that': disentangling option value effects from user heterogeneity when valuing natural resources with a use component.

PubMed

Lyssenko, Nikita; Martínez-Espiñeira, Roberto

2012-11-01

Endogeneity bias arises in contingent valuation studies when the error term in the willingness to pay (WTP) equation is correlated with explanatory variables because observable and unobservable characteristics of the respondents affect both their WTP and the value of those variables. We correct for the endogeneity of variables that capture previous experience with the resource valued, humpback whales, and with the geographic area of study. We consider several endogenous behavioral variables. Therefore, we apply a multivariate Probit approach to jointly model them with WTP. In this case, correcting for endogeneity increases econometric efficiency and substantially corrects the bias affecting the estimated coefficients of the experience variables, by isolating the decreasing effect on option value caused by having already experienced the resource. Stark differences are unveiled between the marginal effects on WTP of previous experience of the resource in an alternative location versus experience in the location studied, Newfoundland and Labrador (Canada).

Risk estimates for CO exposure in man based on behavioral and physiological responses in rodents

NASA Technical Reports Server (NTRS)

Gross, M. K.

1983-01-01

An examination of animal response to CO is studied along with potential models for extrapolating animal test data to humans. The best models for extrapolating data were found to be the Probit and Weibull models.

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

DOT National Transportation Integrated Search

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...

Factors Influencing the Incidence of Obesity in Australia: A Generalized Ordered Probit Model.

PubMed

Avsar, Gulay; Ham, Roger; Tannous, W Kathy

2017-02-10

The increasing health costs of and the risks factors associated with obesity are well documented. From this perspective, it is important that the propensity of individuals towards obesity is analyzed. This paper uses longitudinal data from the Household Income and Labour Dynamics in Australia (HILDA) Survey for 2005 to 2010 to model those variables which condition the probability of being obese. The model estimated is a random effects generalized ordered probit, which exploits two sources of heterogeneity; the individual heterogeneity of panel data models and heterogeneity across body mass index (BMI) categories. The latter is associated with non-parallel thresholds in the generalized ordered model, where the thresholds are functions of the conditioning variables, which comprise economic, social, and demographic and lifestyle variables. To control for potential predisposition to obesity, personality traits augment the empirical model. The results support the view that the probability of obesity is significantly determined by the conditioning variables. Particularly, personality is found to be important and these outcomes reinforce other work examining personality and obesity.

A Note on the Heterogeneous Choice Model

ERIC Educational Resources Information Center

Rohwer, Goetz

2015-01-01

The heterogeneous choice model (HCM) has been proposed as an extension of the standard logit and probit models, which allows taking into account different error variances of explanatory variables. In this note, I show that in an important special case, this model is just another way to specify an interaction effect.

Multiple-Shrinkage Multinomial Probit Models with Applications to Simulating Geographies in Public Use Data.

PubMed

Burgette, Lane F; Reiter, Jerome P

2013-06-01

Multinomial outcomes with many levels can be challenging to model. Information typically accrues slowly with increasing sample size, yet the parameter space expands rapidly with additional covariates. Shrinking all regression parameters towards zero, as often done in models of continuous or binary response variables, is unsatisfactory, since setting parameters equal to zero in multinomial models does not necessarily imply "no effect." We propose an approach to modeling multinomial outcomes with many levels based on a Bayesian multinomial probit (MNP) model and a multiple shrinkage prior distribution for the regression parameters. The prior distribution encourages the MNP regression parameters to shrink toward a number of learned locations, thereby substantially reducing the dimension of the parameter space. Using simulated data, we compare the predictive performance of this model against two other recently-proposed methods for big multinomial models. The results suggest that the fully Bayesian, multiple shrinkage approach can outperform these other methods. We apply the multiple shrinkage MNP to simulating replacement values for areal identifiers, e.g., census tract indicators, in order to protect data confidentiality in public use datasets.

Evaluation of Statistical Methods for Modeling Historical Resource Production and Forecasting

NASA Astrophysics Data System (ADS)

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 model is designed in a way that each cycle is described by the same three parameters as conventional multicyclic Hubbert model and every two cycles are connected with a transition width. Transition width indicates the shift from one cycle to the next and is described as weighted coaddition of neighboring two cycles. It is determined by three parameters: transition year, transition width, and gamma parameter for weighting. The cycle-jumping method provides superior model compared to the conventional multicyclic Hubbert model and reflects historical production behavior more reasonably and practically, by better modeling of the effects of technological transitions and socioeconomic factors that affect historical resource production behavior by explicitly considering the form of the transitions between production cycles.

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

PubMed

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

2015-04-16

Depression is a mental health state whose frequency has been increasing in modern societies. It imposes a great burden, because of the strong impact on people's quality of life and happiness. Depression can be reliably diagnosed and treated in primary care: if more people could get effective treatments earlier, the costs related to depression would be reversed. The aim of this study was to examine the influence of socio-economic factors and gender on depressed mood, focusing on Korea. In fact, in spite of the great amount of empirical studies carried out for other countries, few epidemiological studies have examined the socio-economic determinants of depression in Korea and they were either limited to samples of employed women or did not control for individual health status. Moreover, as the likely data endogeneity (i.e. the possibility of correlation between the dependent variable and the error term as a result of autocorrelation or simultaneity, such as, in this case, the depressed mood due to health factors that, in turn might be caused by depression), might bias the results, the present study proposes an empirical approach, based on instrumental variables, to deal with this problem. Data for the year 2008 from the Korea National Health and Nutrition Examination Survey (KNHANES) were employed. About seven thousands of people (N= 6,751, of which 43% were males and 57% females), aged from 19 to 75 years old, were included in the sample considered in the analysis. In order to take into account the possible endogeneity of some explanatory variables, two Instrumental Variables Probit (IVP) regressions were estimated; the variables for which instrumental equations were estimated were related to the participation of women to the workforce and to good health, as reported by people in the sample. Explanatory variables were related to age, gender, family factors (such as the number of family members and marital status) and socio-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. 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.

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

PubMed Central

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

2015-01-01

Background: Depression is a mental health state whose frequency has been increasing in modern societies. It imposes a great burden, because of the strong impact on people’s quality of life and happiness. Depression can be reliably diagnosed and treated in primary care: if more people could get effective treatments earlier, the costs related to depression would be reversed. The aim of this study was to examine the influence of socio-economic factors and gender on depressed mood, focusing on Korea. In fact, in spite of the great amount of empirical studies carried out for other countries, few epidemiological studies have examined the socio-economic determinants of depression in Korea and they were either limited to samples of employed women or did not control for individual health status. Moreover, as the likely data endogeneity (i.e. the possibility of correlation between the dependent variable and the error term as a result of autocorrelation or simultaneity, such as, in this case, the depressed mood due to health factors that, in turn might be caused by depression), might bias the results, the present study proposes an empirical approach, based on instrumental variables, to deal with this problem. Methods: Data for the year 2008 from the Korea National Health and Nutrition Examination Survey (KNHANES) were employed. About seven thousands of people (N= 6,751, of which 43% were males and 57% females), aged from 19 to 75 years old, were included in the sample considered in the analysis. In order to take into account the possible endogeneity of some explanatory variables, two Instrumental Variables Probit (IVP) regressions were estimated; the variables for which instrumental equations were estimated were related to the participation of women to the workforce and to good health, as reported by people in the sample. Explanatory variables were related to age, gender, family factors (such as the number of family members and marital status) and socio-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 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. PMID:26340392

A tale of two epidemics: gender differences in socio-demographic characteristics and sexual behaviors among HIV positive individuals in Mexico City.

PubMed

Bautista-Arredondo, Sergio; Servan-Mori, Edson; Beynon, Fenella; González, Andrea; Volkow, Patricia

2015-12-16

To date, the HIV epidemic in Mexico has been concentrated mainly among men who have sex with men, butheterosexual transmission, particularly to women, is increasingly important. This study examine gender differences in socio-demographic characteristics and risk behaviors of HIV positive individuals in Mexico City. We analyzed data from a cross-sectional survey of 1,490 clinic patients (male:female ratio 8:1) with HIV inMexico City in 2010. We examined socio-demographic characteristics, risk behavior, and history of HIV infection.From multivariate non-linear probability (probit) models we calculated predicted probabilities by sex of several outcomes: marginalization, demographic and sexual risk behaviors. Significant differences were found between men and women. Multivariate models suggest that women had lower schooling levels; were less likely to have been employed in the past month and earn more than the minimal wage; more likely to have children, to have been sexually abused, to never have used condoms and to report having been infected by a stable partner. Additionally, women were less likely to report having a partner with a history of migration to the USA and to have engaged in transactional sex. Significant differences exist between men and women with HIV in Mexico City in terms of their socioeconomicand behavioral profiles, which translate into differences in terms of exposure to HIV infection. Women face social and economic vulnerability while men tend to have riskier sexual behavior. Gender issues must be approached in prevention and treatment efforts, using diverse methods to target those most vulnerable and at risk.

USE OF WEIBULL FUNCTION FOR NON-LINEAR ANALYSIS OF EFFECTS OF LOW LEVELS OF SIMULATED HERBICIDE DRIFT ON PLANTS

EPA Science Inventory

We compared two regression models, which are based on the Weibull and probit functions, for the analysis of pesticide toxicity data from laboratory studies on Illinois crop and native plant species. Both mathematical models are continuous, differentiable, strictly positive, and...

Recommender system based on scarce information mining.

PubMed

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.

Women's Individual Asset Ownership and Experience of Intimate Partner Violence: Evidence From 28 International Surveys.

PubMed

Peterman, Amber; Pereira, Audrey; Bleck, Jennifer; Palermo, Tia M; Yount, Kathryn M

2017-05-01

To assess the oft-perceived protective relationship between women's asset ownership and experience of intimate partner violence (IPV) in the previous 12 months. We used international survey data from women aged 15 to 49 years from 28 Demographic and Health Surveys (2010-2014) to examine the association between owning assets and experience of recent IPV, matching on household wealth by using multivariate probit models. Matching methods helped to account for the higher probability that women in wealthier households also have a higher likelihood of owning assets. Asset ownership of any type was negatively associated with IPV in 3 countries, positively associated in 5 countries, and had no significant relationship in 20 countries (P < .10). Disaggregation by asset type, sole or joint ownership, women's age, and community level of women's asset ownership similarly showed no conclusive patterns. Results suggest that the relationship between women's asset ownership and IPV is highly context specific. Additional methodologies and data are needed to identify causality, and to understand how asset ownership differs from other types of women's economic empowerment.

The intermediate endpoint effect in logistic and probit regression

PubMed Central

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 conclusions regarding the intermediate effect. PMID:17942466

Grades as Information

ERIC Educational Resources Information Center

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…

Exploratory Analysis of Survey Data for Understanding Adoption of Novel Aerospace Systems

NASA Astrophysics Data System (ADS)

Reddy, Lauren M.

In order to meet the increasing demand for manned and unmanned flight, the air transportation system must constantly evolve. As new technologies or operational procedures are conceived, we must determine their effect on humans in the system. In this research, we introduce a strategy to assess how individuals or organizations would respond to a novel aerospace system. We employ the most appropriate and sophisticated exploratory analysis techniques on the survey data to generate insight and identify significant variables. We employ three different methods for eliciting views from individuals or organizations who are affected by a system: an opinion survey, a stated preference survey, and structured interviews. We conduct an opinion survey of both the general public and stakeholders in the unmanned aircraft industry to assess their knowledge, attitude, and practices regarding unmanned aircraft. We complete a statistical analysis of the multiple-choice questions using multinomial logit and multivariate probit models and conduct qualitative analysis on free-text questions. We next present a stated preference survey of the general public on the use of an unmanned aircraft package delivery service. We complete a statistical analysis of the questions using multinomial logit, ordered probit, linear regression, and negative binomial models. Finally, we discuss structured interviews conducted on stakeholders from ANSPs and airlines operating in the North Atlantic. We describe how these groups may choose to adopt a new technology (space-based ADS-B) or operational procedure (in-trail procedures). We discuss similarities and differences between the stakeholders groups, the benefits and costs of in-trail procedures and space-based ADS-B as reported by the stakeholders, and interdependencies between the groups interviewed. To demonstrate the value of the data we generated, we explore how the findings from the surveys can be used to better characterize uncertainty in the cost-benefit analysis of aerospace systems. We demonstrate how the findings from the opinion and stated preference surveys can be infused into the cost-benefit analysis of an unmanned aircraft delivery system. We also demonstrate how to apply the findings from the interviews to characterize uncertainty in the estimation of the benefits of space-based ADS-B.

Dose-volume effects in pathologic lymph nodes in locally advanced cervical cancer.

PubMed

Bacorro, Warren; Dumas, Isabelle; Escande, Alexandre; Gouy, Sebastien; Bentivegna, Enrica; Morice, Philippe; Haie-Meder, Christine; Chargari, Cyrus

2018-03-01

In cervical cancer patients, dose-volume relationships have been demonstrated for tumor and organs-at-risk, but not for pathologic nodes. The nodal control probability (NCP) according to dose/volume parameters was investigated. Patients with node-positive cervical cancer treated curatively with external beam radiotherapy (EBRT) and image-guided brachytherapy (IGABT) were identified. Nodal doses during EBRT, IGABT and boost were converted to 2-Gy equivalent (α/β = 10 Gy) and summed. Pathologic nodes were followed individually from diagnosis to relapse. Statistical analyses comprised log-rank tests (univariate analyses), Cox proportional model (factors with p ≤ 0.1 in univariate) and Probit analyses. A total of 108 patients with 254 unresected pathological nodes were identified. The mean nodal volume at diagnosis was 3.4 ± 5.8 cm 3 . The mean total nodal EQD2 doses were 55.3 ± 5.6 Gy. Concurrent chemotherapy was given in 96%. With a median follow-up of 33.5 months, 20 patients (18.5%) experienced relapse in nodes considered pathologic at diagnosis. Overall nodal recurrence rate was 9.1% (23/254). On univariate analyses, nodal volume (threshold: 3 cm 3 , p < .0001) and lymph node dose (≥57.5 Gy α/β10 , p = .039) were significant for nodal control. The use of simultaneous boost was borderline for significance (p = .07). On multivariate analysis, volume (HR = 8.2, 4.0-16.6, p < .0001) and dose (HR = 2, 1.05-3.9, p = .034) remained independent factors. Probit analysis combining dose and volume showed significant relationships with NCP, with increasing gap between the curves with higher nodal volumes. A nodal dose-volume effect on NCP is demonstrated for the first time, with increasing NCP benefit of additional doses to higher-volume nodes. Copyright © 2018 Elsevier Inc. All rights reserved.

A novel, efficient method for estimating the prevalence of acute malnutrition in resource-constrained and crisis-affected settings: A simulation study.

PubMed

Frison, Severine; Kerac, Marko; Checchi, Francesco; Nicholas, Jennifer

2017-01-01

The assessment of the prevalence of acute malnutrition in children under five is widely used for the detection of emergencies, planning interventions, advocacy, and monitoring and evaluation. This study examined PROBIT Methods which convert parameters (mean and standard deviation (SD)) of a normally distributed variable to a cumulative probability below any cut-off to estimate acute malnutrition in children under five using Middle-Upper Arm Circumference (MUAC). We assessed the performance of: PROBIT Method I, with mean MUAC from the survey sample and MUAC SD from a database of previous surveys; and PROBIT Method II, with mean and SD of MUAC observed in the survey sample. Specifically, we generated sub-samples from 852 survey datasets, simulating 100 surveys for eight sample sizes. Overall the methods were tested on 681 600 simulated surveys. PROBIT methods relying on sample sizes as small as 50 had better performance than the classic method for estimating and classifying the prevalence of acute malnutrition. They had better precision in the estimation of acute malnutrition for all sample sizes and better coverage for smaller sample sizes, while having relatively little bias. They classified situations accurately for a threshold of 5% acute malnutrition. Both PROBIT methods had similar outcomes. PROBIT Methods have a clear advantage in the assessment of acute malnutrition prevalence based on MUAC, compared to the classic method. Their use would require much lower sample sizes, thus enable great time and resource savings and permit timely and/or locally relevant prevalence estimates of acute malnutrition for a swift and well-targeted response.

A Bayesian Approach for Nonlinear Structural Equation Models with Dichotomous Variables Using Logit and Probit Links

ERIC Educational Resources Information Center

Lee, Sik-Yum; Song, Xin-Yuan; Cai, Jing-Heng

2010-01-01

Analysis of ordered binary and unordered binary data has received considerable attention in social and psychological research. This article introduces a Bayesian approach, which has several nice features in practical applications, for analyzing nonlinear structural equation models with dichotomous data. We demonstrate how to use the software…

The episodic random utility model unifies time trade-off and discrete choice approaches in health state valuation

PubMed Central

Craig, Benjamin M; Busschbach, Jan JV

2009-01-01

Background To present an episodic random utility model that unifies time trade-off and discrete choice approaches in health state valuation. Methods First, we introduce two alternative random utility models (RUMs) for health preferences: the episodic RUM and the more common instant RUM. For the interpretation of time trade-off (TTO) responses, we show that the episodic model implies a coefficient estimator, and the instant model implies a mean slope estimator. Secondly, we demonstrate these estimators and the differences between the estimates for 42 health states using TTO responses from the seminal Measurement and Valuation in Health (MVH) study conducted in the United Kingdom. Mean slopes are estimates with and without Dolan's transformation of worse-than-death (WTD) responses. Finally, we demonstrate an exploded probit estimator, an extension of the coefficient estimator for discrete choice data that accommodates both TTO and rank responses. Results By construction, mean slopes are less than or equal to coefficients, because slopes are fractions and, therefore, magnify downward errors in WTD responses. The Dolan transformation of WTD responses causes mean slopes to increase in similarity to coefficient estimates, yet they are not equivalent (i.e., absolute mean difference = 0.179). Unlike mean slopes, coefficient estimates demonstrate strong concordance with rank-based predictions (Lin's rho = 0.91). Combining TTO and rank responses under the exploded probit model improves the identification of health state values, decreasing the average width of confidence intervals from 0.057 to 0.041 compared to TTO only results. Conclusion The episodic RUM expands upon the theoretical framework underlying health state valuation and contributes to health econometrics by motivating the selection of coefficient and exploded probit estimators for the analysis of TTO and rank responses. In future MVH surveys, sample size requirements may be reduced through the incorporation of multiple responses under a single estimator. PMID:19144115

Bayesian Estimation of Panel Data Fractional Response Models with Endogeneity: An Application to Standardized Test Rates

ERIC Educational Resources Information Center

Kessler, Lawrence M.

2013-01-01

In this paper I propose Bayesian estimation of a nonlinear panel data model with a fractional dependent variable (bounded between 0 and 1). Specifically, I estimate a panel data fractional probit model which takes into account the bounded nature of the fractional response variable. I outline estimation under the assumption of strict exogeneity as…

Firm-Related Training Tracks: A Random Effects Ordered Probit Model

ERIC Educational Resources Information Center

Groot, Wim; van den Brink, Henriette Maassen

2003-01-01

A random effects ordered response model of training is estimated to analyze the existence of training tracks and time varying coefficients in training frequency. Two waves of a Dutch panel survey of workers are used covering the period 1992-1996. The amount of training received by workers increased during the period 1994-1996 compared to…

Estimation of Item Parameters and the GEM Algorithm.

ERIC Educational Resources Information Center

Tsutakawa, Robert K.

The models and procedures discussed in this paper are related to those presented in Bock and Aitkin (1981), where they considered the 2-parameter probit model and approximated a normally distributed prior distribution of abilities by a finite and discrete distribution. One purpose of this paper is to clarify the nature of the general EM (GEM)…

Linking harvest choices to timber supply

Treesearch

Jeffrey P. Prestemon; David N. Wear

2000-01-01

Aggregate timber supply by ownership was investigated for a small region by applying stand-level harvest choice models to a representative sample of stands and then aggregating to regional totals using the area-frame of the forest survey. Timber harvest choices were estimated as probit models for three ownership categories in coastal plain southern pine stands of North...

Persistence in the Determination of Work-Related Training Participation: Evidence from the BHPS, 1991-1997

ERIC Educational Resources Information Center

Sousounis, Panos; Bladen-Hovell, Robin

2010-01-01

In this paper we investigate the role of workers' training history in determining current training-incidence. The analysis is conducted on an unbalanced sample comprising information on approximately 5000 employees from the first seven waves of the BHPS. Training participation is modelled as a dynamic random effects probit model where the effects…

[How to intervene and prevent stunting of children from homes belonging to the Sisbén in Caldas].

PubMed

Benjumea, María Victoria; Parra, José Hernán; Jaramillo, Juan Felipe

2017-12-01

Growth retardation or chronic malnutrition (low height for age) indicates a failure in the natural genetic potential that allows us to growth. To estimate predictive models of growth retardation in households with children younger than five years in the department of Caldas and registered in the identification system of potential beneficiaries of social programs (Sistema de Identificación de Potenciales Beneficiarios de Programas Sociales, Sisbén). We conducted an analytical study in all households (N=56,987) included in the Sisbén III database with the presence of children younger than five years (N=33,244). The variables under study were demographic and socioeconomic characteristics, health service access, housing, poverty, education, job market, and growth retardation. The multivariate analysis was done in two phases: first, an exploratory analysis of households using hierarchical classification (cluster), then estimation of a nonlinear predictive model (probit) with growth retardation as the dependent variable. The largest proportion of growth retardation in children younger than five years was found in southcentral Caldas, in urban centers, and households with monthly income lower than USD$ 65. Poverty in Caldas women-headed households with children younger than five years registered in the Sisbén was the main predictor of growth retardation.

Do increases in cigarette prices lead to increases in sales of cigarettes with high tar and nicotine yields?

PubMed

Farrelly, Matthew C; Loomis, Brett R; Mann, Nathan H

2007-10-01

We used scanner data on cigarette prices and sales collected from supermarkets across the United States from 1994 to 2004 to test the hypothesis that cigarette prices are positively correlated with sales of cigarettes with higher tar and nicotine content. During this period the average inflation-adjusted price for menthol cigarettes increased 55.8%. Price elasticities from multivariate regression models suggest that this price increase led to an increase of 1.73% in sales-weighted average tar yields and a 1.28% increase in sales-weighted average nicotine yields for menthol cigarettes. The 50.5% price increase of nonmenthol varieties over the same period yielded an estimated increase of 1% in tar per cigarette but no statistically significant increase in nicotine yields. An ordered probit model of the impact of cigarette prices on cigarette strength (ultra-light, light, full flavor, unfiltered) offers an explanation: As cigarette prices increase, the probability that stronger cigarette types will be sold increases. This effect is larger for menthol than for nonmenthol cigarettes. Our results are consistent with earlier population-based cross-sectional and longitudinal studies showing that higher cigarette prices and taxes are associated with increasing consumption of higher-yield cigarettes by smokers.

Modeling willingness to pay for land conservation easements: treatment of zero and protest bids and application and policy implications

Treesearch

Seong-Hoon Cho; Steven T. Yen; J. Michael Bowker; David H. Newman

2008-01-01

This study compares an ordered probit model and a Tobit model with selection to take into account both true zero and protest zero bids while estimating the willingness to pay (WTP) for conservation easements in Macon County, NC. By comparing the two models, the ordered/Unordered selection issue of the protest responses is analyzed to demonstrate how the treatment of...

The Earnings Impact of Training Duration in a Developing Country. An Ordered Probit Selection Model of Colombia's "Servicio Nacional de Aprendizaje" (SENA).

ERIC Educational Resources Information Center

Jimenez, Emmanuel; Kugler, Bernardo

1987-01-01

Estimates the earnings impact of an extensive inservice training program in the developing world, Colombia's Servicio Nacional de Aprendizaje (SENA), through a comparison of nongraduates' and graduates' earnings profiles. (JOW)

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

PubMed

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.

Mechanism-based model for tumor drug resistance.

PubMed

Kuczek, T; Chan, T C

1992-01-01

The development of tumor resistance to cytotoxic agents has important implications in the treatment of cancer. If supported by experimental data, mathematical models of resistance can provide useful information on the underlying mechanisms and aid in the design of therapeutic regimens. We report on the development of a model of tumor-growth kinetics based on the assumption that the rates of cell growth in a tumor are normally distributed. We further assumed that the growth rate of each cell is proportional to its rate of total pyrimidine synthesis (de novo plus salvage). Using an ovarian carcinoma cell line (2008) and resistant variants selected for chronic exposure to a pyrimidine antimetabolite, N-phosphonacetyl-L-aspartate (PALA), we derived a simple and specific analytical form describing the growth curves generated in 72 h growth assays. The model assumes that the rate of de novo pyrimidine synthesis, denoted alpha, is shifted down by an amount proportional to the log10 PALA concentration and that cells whose rate of pyrimidine synthesis falls below a critical level, denoted alpha 0, can no longer grow. This is described by the equation: Probability (growth) = probability (alpha 0 less than alpha-constant x log10 [PALA]). This model predicts that when growth curves are plotted on probit paper, they will produce straight lines. This prediction is in agreement with the data we obtained for the 2008 cells. Another prediction of this model is that the same probit plots for the resistant variants should shift to the right in a parallel fashion. Probit plots of the dose-response data obtained for each resistant 2008 line following chronic exposure to PALA again confirmed this prediction. Correlation of the rightward shift of dose responses to uridine transport (r = 0.99) also suggests that salvage metabolism plays a key role in tumor-cell resistance to PALA. Furthermore, the slope of the regression lines enables the detection of synergy such as that observed between dipyridamole and PALA. Although the rate-normal model was used to study the rate of salvage metabolism in PALA resistance in the present study, it may be widely applicable to modeling of other resistance mechanisms such as gene amplification of target enzymes.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

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 maintainedmore » 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.« less

Determinants of tree quality and lumber value in natural uneven-aged southern pine stands

Treesearch

Jeffrey P. Prestemon; Joseph Buongiorno

2000-01-01

An ordered-probit model was developed to predict tree grade from tree- and stand-level variables, some of which could be changed by management. Applied to uneven-aged mixed loblolly (Pinus taeda L.) - shortleaf pine (Pinus echinata Mill.) stands, the model showed that the grade of pine trees was highly correlated with tree diameter...

Social Participation and Disaster Risk Reduction Behaviors in Tsunami Prone Areas.

PubMed

Witvorapong, Nopphol; Muttarak, Raya; Pothisiri, Wiraporn

2015-01-01

This paper examines the relationships between social participation and disaster risk reduction actions. A survey of 557 households in tsunami prone areas in Phang Nga, Thailand was conducted following the 2012 Indian Ocean earthquakes. We use a multivariate probit model to jointly estimate the likelihood of undertaking three responses to earthquake and tsunami hazards (namely, (1) following disaster-related news closely, (2) preparing emergency kits and/or having a family emergency plan, and (3) having an intention to migrate) and community participation. We find that those who experienced losses from the 2004 tsunami are more likely to participate in community activities and respond to earthquake hazards. Compared to men, women are more likely to prepare emergency kits and/or have an emergency plan and have a greater intention to migrate. Living in a community with a higher proportion of women with tertiary education increases the probability of engaging in community activities and carrying out disaster risk reduction measures. Individuals who participate in village-based activities are 5.2% more likely to undertake all three risk reduction actions compared to those not engaging in community activities. This implies that encouraging participation in community activities can have positive externalities in disaster mitigation.

Women’s Individual Asset Ownership and Experience of Intimate Partner Violence: Evidence From 28 International Surveys

PubMed Central

Pereira, Audrey; Bleck, Jennifer; Palermo, Tia M.; Yount, Kathryn M.

2017-01-01

Objectives. To assess the oft-perceived protective relationship between women’s asset ownership and experience of intimate partner violence (IPV) in the previous 12 months. Methods. We used international survey data from women aged 15 to 49 years from 28 Demographic and Health Surveys (2010–2014) to examine the association between owning assets and experience of recent IPV, matching on household wealth by using multivariate probit models. Matching methods helped to account for the higher probability that women in wealthier households also have a higher likelihood of owning assets. Results. Asset ownership of any type was negatively associated with IPV in 3 countries, positively associated in 5 countries, and had no significant relationship in 20 countries (P < .10). Disaggregation by asset type, sole or joint ownership, women’s age, and community level of women’s asset ownership similarly showed no conclusive patterns. Conclusions. Results suggest that the relationship between women’s asset ownership and IPV is highly context specific. Additional methodologies and data are needed to identify causality, and to understand how asset ownership differs from other types of women’s economic empowerment. PMID:28398779

Social Participation and Disaster Risk Reduction Behaviors in Tsunami Prone Areas

PubMed Central

Witvorapong, Nopphol; Muttarak, Raya; Pothisiri, Wiraporn

2015-01-01

This paper examines the relationships between social participation and disaster risk reduction actions. A survey of 557 households in tsunami prone areas in Phang Nga, Thailand was conducted following the 2012 Indian Ocean earthquakes. We use a multivariate probit model to jointly estimate the likelihood of undertaking three responses to earthquake and tsunami hazards (namely, (1) following disaster-related news closely, (2) preparing emergency kits and/or having a family emergency plan, and (3) having an intention to migrate) and community participation. We find that those who experienced losses from the 2004 tsunami are more likely to participate in community activities and respond to earthquake hazards. Compared to men, women are more likely to prepare emergency kits and/or have an emergency plan and have a greater intention to migrate. Living in a community with a higher proportion of women with tertiary education increases the probability of engaging in community activities and carrying out disaster risk reduction measures. Individuals who participate in village-based activities are 5.2% more likely to undertake all three risk reduction actions compared to those not engaging in community activities. This implies that encouraging participation in community activities can have positive externalities in disaster mitigation. PMID:26153891

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

Treesearch

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...

Reducing fatalities and severe injuries on Florida's high-speed multi-lane arterial corridors : part II, analysis of the crash level data, final report, April 2009

DOT National Transportation Integrated Search

2009-04-28

This report presents the analysis conducted to identify the factors that contribute to severe and fatal crash occurrence on multilane corridors. The authors preliminary investigation using simultaneous ordered probit model provided enough evidence...

Using Neural Networks to Predict MBA Student Success

ERIC Educational Resources Information Center

Naik, Bijayananda; Ragothaman, Srinivasan

2004-01-01

Predicting MBA student performance for admission decisions is crucial for educational institutions. This paper evaluates the ability of three different models--neural networks, logit, and probit to predict MBA student performance in graduate programs. The neural network technique was used to classify applicants into successful and marginal student…

Lethal Temperature for Pinewood Nematode, Bursaphelenchus xylophilus, in Infested Wood Using Microwave Energy

PubMed Central

Hoover, Kelli; Uzunovic, Adnan; Gething, Brad; Dale, Angela; Leung, Karen; Ostiguy, Nancy; Janowiak, John J.

2010-01-01

To reduce the risks associated with global transport of wood infested with pinewood nematode Bursaphelenchus xylophilus, microwave irradiation was tested at 14 temperatures in replicated wood samples to determine the temperature that would kill 99.9968% of nematodes in a sample of ≥ 100,000 organisms, meeting a level of efficacy of Probit 9. Treatment of these heavily infested wood samples (mean of > 1,000 nematodes/g of sapwood) produced 100% mortality at 56 °C and above, held for 1 min. Because this “brute force” approach to Probit 9 treats individual nematodes as the observational unit regardless of the number of wood samples it takes to treat this number of organisms, we also used a modeling approach. The best fit was to a Probit function, which estimated lethal temperature at 62.2 (95% confidence interval 59.0-70.0) °C. This discrepancy between the observed and predicted temperature to achieve Probit 9 efficacy may have been the result of an inherently limited sample size when predicting the true mean from the total population. The rate of temperature increase in the small wood samples (rise time) did not affect final nematode mortality at 56 °C. In addition, microwave treatment of industrial size, infested wood blocks killed 100% of > 200,000 nematodes at ≥ 56 °C held for 1 min in replicated wood samples. The 3rd-stage juvenile (J3) of the nematode, that is resistant to cold temperatures and desiccation, was abundant in our wood samples and did not show any resistance to microwave treatment. Regression analysis of internal wood temperatures as a function of surface temperature produced a regression equation that could be used with a relatively high degree of accuracy to predict internal wood temperatures, under the conditions of this study. These results provide strong evidence of the ability of microwave treatment to successfully eradicate B. xylophilus in infested wood at or above 56 °C held for 1 min. PMID:22736846

Bayesian multinomial probit modeling of daily windows of ...

EPA Pesticide Factsheets

Past epidemiologic studies suggest maternal ambient air pollution exposure during critical periods of the pregnancy is associated with fetal development. We introduce a multinomial probit model that allows for the joint identification of susceptible daily periods during the pregnancy for 12 individual types of CHDs with respect to maternal PM2.5 exposure. We apply the model to a dataset of mothers from the National Birth Defect Prevention Study where daily PM2.5 exposures from weeks 2-8 of pregnancy are assigned (specific to each location and pregnancy date) using predictions from the downscaler pollution model. Results are compared to an aggregated exposure model which defines exposure as the average value over pregnancy weeks 2-8. Increased PM2.5 exposure during pregnancy days 53 and 50-51 for pulmonary valve stenosis and tetralogy of Fallot, respectively, are associated with an increased probability of development of each CHD. The largest estimated effect is seen for atrioventricular septal defects on pregnancy day 14. The aggregated exposure model fails to identify any significant windows of susceptibility during pregnancy weeks 2-8 for the considered CHDs. Considering daily PM2.5 exposures in a new modeling framework revealed positive associations for defects that the standard aggregated exposure model was unable to identify. Disclaimer: The views expressed in this manuscript are those of the authors and do not necessarily represent the views or policie

Median infectious dose (ID₅₀) of porcine reproductive and respiratory syndrome virus isolate MN-184 via aerosol exposure.

PubMed

Cutler, Timothy D; Wang, Chong; Hoff, Steven J; Kittawornrat, Apisit; Zimmerman, Jeffrey J

2011-08-05

The median infectious dose (ID(50)) of porcine reproductive and respiratory syndrome (PRRS) virus isolate MN-184 was determined for aerosol exposure. In 7 replicates, 3-week-old pigs (n=58) respired 10l of airborne PRRS virus from a dynamic aerosol toroid (DAT) maintained at -4°C. Thereafter, pigs were housed in isolation and monitored for evidence of infection. Infection occurred at virus concentrations too low to quantify by microinfectivity assays. Therefore, exposure dose was determined using two indirect methods ("calculated" and "theoretical"). "Calculated" virus dose was derived from the concentration of rhodamine B monitored over the exposure sequence. "Theoretical" virus dose was based on the continuous stirred-tank reactor model. The ID(50) estimate was modeled on the proportion of pigs that became infected using the probit and logit link functions for both "calculated" and "theoretical" exposure doses. Based on "calculated" doses, the probit and logit ID(50) estimates were 1 × 10(-0.13)TCID(50) and 1 × 10(-0.14)TCID(50), respectively. Based on "theoretical" doses, the probit and logit ID(50) were 1 × 10(0.26)TCID(50) and 1 × 10(0.24)TCID(50), respectively. For each point estimate, the 95% confidence interval included the other three point estimates. The results indicated that MN-184 was far more infectious than PRRS virus isolate VR-2332, the only other PRRS virus isolate for which ID(50) has been estimated for airborne exposure. Since aerosol ID(50) estimates are available for only these two isolates, it is uncertain whether one or both of these isolates represent the normal range of PRRS virus infectivity by this route. Copyright © 2011 Elsevier B.V. All rights reserved.

Bayesian Analysis of Multilevel Probit Models for Data with Friendship Dependencies

ERIC Educational Resources Information Center

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…

The Richer, the Happier? An Empirical Investigation in Selected European Countries

ERIC Educational Resources Information Center

Seghieri, Chiara; Desantis, Gustavo; Tanturri, Maria Letizia

2006-01-01

This study analyses the relationship between subjective and objective measures of well-being in selected European countries using the data of the European Community Household Panel (ECHP). In the first part of the paper, we develop a random-effect ordered probit model, separately for each country, relating the subjective measure of income…

Intimate Partner Violence in Colombia: Who Is at Risk?

ERIC Educational Resources Information Center

Friedemann-Sanchez, Greta; Lovaton, Rodrigo

2012-01-01

The role that domestic violence plays in perpetuating poverty is often overlooked as a development issue. Using data from the 2005 Demographic Health Survey, this paper examines the prevalence of intimate partner violence in Colombia. Employing an intrahousehold bargaining framework and a bivariate probit model, it assesses the prevalence of and…

The Australian electricity market's pre-dispatch process: Some observations on its efficiency using ordered probit model

NASA Astrophysics Data System (ADS)

Zainudin, Wan Nur Rahini Aznie; Becker, Ralf; Clements, Adam

2015-12-01

Many market participants in Australia Electricity Market had cast doubts on whether the pre-dispatch process in the electricity market is able to give them good and timely quantity and price information. In a study by [11], they observed a significant bias (mainly indicating that the pre-dispatch process tends to underestimate spot price outcomes), a seasonality features of the bias across seasons and/or trading periods and changes in bias across the years in our sample period (1999 to 2007). In a formal setting of an ordered probit model we establish that there are some exogenous variables that are able to explain increased probabilities of over- or under-predictions of the spot price. It transpires that meteorological data, expected pre-dispatch prices and information on past over- and under-predictions contribute significantly to explaining variation in the probabilities for over- and under-predictions. The results allow us to conjecture that some of the bids and re-bids provided by electricity generators are not made in good faith.

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

Treesearch

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 empirical comparison of methods for analyzing correlated data from a discrete choice survey to elicit patient preference for colorectal cancer screening

PubMed Central

2012-01-01

Background A discrete choice experiment (DCE) is a preference survey which asks participants to make a choice among product portfolios comparing the key product characteristics by performing several choice tasks. Analyzing DCE data needs to account for within-participant correlation because choices from the same participant are likely to be similar. In this study, we empirically compared some commonly-used statistical methods for analyzing DCE data while accounting for within-participant correlation based on a survey of patient preference for colorectal cancer (CRC) screening tests conducted in Hamilton, Ontario, Canada in 2002. Methods A two-stage DCE design was used to investigate the impact of six attributes on participants' preferences for CRC screening test and willingness to undertake the test. We compared six models for clustered binary outcomes (logistic and probit regressions using cluster-robust standard error (SE), random-effects and generalized estimating equation approaches) and three models for clustered nominal outcomes (multinomial logistic and probit regressions with cluster-robust SE and random-effects multinomial logistic model). We also fitted a bivariate probit model with cluster-robust SE treating the choices from two stages as two correlated binary outcomes. The rank of relative importance between attributes and the estimates of β coefficient within attributes were used to assess the model robustness. Results In total 468 participants with each completing 10 choices were analyzed. Similar results were reported for the rank of relative importance and β coefficients across models for stage-one data on evaluating participants' preferences for the test. The six attributes ranked from high to low as follows: cost, specificity, process, sensitivity, preparation and pain. However, the results differed across models for stage-two data on evaluating participants' willingness to undertake the tests. Little within-patient correlation (ICC ≈ 0) was found in stage-one data, but substantial within-patient correlation existed (ICC = 0.659) in stage-two data. Conclusions When small clustering effect presented in DCE data, results remained robust across statistical models. However, results varied when larger clustering effect presented. Therefore, it is important to assess the robustness of the estimates via sensitivity analysis using different models for analyzing clustered data from DCE studies. PMID:22348526

Care-Seeking Patterns and Direct Economic Burden of Injuries in Bangladesh.

PubMed

Alfonso, Natalia Y; Alonge, Olakunle; Hoque, Dewan Md Emdadul; Baset, Kamran Ul; Hyder, Adnan A; Bishai, David

2017-04-29

This study provides a comprehensive review of the care-seeking patterns and direct economic burden of injuries from the victims' perspective in rural Bangladesh using a 2013 household survey covering 1.17 million people. Descriptive statistics and bivariate analyses were used to derive rates and test the association between variables. An analytic model was used to estimate total injury out-of-pocket (OOP) payments and a multivariate probit regression model assessed the relationship between financial distress and injury type. Results show non-fatal injuries occur to 1 in 5 people in our sample per year. With average household size of 4.5 in Bangladesh--every household has an injury every year. Most non-fatally injured patients sought healthcare from drug sellers. Less than half of fatal injuries sought healthcare and half of those with care were hospitalized. Average OOP payments varied significantly (range: $8-$830) by injury type and outcome (fatal vs. non-fatal). Total injury OOP expenditure was $$355,795 and $5000 for non-fatal and fatal injuries, respectively, per 100,000 people. The majority of household heads with injuries reported financial distress. This study can inform injury prevention advocates on disparities in healthcare usage, OOP costs and financial distress. Reallocation of resources to the most at risk populations can accelerate reduction of preventable injuries and prevent injury related catastrophic payments and impoverishment.

Care-Seeking Patterns and Direct Economic Burden of Injuries in Bangladesh

PubMed Central

Alfonso, Yira Natalia; Alonge, Olakunle; Hoque, Dewan Md Emdadul; Ul Baset, Md Kamran; Hyder, Adnan A.; Bishai, David

2017-01-01

This study provides a comprehensive review of the care-seeking patterns and direct economic burden of injuries from the victims’ perspective in rural Bangladesh using a 2013 household survey covering 1.17 million people. Descriptive statistics and bivariate analyses were used to derive rates and test the association between variables. An analytic model was used to estimate total injury out-of-pocket (OOP) payments and a multivariate probit regression model assessed the relationship between financial distress and injury type. Results show non-fatal injuries occur to 1 in 5 people in our sample per year. With average household size of 4.5 in Bangladesh--every household has an injury every year. Most non-fatally injured patients sought healthcare from drug sellers. Less than half of fatal injuries sought healthcare and half of those with care were hospitalized. Average OOP payments varied significantly (range: $8–$830) by injury type and outcome (fatal vs. non-fatal). Total injury OOP expenditure was $355,795 and $5000 for non-fatal and fatal injuries, respectively, per 100,000 people. The majority of household heads with injuries reported financial distress. This study can inform injury prevention advocates on disparities in healthcare usage, OOP costs and financial distress. Reallocation of resources to the most at risk populations can accelerate reduction of preventable injuries and prevent injury related catastrophic payments and impoverishment. PMID:28468240

Categorical Data Analysis Using a Skewed Weibull Regression Model

NASA Astrophysics Data System (ADS)

Caron, Renault; Sinha, Debajyoti; Dey, Dipak; Polpo, Adriano

2018-03-01

In this paper, we present a Weibull link (skewed) model for categorical response data arising from binomial as well as multinomial model. We show that, for such types of categorical data, the most commonly used models (logit, probit and complementary log-log) can be obtained as limiting cases. We further compare the proposed model with some other asymmetrical models. The Bayesian as well as frequentist estimation procedures for binomial and multinomial data responses are presented in details. The analysis of two data sets to show the efficiency of the proposed model is performed.

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

PubMed

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, both in strength and significance, for males in countries as the likelihood of a move increases; and 2) the effect of the kin and friend contract variable in Colombia appears to be related to its usefulness in explaining th initial consideration of a move rather than the plans that carry a probability or certainty of implementation. The careful measurement of strength of migration intentions and the application of ordinal probit estimation methods to the analysis of prospective migration may contribute to the refinement of our understanding of the process of migration decision making across a range of geographical, cultural, and developmental contexts.

Study of Personnel Attrition and Revocation within U.S. Marine Corps Air Traffic Control Specialties

DTIC Science & Technology

2012-03-01

Entrance Processing Stations (MEPS) and recruit depots, to include non-cognitive testing, such as Navy Computer Adaptive Personality Scales ( NCAPS ...Revocation, Selection, MOS, Regression, Probit, dProbit, STATA, Statistics, Marginal Effects, ASVAB, AFQT, Composite Scores, Screening, NCAPS 15. NUMBER...Navy Computer Adaptive Personality Scales ( NCAPS ), during recruitment. It is also recommended that an economic analysis be conducted comparing the

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

NASA Astrophysics Data System (ADS)

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.

The Effect of Response Time on Conjoint Analysis Estimates of Rainforest Protection Values

Treesearch

Thomas Holmes; Keith Alger; Christian Zinkhan; D. Evan Mercer

1998-01-01

This paper reports the first estimutes of willingness to pay (WTP) for rain forest protection in the threatened Atlantic Coastal Forest ecosystem in northeastern Brazil. Conjoint analysis data were collected from Brazilian tourists for recreational bundles with complex prices. An ordered probit model with time-varying parameters and heteroskedastic errors was...

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

ERIC Educational Resources Information Center

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…

Does Alcohol Use during High School Affect Educational Attainment?: Evidence from the National Education Longitudinal Study

ERIC Educational Resources Information Center

Chatterji, Pinka

2006-01-01

This paper uses data from the National Education Longitudinal Study to estimate the association between high school alcohol use and educational attainment measured around age 26. Initially, the effect of alcohol use on educational attainment is estimated using baseline probit models, which ignore the possibility that unmeasured determinants of…

The Effect of Overskilling Dynamics on Wages

ERIC Educational Resources Information Center

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…

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

PubMed

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.

The Interaction between HIV and Intestinal Helminth Parasites Coinfection with Nutrition among Adults in KwaZulu-Natal, South Africa

PubMed Central

Mabaso, M.; Mamba, T.; Napier, C. E.; Mkhize-Kwitshana, Z. L.

2017-01-01

In South Africa few studies have examined the effects of the overlap of HIV and helminth infections on nutritional status. This cross-sectional study investigated the interaction between HIV and intestinal helminths coinfection with nutritional status among KwaZulu-Natal adults. Participants were recruited from a comprehensive primary health care clinic and stratified based on their HIV, stool parasitology, IgE, and IgG4 results into four groups: the uninfected, HIV infected, helminth infected, and HIV-helminth coinfected groups. The nutritional status was assessed using body mass index, 24-hour food recall, micro-, and macronutrient biochemical markers. Univariate and multivariate multinomial probit regression models were used to assess nutritional factors associated with singly and dually infected groups using the uninfected group as a reference category. Biochemically, the HIV-helminth coinfected group was associated with a significantly higher total protein, higher percentage of transferrin saturation, and significantly lower ferritin. There was no significant association between single or dual infections with HIV and helminths with micro- and macronutrient deficiency; however general obesity and low micronutrient intake patterns, which may indicate a general predisposition to micronutrient and protein-energy deficiency, were observed and may need further investigations. PMID:28421202

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

PubMed

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.

The Predicaments of Non-Residential Students in Ghanaian Institutions of Higher Education: A Micro-Level Empirical Evidence

ERIC Educational Resources Information Center

Addai, Isaac

2015-01-01

This paper in the field of capacity building and students' affairs used the external survey assessment techniques of the probit model to examine the predicaments of non-resident students of the College of Technology Education, University of Education, Winneba. Considering the very limited residential facilities and the growing demand for tertiary…

Certification of family forests: What influences owners’ awareness and participation?

Treesearch

Selmin F. Creamer; Keith A. Blatner; Brett J. Butler

2012-01-01

In the United States, 35% of the forestland is owned by family forest owners with approximately 0.2% of this land reported to be enrolled in a forest certification system. The current study was conducted to provide insights into factors influencing family forest owners’ decisions to certify their lands. The bivariate probit model with sample selection results suggests...

An Analysis of the Social Impact of the Stipend Program for Secondary School Girls of Khyber Pakhtunkhwa

ERIC Educational Resources Information Center

Ahmed, Vaqar; Zeshan, Muhammad

2014-01-01

The present study carries out an impact analysis of a conditional cash transfer (CCT) program for secondary-school girls in seven districts of Khyber Pakhtunkhwa province in Pakistan, including Battagram, Bonair, Hangu, Kohistan, Shangla, Tank, and Upper Dir. In 2012 we collected household-level primary data and used a probit model for…

Who Is Overeducated and Why? Probit and Dynamic Mixed Multinomial Logit Analyses of Vertical Mismatch in East and West Germany

ERIC Educational Resources Information Center

Boll, Christina; Leppin, Julian Sebastian; Schömann, Klaus

2016-01-01

Overeducation potentially signals a productivity loss. With Socio-Economic Panel data from 1984 to 2011 we identify drivers of educational mismatch for East and West medium and highly educated Germans. Addressing measurement error, state dependence and unobserved heterogeneity, we run dynamic mixed multinomial logit models for three different…

The Effects of Designated Pollutants on Plants

DTIC Science & Technology

1978-11-01

two marigold . . . . . . . . . . . . . . . . . . . . . . . . . 44 21. Probit analysis of five plant species: petunia , bean, radish, salvia and tomato...Tagetes patula L. French dwarf double goldie Marigold Tagetes erecta L. American,Senator Dirksen Petunia Petunia hybrida Vilm. White cascade Radish...00 s0 too 200 4w0 1000 1 20 3O 060 100 20 00 1000 HCL CONCENTRATION (MG Mŗ ) Figure 21. Probit analysis of five plant species: 16-day- petunia , 25-day

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

PubMed Central

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. PMID:25466413

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

PubMed Central

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

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

PubMed

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

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

PubMed

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.

Health Insurance: The Trade-Off Between Risk Pooling and Moral Hazard.

DTIC Science & Technology

1989-12-01

bias comes about because we suppress the intercept term in estimating VFor the power, the test is against 1, - 1. With this transform, the risk...dealing with the same utility function. As one test of whether families behave in the way economic theory suggests, we have also fitted a probit model of...nonparametric alternative to test our results’ sensitivity to the assumption of a normal error in both the theoretical and empirical models of the

Validity of using ad hoc methods to analyze secondary traits in case-control association studies.

PubMed

Yung, Godwin; Lin, Xihong

2016-12-01

Case-control association studies often collect from their subjects information on secondary phenotypes. Reusing the data and studying the association between genes and secondary phenotypes provide an attractive and cost-effective approach that can lead to discovery of new genetic associations. A number of approaches have been proposed, including simple and computationally efficient ad hoc methods that ignore ascertainment or stratify on case-control status. Justification for these approaches relies on the assumption of no covariates and the correct specification of the primary disease model as a logistic model. Both might not be true in practice, for example, in the presence of population stratification or the primary disease model following a probit model. In this paper, we investigate the validity of ad hoc methods in the presence of covariates and possible disease model misspecification. We show that in taking an ad hoc approach, it may be desirable to include covariates that affect the primary disease in the secondary phenotype model, even though these covariates are not necessarily associated with the secondary phenotype. We also show that when the disease is rare, ad hoc methods can lead to severely biased estimation and inference if the true disease model follows a probit model instead of a logistic model. Our results are justified theoretically and via simulations. Applied to real data analysis of genetic associations with cigarette smoking, ad hoc methods collectively identified as highly significant (P<10-5) single nucleotide polymorphisms from over 10 genes, genes that were identified in previous studies of smoking cessation. © 2016 WILEY PERIODICALS, INC.

Exploring motorcyclist injury severity resulting from various crash configurations at T-junctions in the United Kingdom--an application of the ordered probit models.

PubMed

Pai, Chih-Wei; Saleh, Wafaa

2007-03-01

The fact that motorcycle users tend to be more vulnerable to injuries than those using other motorized vehicles may act synergistically with the complexity of conflicting movements between vehicles and motorcycles to increase injury severity in a junction-type accident. A junction-type collision tends to be more severe than a non-junction case due to the fact that some of the injurious crashes such as angle-collision commonly occur. Existing studies have applied several statistical modeling techniques to examine influential factors on the occurrences of different crashes among motorized vehicles but surprisingly very little has empirically explored whether a particular crash type, resulting from a junction-type accident, is more injurious to motorcyclists. This article attempts to investigate whether a particular collision is more deadly to motorcyclists conditioned on crash occurrence at T-junctions in the U.K., while controlling for environment, vehicle, and demographic factors. The statistical modeling technique employed is the ordered probit models using the data extracted from the STATS19 accident injury database (1999-2004). The modeling found determinants of injury severity among motorcyclists at T-junctions in the U.K. For example, an approach-turn/head-on collision is much more injurious to motorcyclists; and, those riding in early morning (i.e., 0000-0659) are more likely to be severely injured. This study offers a guideline for future research, as well as insight into potential prevention strategies that might help moderate motorcyclist injuries.

Hamburger hazards and emotions.

PubMed

Olsen, Nina Veflen; Røssvoll, Elin; Langsrud, Solveig; Scholderer, Joachim

2014-07-01

Previous studies indicate that many consumers eat rare hamburgers and that information about microbiological hazards related to undercooked meat not necessarily leads to more responsible behavior. With this study we aim to investigate whether consumers' willingness to eat hamburgers depends on the emotions they experience when confronted with the food. A representative sample of 1046 Norwegian consumers participated in an online experiment. In the first part, participants were randomly divided into two groups. One group was confronted with a picture of a rare hamburger, whereas the other group was confronted with a picture of a well-done hamburger. The respondents were instructed to imagine that they were served the hamburger on the picture and then to indicate which emotions they experienced: fear, disgust, surprise, interest, pleasure, or none of these. In part two, all respondents were confronted with four pictures of hamburgers cooked to different degrees of doneness (rare, medium rare, medium well-done, well-done), and were asked to state their likelihood of eating. We analyzed the data by means of a multivariate probit model and two linear fixed-effect models. The results show that confrontation with rare hamburgers evokes more fear and disgust than confrontation with well-done hamburgers, that all hamburgers trigger pleasure and interest, and that a consumer's willingness to eat rare hamburgers depends on the particular type of emotion evoked. These findings indicate that emotions play an important role in a consumer's likelihood of eating risky food, and should be considered when developing food safety strategies. Copyright © 2014 Elsevier Ltd. All rights reserved.

Tolerance of ciliated protozoan Paramecium bursaria (Protozoa, Ciliophora) to ammonia and nitrites

NASA Astrophysics Data System (ADS)

Xu, Henglong; Song, Weibo; Lu, Lu; Alan, Warren

2005-09-01

The tolerance to ammonia and nitrites in freshwater ciliate Paramecium bursaria was measured in a conventional open system. The ciliate was exposed to different concentrations of ammonia and nitrites for 2h and 12h in order to determine the lethal concentrations. Linear regression analysis revealed that the 2h-LC50 value for ammonia was 95.94 mg/L and for nitrite 27.35 mg/L using probit scale method (with 95% confidence intervals). There was a linear correlation between the mortality probit scale and logarithmic concentration of ammonia which fit by a regression equation y=7.32 x 9.51 ( R 2=0.98; y, mortality probit scale; x, logarithmic concentration of ammonia), by which 2 h-LC50 value for ammonia was found to be 95.50 mg/L. A linear correlation between mortality probit scales and logarithmic concentration of nitrite is also followed the regression equation y=2.86 x+0.89 ( R 2=0.95; y, mortality probit scale; x, logarithmic concentration of nitrite). The regression analysis of toxicity curves showed that the linear correlation between exposed time of ammonia-N LC50 value and ammonia-N LC50 value followed the regression equation y=2 862.85 e -0.08 x ( R 2=0.95; y, duration of exposure to LC50 value; x, LC50 value), and that between exposed time of nitrite-N LC50 value and nitrite-N LC50 value followed the regression equation y=127.15 e -0.13 x ( R 2=0.91; y, exposed time of LC50 value; x, LC50 value). The results demonstrate that the tolerance to ammonia in P. bursaria is considerably higher than that of the larvae or juveniles of some metozoa, e.g. cultured prawns and oysters. In addition, ciliates, as bacterial predators, are likely to play a positive role in maintaining and improving water quality in aquatic environments with high-level ammonium, such as sewage treatment systems.

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

ERIC Educational Resources Information Center

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…

An in vitro Corneal Model with a Laser Damage Threshold at 2 Micrometers That is Similar to That in the Rabbit

DTIC Science & Technology

2007-11-01

Proceedings 3. DATES COVERED (From - To) June 2007- November 2007 4. TITLE AND SUBTITLE An In Vitro Corneal Model with a Laser Damage Threshold at 2...2-µm wavelength output of a thulium fiber laser with 4 mm beam diameter for 0.25 seconds in a thermally controlled environment and then assayed for...data in the literature. 15. SUBJECT TERMS corneal organotypic culture, laser , threshold, thermography, Probit 16. SECURITY CLASSIFICATION OF

Social and economic costs and health-related quality of life in stroke survivors in the Canary Islands, Spain

PubMed Central

2012-01-01

Background Cost-of-illness analysis is the main method of providing an overall vision of the economic impact of a disease. Such studies have been used to set priorities for healthcare policies and inform resource allocation. The aim of this study was to determine the economic burden and health-related quality of life (HRQOL) in the first, second and third years after surviving a stroke in the Canary Islands, Spain. Methods Cross-sectional, retrospective study of 448 patients with stroke based on ICD 9 discharge codes, who received outpatient care at five hospitals. The study was approved by the Research Ethics Committee of Nuestra Señora de la Candelaria University Hospital. Data on demographic characteristics, health resource utilization, informal care, labor productivity losses and HRQOL were collected from the hospital admissions databases and questionnaires completed by stroke patients or their caregivers. Labor productivity losses were calculated from physical units and converted into monetary units with a human capital-based method. HRQOL was measured with the EuroQol EQ-5D questionnaire. Healthcare costs, productivity losses and informal care costs were analyzed with log-normal, probit and ordered probit multivariate models. Results The average cost for each stroke survivor was €17 618 in the first, €14 453 in the second and €12 924 in the third year after the stroke; the reference year for unit prices was 2004. The largest expenditures in the first year were informal care and hospitalizations; in the second and third years the main costs were for informal care, productivity losses and medication. Mean EQ-5D index scores for stroke survivors were 0.50 for the first, 0.47 for the second and 0.46 for the third year, and mean EQ-5D visual analog scale scores were 56, 52 and 55, respectively. Conclusions The main strengths of this study lie in our bottom-up-approach to costing, and in the evaluation of stroke survivors from a broad perspective (societal costs) in the first, second and third years after surviving the stroke. This type of analysis is rare in the Spanish context. We conclude that stroke incurs considerable societal costs among survivors to three years and there is substantial deterioration in HRQOL. PMID:22970797

Effectiveness of conservation easements in agricultural regions.

PubMed

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.

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

PubMed

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.

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

PubMed

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.

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

PubMed Central

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 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. PMID:27158217

Binge drinking and sleep problems among young adults.

PubMed

Popovici, Ioana; French, Michael T

2013-09-01

As most of the literature exploring the relationships between alcohol use and sleep problems is descriptive and with small sample sizes, the present study seeks to provide new information on the topic by employing a large, nationally representative dataset with several waves of data and a broad set of measures for binge drinking and sleep problems. We use data from the National Longitudinal Study of Adolescent Health (Add Health), a nationally representative survey of adolescents and young adults. The analysis sample consists of all Wave 4 observations without missing values for the sleep problems variables (N=14,089, 53% females). We estimate gender-specific multivariate probit models with a rich set of socioeconomic, demographic, physical, and mental health variables to control for confounding factors. Our results confirm that alcohol use, and specifically binge drinking, is positively and significantly associated with various types of sleep problems. The detrimental effects on sleep increase in magnitude with frequency of binge drinking, suggesting a dose-response relationship. Moreover, binge drinking is associated with sleep problems independent of psychiatric conditions. The statistically strong association between sleep problems and binge drinking found in this study is a first step in understanding these relationships. Future research is needed to determine the causal links between alcohol misuse and sleep problems to inform appropriate clinical and policy responses. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Role of intrinsic search cues in the formation of consumer preferences and choice for pork chops.

PubMed

Verbeke, Wim; De Smet, Stefaan; Vackier, Isabelle; Van Oeckel, Monique J; Warnants, Nathalie; Van Kenhove, Patrick

2005-02-01

This study investigates the role of drip, colour, marbling and fat cover as intrinsic search cues in the formation of pork chop preferences and individual determinants. Data are collected from a sample of 443 pork consumers in Belgium through using repeated selection of chops from randomised photobooks and questionnaires including socio-demographic, attitudinal and behavioural variables. Data analysis includes mixture regression analysis, bivariate descriptive statistics and the estimation of multivariate probit models. Consumers sampled in this study prefer pork chops without fat cover. Preference for fat cover is stronger among male, 35+ aged consumers with lower levels of awareness of the relation between food and health and who like pork for other reasons than taste and nutritional value (all p<0.05). Preference for colour is equally consistent within an individual, though fifty-fifty light-dark, with dark chops being more preferred by 35+ aged consumers (p<0.05). Preferences for marbling and drip are not consistent and not determined by joint socio-demographic, attitudinal and behavioural factors. Preferences for cue levels are not correlated, except a weak relation between preference for dark chops without drip (r=0.116). Preferences are apparently formed by deductions with the use of single cues as key information, mainly based on fat cover or colour, and random choice on marbling and drip.

Waste disposal and households' heterogeneity. Identifying factors shaping attitudes towards source-separated recycling in Bogotá, Colombia.

PubMed

J Padilla, Alcides; Trujillo, Juan C

2018-04-01

Solid waste management in many cities of developing countries is not environmentally sustainable. People traditionally dispose of their solid waste in unsuitable urban areas like sidewalks and satellite dumpsites. This situation nowadays has become a serious public health problem in big Latin American conurbations. Among these densely-populated urban spaces, the Colombia's capital and main city stands out as a special case. In this study, we aim to identify the factors that shape the attitudes towards source-separated recycling among households in Bogotá. Using data from the Colombian Department of Statistics and Bogotá's multi-purpose survey, we estimated a multivariate Probit model. In general, our results show that the higher the household's socioeconomic class, the greater its effort for separating solid wastes. Likewise, our findings also allowed us to characterize household profiles regarding solid waste separation and considering each socioeconomic class. Among these profiles, we found that at lower socioeconomic classes, the attitudes towards solid waste separation are influenced by the use of Internet, the membership to an environmentalist organization, the level of education of the head of household and the homeownership. Hence, increasing the education levels within the poorest segment of the population, promoting affordable housing policies and facilitating Internet access for the vulnerable population could reinforce households' attitudes towards a greater source-separated recycling effort. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

PubMed

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

2013-10-01

Vehicle operating speed measured on roadways is a critical component for a host of analysis in the transportation field including transportation safety, traffic flow modeling, roadway geometric design, vehicle emissions modeling, and road user route decisions. The current research effort contributes to the literature on examining vehicle speed on urban roads methodologically and substantively. In terms of methodology, we formulate a new econometric model framework for examining speed profiles. The proposed model is an ordered response formulation of a fractional split model. The ordered nature of the speed variable allows us to propose an ordered variant of the fractional split model in the literature. The proposed formulation allows us to model the proportion of vehicles traveling in each speed interval for the entire segment of roadway. We extend the model to allow the influence of exogenous variables to vary across the population. Further, we develop a panel mixed version of the fractional split model to account for the influence of site-specific unobserved effects. The paper contributes substantively by estimating the proposed model using a unique dataset from Montreal consisting of weekly speed data (collected in hourly intervals) for about 50 local roads and 70 arterial roads. We estimate separate models for local roads and arterial roads. The model estimation exercise considers a whole host of variables including geometric design attributes, roadway attributes, traffic characteristics and environmental factors. The model results highlight the role of various street characteristics including number of lanes, presence of parking, presence of sidewalks, vertical grade, and bicycle route on vehicle speed proportions. The results also highlight the presence of site-specific unobserved effects influencing the speed distribution. The parameters from the modeling exercise are validated using a hold-out sample not considered for model estimation. The results indicate 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.

An Analysis of the Effects of Military Service on Retirees’ Civilian Earnings

DTIC Science & Technology

1993-12-01

labor market following separation from the service. Thus. military retirees receive two incomes over a lengthy period of their lives, the military pension...labor market experience. Within this model. Probit analysis Was emprio~cd to correct for expected selecti\\I1!% bilas. The sampie employed in this...have a more direct correlation with the civilian lob market . The third phase examined occupational transfer effects. A dummy transfer variable was

Menarcheal age of girls from dysfunctional families.

PubMed

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, 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.

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

PubMed

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 impact of diabetes on employment and work productivity.

PubMed

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.

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

PubMed

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.

A Framework for the Analysis of the Reserve Officer Augmentation Process in the United States Marine Corps

DTIC Science & Technology

1987-12-01

occupation group, category (i.e., strength, loss, etc.), years of commissioned service (YCS), grade, occupation, source of commission, education, sex ...OF MCORP OUTPUT OCCUPATION GROUP: All CAT: Strength YCS: 01 - 09 GRADE: All Unrestricted Officers OCCUPATION: All SOURCE: All EDUCATION: All SEX : All...source of commission, sex , MOS, GCT, and other pertinent variables such as the performance index. A Probit or Logit model could be utilized. The variables

Probabilistic Model for Laser Damage to the Human Retina

DTIC Science & Technology

2012-03-01

the beam. Power density may be measured in radiant exposure, J cm2 , or by irradiance , W cm2 . In the experimental database used in this study and...to quan- tify a binary response, either lethal or non-lethal, within a population such as insects or rats. In directed energy research, probit...value of the normalized Arrhenius damage integral. In a one-dimensional simulation, the source term is determined as a spatially averaged irradiance (W

Demand for Health Insurance by Military Retirees

DTIC Science & Technology

2015-05-01

Plans,” The Journal of Health Economics 16, No. 2 (1997): 231–247 and Bruce A. Strombom, Thomas C. Buchmueller, and Paul J. Feldstein, “Switching Costs...Initiative: Volume 3. Health Care Utilization and Costs,” R -4244/3-HA (Santa Monica, CA: RAND Corporation, 1993). 10 probit regression model for TRICARE...Solomon (1998) Stanford University employees, panel data, 1994–95 HMO vs. PPO and FFS Logit -0.29 Fixed-Effects Logit -0.97 Barringer and Mitchell

Does reporting behaviour bias the measurement of social inequalities in self-rated health in Indonesia? An anchoring vignette analysis.

PubMed

Hanandita, Wulung; Tampubolon, Gindo

2016-05-01

Studies on self-rated health outcomes are fraught with problems when individuals' reporting behaviour is systematically biased by demographic, socio-economic, or cultural factors. Analysing the data drawn from the Indonesia Family Life Survey 2007, this paper aims to investigate the extent of differential health reporting behaviour by demographic and socio-economic status among Indonesians aged 40 and older (N = 3735). Interpersonal heterogeneity in reporting style is identified by asking respondents to rate a number of vignettes that describe varying levels of health status in targeted health domains (mobility, pain, cognition, sleep, depression, and breathing) using the same ordinal response scale that is applied to the self-report health question. A compound hierarchical ordered probit model is fitted to obtain health differences by demographic and socio-economic status. The obtained regression coefficients are then compared to the standard ordered probit model. We find that Indonesians with more education tend to rate a given health status in each domain more negatively than their less-educated counterparts. Allowing for such differential reporting behaviour results in relatively stronger positive education effects. There is a need to correct for differential reporting behaviour using vignettes when analysing self-rated health measures in older adults in Indonesia. Unless such an adjustment is made, the salutary effect of education will be underestimated.

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

PubMed

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.

Income, family characteristics, and physical violence toward children.

PubMed

Berger, Lawrence M

2005-02-01

This paper discusses the ways in which existing microeconomic theories of partner abuse, intra-family bargaining, and distribution of resources within families may contribute to our current understanding of physical child abuse. The empirical implications of this discussion are then tested on data from the 1985 National Family Violence Survey (NFVS) in order to estimate the effects of income, family characteristics, and state characteristics on physical violence toward children. The sample consists of 2,760 families with children from the NFVS. Probit and ordered probit models are used to explore relationships between income, family characteristics, state characteristics, and physical violence toward children among single-parent and two-parent families. In both single-parent and two-parent families, depression, maternal alcohol consumption, and history of family violence affect children's probabilities of being abused. Additionally, income is significantly related to violence toward children in single-parent families. These results reinforce earlier findings that demographic characteristics, maternal depression, maternal alcohol use, and intra-family patterns of violence may largely contribute to child abuse. This research also suggests that income may play a substantially more important role in regard to parental violence in single-parent families than in two-parent families.

[Relevant factors of early puberty timing in urban primary schools in Chongqing].

PubMed

Luo, Yan; Liu, Qin; Wen, Yi; Liu, Shudan; Lei, Xun; Wang, Hong

2016-05-01

To investigate the status of puberty timing and relevant factors of early puberty timing in children from grade one to four in urban primary schools of Chongqing. According to the purposive sample method, four urban primary schools in Chongqing were selected and of which 1471 children from grade one to four who have obtained informed consent were recruited. Questionnaire survey on social-demographic characteristics and family environment (e.g., age, parents' relationship, diet and lifestyle, etc), and Pubertal Development Scale (PDS) survey and physical examination (measurements of height, weight, pubertal development status, etc) were conducted. P25, P50, P75 ages of each important pubertal event were calculated by probit regression. Univariate and multivariate analysis were used to analyze relevant factors. The detection rate of early puberty timing was 17.7%, and the median ages of the onset of breast and testicular development were 10.77 and 11.48 years old, respectively. Multivariate logistic regression showed that early puberty timing occurred more likely in girls than in boys (OR = 0.561, 95% CI 0.406-0.774), and bad relationship between parents (OR = 1.320, 95% CI 1.007-1.729) and hair-products-use (OR = 1.685, 95%, CI 1.028-2.762) were risk factors of early puberty timing. Early onset of puberty in urban Chongqing is still exist. Gender, parents' relationship, and hair-products-use have an essential impact on early puberty timing.

Long-term effects of obesity on employment and work limitations among U.S. Adults, 1986 to 1999.

PubMed

Tunceli, Kaan; Li, Kemeng; Williams, L Keoki

2006-09-01

To determine the relationships between BMI and workforce participation and the presence of work limitations in a U.S. working-age population. We used data from the Panel Study of Income Dynamics, a nationwide prospective cohort, to estimate the effect of obesity in 1986 on employment and work limitations in 1999. Individuals were classified into the following weight categories: underweight (BMI < 18.5), normal weight (18.5 < or = BMI < 25), overweight (25 < or = BMI < 30), and obese (BMI > or = 30). Using multivariable probit models, we estimated the relationships between obesity and both employment and work disability. All analyses were stratified by sex. After adjusting for baseline sociodemographic characteristics, smoking status, exercise, and self-reported health, obesity was associated with reduced employment at follow-up [men: marginal effect (ME) -4.8 percentage points (pp); p < 0.05; women: ME -5.8 pp; p < 0.10]. Among employed women, being either overweight or obese was associated with an increase in self-reported work limitations when compared with normal-weight individuals (overweight: ME +3.9 pp; p < 0.01; obese: ME +12.6 pp; p < 0.01). Among men, the relationship between obesity and work limitations was not statistically significant. Obesity appears to result in future productivity losses through reduced workforce participation and increased work limitations. These findings have important implications in the U.S., which is currently experiencing a rise in the prevalence of obesity.

The household food insecurity gradient and potential reductions in adverse population mental health outcomes in Canadian adults.

PubMed

Jessiman-Perreault, Geneviève; McIntyre, Lynn

2017-12-01

Household food insecurity is related to poor mental health. This study examines whether the level of household food insecurity is associated with a gradient in the risk of reporting six adverse mental health outcomes. This study further quantifies the mental health impact if severe food insecurity, the extreme of the risk continuum, were eliminated in Canada. Using a pooled sample of the Canadian Community Health Survey (N = 302,683), we examined the relationship between level of food insecurity, in adults 18-64 years, and reporting six adverse mental health outcomes. We conducted a probit analysis adjusted for multi-variable models, to calculate the reduction in the odds of reporting mental health outcomes that might accrue from the elimination of severe food insecurity. Controlling for various demographic and socioeconomic covariates, a food insecurity gradient was found in six mental health outcomes. We calculated that a decrease between 8.1% and 16.0% in the reporting of these mental health outcomes would accrue if those who are currently severely food insecure became food secure, after controlling for covariates. Household food insecurity has a pervasive graded negative effect on a variety of mental health outcomes, in which significantly higher levels of food insecurity are associated with a higher risk of adverse mental health outcomes. Reduction of food insecurity, particularly at the severe level, is a public health concern and a modifiable structural determinant of health worthy of macro-level policy intervention.

Caregiver burden in Alzheimer's disease patients in Spain.

PubMed

Peña-Longobardo, Luz María; Oliva-Moreno, Juan

2015-01-01

Alzheimer's disease constitutes one of the leading causes of burden of disease, and it is the third leading disease in terms of economic and social costs. To analyze the burden and problems borne by informal caregivers of patients who suffer from Alzheimer's disease in Spain. We used the Survey on Disabilities, Autonomy and Dependency to obtain information on the characteristics of disabled people with Alzheimer's disease and the individuals who provide them with personal care. Additionally, statistical multivariate analyses using probit models were performed to analyze the burden placed on caregivers in terms of health, professional, and leisure/social aspects. 46% of informal caregivers suffered from health-related problems as a result of providing care, 90% had leisure-related problems, and 75% of caregivers under 65 years old admitted to suffering from problems related to their professional lives. The probability of a problem arising for an informal caregiver was positively associated with the degree of dependency of the person cared for. In the case of caring for a greatly dependent person, the probability of suffering from health-related problems was 22% higher, the probability of professional problems was 18% higher, and there was a 10% greater probability of suffering from leisure-related problems compared to non-dependents. The results show a part of the large hidden cost for society in terms of problems related to the burden lessened by the caregivers. This information should be a useful tool for designing policies focused toward supporting caregivers and improving their welfare.

Physical activity in England: who is meeting the recommended level of participation through sports and exercise?

PubMed

Anokye, Nana Kwame; Pokhrel, Subhash; Buxton, Martin; Fox-Rushby, Julia

2013-06-01

Little is known about the correlates of meeting recommended levels of participation in physical activity (PA) and how this understanding informs public health policies on behaviour change. To analyse who meets the recommended level of participation in PA in males and females separately by applying 'process' modelling frameworks (single vs. sequential 2-step process). Using the Health Survey for England 2006, (n = 14 142; ≥ 16 years), gender-specific regression models were estimated using bivariate probit with selectivity correction and single probit models. A 'sequential, 2-step process' modelled participation and meeting the recommended level separately, whereas the 'single process' considered both participation and level together. In females, meeting the recommended level was associated with degree holders [Marginal effect (ME) = 0.013] and age (ME = -0.001), whereas in males, age was a significant correlate (ME = -0.003 to -0.004). The order of importance of correlates was similar across genders, with ethnicity being the most important correlate in both males (ME = -0.060) and females (ME = -0.133). In females, the 'sequential, 2-step process' performed better (ρ = -0.364, P < 0.001) than that in males (ρ = 0.154). The degree to which people undertake the recommended level of PA through vigorous activity varies between males and females, and the process that best predicts such decisions, i.e. whether it is a sequential, 2-step process or a single-step choice, is also different for males and females. Understanding this should help to identify subgroups that are less likely to meet the recommended level of PA (and hence more likely to benefit from any PA promotion intervention).

Linking hearing impairment, employment and education.

PubMed

Garramiola-Bilbao, I; Rodríguez-Álvarez, A

2016-12-01

To analyse the impact that hearing impairment and other relevant variables have on the education and employment situation of those affected by it in the Principality of Asturias, Spain. To achieve this objective, two discrete choice models (probit) are presented. The first one associates, among other variables, hearing impairment with the individual's employment status and in the second model, an ordered multinomial probit model is used to analyse, among other variables, how the impairment affects the individual's level of studies. Although the levels of statistical significance are low, the model's estimates appear to indicate that hearing impairment in Spain increases the probability of being unemployed by 18.4% (P = 0.09). Additionally, the people suffering from such a disability are, compared with the rest of the population, 10.2% (P = 0.05) more likely to have only completed elementary studies without pursuing any further education. If an individual is able to reach a level of secondary or higher education thus enabling a future incorporation to the work place, a benefit is obviously generated for both the individual as well as society (which has additionally incurred an investment in human capital). In this regard, encouraging the education of hearing-impaired students would profit both the individual (who receives an early integration as a child), which may contribute positively to family and social factors, as well as society who have incurred the investment. Therefore, our result could indicate that programmes created to support individuals with this type of disability represent an increase of welfare both individually and socially. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Job tenure and self-reported workplace discrimination for cancer survivors 2 years after diagnosis: does employment legislation matter?

PubMed

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.

Estimation of Rank Correlation for Clustered Data

PubMed Central

Rosner, Bernard; Glynn, Robert

2017-01-01

It is well known that the sample correlation coefficient (Rxy) is the maximum likelihood estimator (MLE) of the Pearson correlation (ρxy) for i.i.d. bivariate normal data. However, this is not true for ophthalmologic data where X (e.g., visual acuity) and Y (e.g., visual field) are available for each eye and there is positive intraclass correlation for both X and Y in fellow eyes. In this paper, we provide a regression-based approach for obtaining the MLE of ρxy for clustered data, which can be implemented using standard mixed effects model software. This method is also extended to allow for estimation of partial correlation by controlling both X and Y for a vector U of other covariates. In addition, these methods can be extended to allow for estimation of rank correlation for clustered data by (a) converting ranks of both X and Y to the probit scale, (b) estimating the Pearson correlation between probit scores for X and Y, and (c) using the relationship between Pearson and rank correlation for bivariate normally distributed data. The validity of the methods in finite-sized samples is supported by simulation studies. Finally, two examples from ophthalmology and analgesic abuse are used to illustrate the methods. PMID:28399615

Binge drinking and marijuana use among menthol and non-menthol adolescent smokers: findings from the youth smoking survey.

PubMed

Azagba, Sunday; Sharaf, Mesbah F

2014-03-01

Research has shown that smoking menthol cigarettes induces smoking initiation and hinders cessation efforts especially among youth. The objective of this paper is to examine the association between menthol cigarette smoking and substance use among adolescent students in Canada. A nationally representative cross-sectional sample of 4466 Canadian students in grades 7 to 12 from the 2010-2011 Youth Smoking Survey is analyzed. A bivariate probit model is used jointly to examine the association of menthol smoking status with binge drinking and marijuana use. 32% of the current smokers in grades 7 to 12 smoke mentholated cigarettes, 73% are binge drinkers and 79% use marijuana. Results of the bivariate probit regression analysis, controlling for other covariates, show statistically significant differences in the likelihood of binge drinking and marijuana use between menthol and non-menthol smokers. Menthol cigarette smokers are 6% (ME=0.06, 95% CI=0.03-0.09) more likely to binge drink and 7% (ME=0.07, 95% CI=0.05-0.10) more likely to use marijuana. Smoking menthol cigarettes is associated with a higher likelihood of binge drinking and marijuana use among Canadian adolescents. Banning menthol in cigarettes may be beneficial to public health. Copyright © 2013 Elsevier Ltd. All rights reserved.

The Consequences of Job Displacement for Health: Moderating Influences of Economic Conditions and Educational Attainment

PubMed Central

Pearlman, Jessica

2015-01-01

This paper will examine the impact of worker displacement on health in the United States from 1975–2004, especially the extent to which the impact of displacement on health varies according to the economic conditions in the year of displacement and the education level of the displaced worker. Findings from ordered probit and fixed effects models suggest that the negative impact of displacement on health is exacerbated by a higher unemployment rate at the time of displacement and for displaced workers with a college degree. PMID:26004481

Household-Level Determinants of Soil and Water Conservation Adoption Phases: Evidence from North-Western Ethiopian Highlands.

PubMed

Teshome, Akalu; de Graaff, Jan; Kassie, Menale

2016-03-01

Soil and water conservation (SWC) practices have been promoted in the highlands of Ethiopia during the last four decades. However, the level of adoption of SWC practices varies greatly. This paper examines the drivers of different stages of adoption of SWC technologies in the north-western highlands of Ethiopia. This study is based on a detailed farm survey among 298 households in three watersheds. Simple descriptive statistics were applied to analyze the stages of adoption. An ordered probit model was used to analyze the drivers of different stages of adoption of SWC. This model is used to analyze more than two outcomes of an ordinal dependent variable. The results indicate that sampled households are found in different phases of adoption, i.e., dis-adoption/non-adoption (18.5 %), initial adoption (30.5 %), actual adoption (20.1 %), and final adoption (30.9 %). The results of the ordered probit model show that some socio-economic and institutional factors affect the adoption phases of SWC differently. Farm labor, parcel size, ownership of tools, training in SWC, presence of SWC program, social capital (e.g., cooperation with adjacent farm owners), labor sharing scheme, and perception of erosion problem have a significant positive influence on actual and final adoption phases of SWC. In addition, the final adoption phase of SWC is positively associated with tenure security, cultivated land sizes, parcel slope, and perception on SWC profitability. Policy makers should take into consideration factors affecting (continued) adoption of SWC such as profitability, tenure security, social capital, technical support, and resource endowments (e.g., tools and labor) when designing and implementing SWC policies and programs.

Household-Level Determinants of Soil and Water Conservation Adoption Phases: Evidence from North-Western Ethiopian Highlands

NASA Astrophysics Data System (ADS)

Teshome, Akalu; de Graaff, Jan; Kassie, Menale

2016-03-01

Soil and water conservation (SWC) practices have been promoted in the highlands of Ethiopia during the last four decades. However, the level of adoption of SWC practices varies greatly. This paper examines the drivers of different stages of adoption of SWC technologies in the north-western highlands of Ethiopia. This study is based on a detailed farm survey among 298 households in three watersheds. Simple descriptive statistics were applied to analyze the stages of adoption. An ordered probit model was used to analyze the drivers of different stages of adoption of SWC. This model is used to analyze more than two outcomes of an ordinal dependent variable. The results indicate that sampled households are found in different phases of adoption, i.e., dis-adoption/non-adoption (18.5 %), initial adoption (30.5 %), actual adoption (20.1 %), and final adoption (30.9 %). The results of the ordered probit model show that some socio-economic and institutional factors affect the adoption phases of SWC differently. Farm labor, parcel size, ownership of tools, training in SWC, presence of SWC program, social capital (e.g., cooperation with adjacent farm owners), labor sharing scheme, and perception of erosion problem have a significant positive influence on actual and final adoption phases of SWC. In addition, the final adoption phase of SWC is positively associated with tenure security, cultivated land sizes, parcel slope, and perception on SWC profitability. Policy makers should take into consideration factors affecting (continued) adoption of SWC such as profitability, tenure security, social capital, technical support, and resource endowments (e.g., tools and labor) when designing and implementing SWC policies and programs.

Genetic contribution to patent ductus arteriosus in the premature newborn.

PubMed

Bhandari, Vineet; Zhou, Gongfu; Bizzarro, Matthew J; Buhimschi, Catalin; Hussain, Naveed; Gruen, Jeffrey R; Zhang, Heping

2009-02-01

The most common congenital heart disease in the newborn population, patent ductus arteriosus, accounts for significant morbidity in preterm newborns. In addition to prematurity and environmental factors, we hypothesized that genetic factors play a significant role in this condition. The objective of this study was to quantify the contribution of genetic factors to the variance in liability for patent ductus arteriosus in premature newborns. A retrospective study (1991-2006) from 2 centers was performed by using zygosity data from premature twins born at < or =36 weeks' gestational age and surviving beyond 36 weeks' postmenstrual age. Patent ductus arteriosus was diagnosed by echocardiography at each center. Mixed-effects logistic regression was used to assess the effect of specific covariates. Latent variable probit modeling was then performed to estimate the heritability of patent ductus arteriosus, and mixed-effects probit modeling was used to quantify the genetic component. We obtained data from 333 dizygotic twin pairs and 99 monozygotic twin pairs from 2 centers (Yale University and University of Connecticut). Data on chorioamnionitis, antenatal steroids, gestational age, body weight, gender, respiratory distress syndrome, patent ductus arteriosus, necrotizing enterocolitis, oxygen supplementation, and bronchopulmonary dysplasia were comparable between monozygotic and dizygotic twins. We found that gestational age, respiratory distress syndrome, and institution were significant covariates for patent ductus arteriosus. After controlling for specific covariates, genetic factors or the shared environment accounted for 76.1% of the variance in liability for patent ductus arteriosus. Preterm patent ductus arteriosus is highly familial (contributed to by genetic and environmental factors), with the effect being mainly environmental, after controlling for known confounders.

Household and community income, economic shocks and risky sexual behavior of young adults: evidence from the Cape Area Panel Study 2002 and 2005.

PubMed

Dinkelman, Taryn; Lam, David; Leibbrandt, Murray

2007-11-01

To describe recent trends in adolescent sexual behavior in Cape Town, South Africa, and to determine whether household and community poverty and negative economic shocks predict risky sexual behavior. Matched survey data on 2993 African and coloured youth from the Cape Area Panel Study 2002 and 2005. Sexual debut, multiple sexual partners in past year, condom use at last sex, measured in 2002 and 2005. We tested for changes over time in reported sexual behavior and estimate multivariate probit models to measure the association between 2002 individual, household and community characteristics and 2005 sexual behavior. There was a statistically significant increase in condom use and a decrease in the incidence of multiple sexual partners between 2002 and 2005 for young women aged 17-22 years. Young women in households with 10% higher income were 0.53% less likely to debut sexually by 2005; young men in communities with a 10% higher poverty rate were 5% less likely to report condom use at last sex. Negative economic shocks are associated with a 0.04% increase in the probability of multiple partnerships for young women. Education is positively correlated with sexual debut for young women and with multiple partnerships for both sexes. Trends in sexual behavior between 2002 and 2005 indicate significant shifts towards safer practices. There is little evidence of a relationship between negative economic shocks, household and community poverty, and risky behavior. We hypothesize that the unexpected positive relationship between education and sexual debut may be driven by peer effects in schools with substantial age mixing.

Comfort and exertion while using filtering facepiece respirators with exhalation valve and an active venting system among male military personnel.

PubMed

Seng, Melvin; Wee, Liang En; Zhao, Xiahong; Cook, Alex R; Chia, Sin Eng; Lee, Vernon J

2017-07-06

This study aimed to determine if disposable filtering facepiece respirators (FFRs), with exhalation valve (EV) and a novel active venting system (AVS), provided greater perceived comfort and exertion when compared to standard N95 FFRs without these features among male military personnel performing prolonged essential outdoor duties. We used a randomised open-label controlled crossover study design to compare three FFR options: (a) standard FFR; (b) FFR with EV; and (c) FFR with EV+AVS. Male military personnel aged between 18 and 20 years completed a questionnaire at the beginning (baseline), after two hours of standardised non-strenuous outdoor duty and after 12 hours of duty divided into two-hour work-rest cycles. Participants rated the degree of discomfort, exertion and symptoms using a five-point Likert scale. The association between outcomes and the types of FFR was assessed using a multivariate ordered probit mixed-effects model. For a majority of the symptoms, study participants rated FFR with EV and FFR with EV+AVS with significantly better scores than standard FFR. Both FFR with EV and FFR with EV+AVS had significantly less discomfort (FFR with EV+AVS: 91.1%; FFR with EV: 57.6%) and exertion (FFR with EV+AVS: 83.5%; FFR with EV: 34.4%) than standard FFR. FFR with EV+AVS also had significantly better scores for exertion (53.4%) and comfort (39.4%) when compared to FFR with EV. Usage of FFR with EV+AVS resulted in significantly reduced symptoms, discomfort and exertion when compared to FFR with EV and standard FFR.

The influence of age on perceptions of anticipated financial inadequacy by palliative radiation outpatients.

PubMed

Francoeur, Richard B

2007-12-01

A consistent body of knowledge suggests that with advancing age, adults tend to report lower financial strain from their current economic condition. But are more negative perceptions shifted onto their expectations about their future economic condition? This study of seriously ill outpatients investigates whether advancing age is related to more negative expectations of future health-related financial strain, in which illness progression would necessitate greater health care consumption. Ordinal probit multivariate regression was conducted on survey findings from 268 outpatients initiating palliative radiation for recurrent cancer. Half were retirees age>/=65. Age comparisons are reported when there was no recent work transition. As age advances (from 40 to 84), outpatients incurring low objective financial stress were more likely to reveal that their health insurance and finances would be less adequate to meet future health needs. Previously, these outpatients were reported to minimize perceptions of current financial strain as age advances. Therefore, older outpatients may cope with current circumstances by displacing perceptions of financial inadequacy onto plausible future situations of cancer progression demanding greater healthcare consumption. Financial strain may be hidden in older outpatients initiating palliative radiation. These outpatients appear at risk of foregoing appropriate healthcare. Targeted screening and advocacy are warranted.

The influence of experiential learning on medical equipment adoption in general practices.

PubMed

Bourke, Jane; Roper, Stephen

2014-10-01

The benefits of the availability and use of medical equipment for medical outcomes are understood by physicians and policymakers alike. However, there is limited understanding of the decision-making processes involved in adopting and using new technologies in health care organisations. Our study focuses on the adoption of medical equipment in Irish general practices which are marked by considerable autonomy in terms of commercial practice and the range of medical services they provide. We examine the adoption of six items of medical equipment taking into account commercial, informational and experiential stimuli. Our analysis is based on primary survey data collected from a sample of 601 general practices in Ireland on practice characteristics and medical equipment use. We use a multivariate Probit to identify commonalities in the determinants of the adoption. Many factors, such as GP and practice characteristics, influence medical equipment adoption. In addition, we find significant and consistent evidence of the influence of learning-by-using effects on the adoption of medical equipment in a general practice setting. Knowledge generated by experiential or applied learning can have commercial, organisational and health care provision benefits in small health care organisations. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

PROBIT: A Probit Analysis Program for the DRES (Defence Research Establishment Suffield) Computer Facility,

DTIC Science & Technology

1986-07-01

p are are also discussed. When iteration is terminated, we can determine the effective dose at the A percentile level or EDX ; that is the dose at...corresponds to the lower limit, fl.. The fiducial or Fieller limits on x , i.e., the EDX are then f’ = exp If.1 (36) It can be readily shown that g = t2 v-1...4 .4+ 1W (n 0 CL C ~o 0 0 X U) -W CLW W 24W Q X C W C > VW U)w5 z C z 5memo f- G5555t (j) tSS a;C w CO W QZ 1 I- ~ ~ ~ C (. 5r 5W <A * a W Www w V

Reanalysis of the start of the UK 1967 to 1968 foot-and-mouth disease epidemic to calculate airborne transmission probabilities.

PubMed

Sanson, R L; Gloster, J; Burgin, L

2011-09-24

The aims of this study were to statistically reassess the likelihood that windborne spread of foot-and-mouth disease (FMD) virus (FMDV) occurred at the start of the UK 1967 to 1968 FMD epidemic at Oswestry, Shropshire, and to derive dose-response probability of infection curves for farms exposed to airborne FMDV. To enable this, data on all farms present in 1967 in the parishes near Oswestry were assembled. Cases were infected premises whose date of appearance of first clinical signs was within 14 days of the depopulation of the index farm. Logistic regression was used to evaluate the association between infection status and distance and direction from the index farm. The UK Met Office's NAME atmospheric dispersion model (ADM) was used to generate plumes for each day that FMDV was excreted from the index farm based on actual historical weather records from October 1967. Daily airborne FMDV exposure rates for all farms in the study area were calculated using a geographical information system. Probit analyses were used to calculate dose-response probability of infection curves to FMDV, using relative exposure rates on case and control farms. Both the logistic regression and probit analyses gave strong statistical support to the hypothesis that airborne spread occurred. There was some evidence that incubation period was inversely proportional to the exposure rate.

Are Lowered Socioeconomic Circumstances Causally Related to Tooth Loss? A Natural Experiment Involving the 2011 Great East Japan Earthquake.

PubMed

Matsuyama, Yusuke; Aida, Jun; Tsuboya, Toru; Hikichi, Hiroyuki; Kondo, Katsunori; Kawachi, Ichiro; Osaka, Ken

2017-07-01

Oral health status is correlated with socioeconomic status. However, the causal nature of the relationship is not established. Here we describe a natural experiment involving deteriorating socioeconomic circumstances following exposure to the 2011 Great East Japan Earthquake and Tsunami. We investigated the relationship between subjective economic deterioration and housing damage due to the disaster and tooth loss in a cohort of community-dwelling residents (n = 3,039), from whom we obtained information about socioeconomic status and health status in 2010 (i.e., predating the disaster). A follow-up survey was performed in 2013 (postdisaster), and 82.1% of the 4,380 eligible survivors responded. We estimated the impact of subjective economic deterioration and housing damage due to the disaster on tooth loss by fitting an instrumental variable probit model. Subjective economic deterioration and housing damage due to the disaster were significantly associated with 8.1% and 1.7% increases in the probability of tooth loss (probit coefficients were 0.469 (95% confidence interval: 0.065, 0.872) and 0.103 (95% confidence interval: 0.011, 0.196), respectively). In this natural experiment, we confirmed the causal relationship between deteriorating socioeconomic circumstances and tooth loss. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Estimation of rank correlation for clustered data.

PubMed

Rosner, Bernard; Glynn, Robert J

2017-06-30

It is well known that the sample correlation coefficient (R xy ) is the maximum likelihood estimator of the Pearson correlation (ρ xy ) for independent and identically distributed (i.i.d.) bivariate normal data. However, this is not true for ophthalmologic data where X (e.g., visual acuity) and Y (e.g., visual field) are available for each eye and there is positive intraclass correlation for both X and Y in fellow eyes. In this paper, we provide a regression-based approach for obtaining the maximum likelihood estimator of ρ xy for clustered data, which can be implemented using standard mixed effects model software. This method is also extended to allow for estimation of partial correlation by controlling both X and Y for a vector U_ of other covariates. In addition, these methods can be extended to allow for estimation of rank correlation for clustered data by (i) converting ranks of both X and Y to the probit scale, (ii) estimating the Pearson correlation between probit scores for X and Y, and (iii) using the relationship between Pearson and rank correlation for bivariate normally distributed data. The validity of the methods in finite-sized samples is supported by simulation studies. Finally, two examples from ophthalmology and analgesic abuse are used to illustrate the methods. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Taxes and Bribes in Uganda.

PubMed

Jagger, Pamela; Shively, Gerald

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 queuing, capital-at-risk, favouritism, networks, and role in the supply chain. We also find that taxes crowd-in bribery in the charcoal market.

Measuring willingness to pay to improve municipal water in southeast Anatolia, Turkey

NASA Astrophysics Data System (ADS)

Bilgic, Abdulbaki

2010-12-01

Increasing demands for water and quality concerns have highlighted the importance of accounting for household perceptions before local municipalities rehabilitate existing water infrastructures and bring them into compliance. We compared different willingness-to-pay (WTP) estimates using household surveys in the southern Anatolian region of Turkey. Our study is the first of its kind in Turkey. Biases resulting from sample selection and the endogeneity of explanatory variables were corrected. When compared to a univariate probit model, correction of these biases was shown to result in statistically significant findings through moderate reductions in mean WTP.

The effect of maternal healthcare on the probability of child survival in Azerbaijan.

PubMed

Habibov, Nazim; Fan, Lida

2014-01-01

This study assesses the effects of maternal healthcare on child survival by using nonrandomized data from a cross-sectional survey in Azerbaijan. Using 2SLS and simultaneous equation bivariate probit models, we estimate the effects of delivering in healthcare facility on probability of child survival taking into account self-selection into the treatment. For women who delivered at healthcare facilities, the probability of child survival increases by approximately 18%. Furthermore, if every woman had the opportunity to deliver in healthcare facility, then the probability of child survival in Azerbaijan as a whole would have increased by approximately 16%.

Taxes and Bribes in Uganda

PubMed Central

Jagger, Pamela; Shively, Gerald

2016-01-01

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 queuing, capital-at-risk, favouritism, networks, and role in the supply chain. We also find that taxes crowd-in bribery in the charcoal market. PMID:27274568

Corporate governance and the adoption of health information technology within integrated delivery systems.

PubMed

Baird, Aaron; Furukawa, Michael F; Rahman, Bushra; Schneller, Eugene S

2014-01-01

Although several previous studies have found "system affiliation" to be a significant and positive predictor of health information technology (IT) adoption, little is known about the association between corporate governance practices and adoption of IT within U.S. integrated delivery systems (IDSs). Rooted in agency theory and corporate governance research, this study examines the association between corporate governance practices (centralization of IT decision rights and strategic alignment between business and IT strategy) and IT adoption, standardization, and innovation within IDSs. Cross-sectional, retrospective analyses using data from the 2011 Health Information and Management Systems Society Analytics Database on adoption within IDSs (N = 485) is used to analyze the correlation between two corporate governance constructs (centralization of IT decision rights and strategic alignment) and three IT constructs (adoption, standardization, and innovation) for clinical and supply chain IT. Multivariate fractional logit, probit, and negative binomial regressions are applied. Multivariate regressions controlling for IDS and market characteristics find that measures of IT adoption, IT standardization, and innovative IT adoption are significantly associated with centralization of IT decision rights and strategic alignment. Specifically, centralization of IT decision rights is associated with 22% higher adoption of Bar Coding for Materials Management and 30%-35% fewer IT vendors for Clinical Data Repositories and Materials Management Information Systems. A combination of centralization and clinical IT strategic alignment is associated with 50% higher Computerized Physician Order Entry adoption, and centralization along with supply chain IT strategic alignment is significantly negatively correlated with Radio Frequency Identification adoption : Although IT adoption and standardization are likely to benefit from corporate governance practices within IDSs, innovation is likely to be delayed. In addition, corporate governance is not one-size-fits-all, and contingencies are important considerations.

MIXOR: a computer program for mixed-effects ordinal regression analysis.

PubMed

Hedeker, D; Gibbons, R D

1996-03-01

MIXOR provides maximum marginal likelihood estimates for mixed-effects ordinal probit, logistic, and complementary log-log regression models. These models can be used for analysis of dichotomous and ordinal outcomes from either a clustered or longitudinal design. For clustered data, the mixed-effects model assumes that data within clusters are dependent. The degree of dependency is jointly estimated with the usual model parameters, thus adjusting for dependence resulting from clustering of the data. Similarly, for longitudinal data, the mixed-effects approach can allow for individual-varying intercepts and slopes across time, and can estimate the degree to which these time-related effects vary in the population of individuals. MIXOR uses marginal maximum likelihood estimation, utilizing a Fisher-scoring solution. For the scoring solution, the Cholesky factor of the random-effects variance-covariance matrix is estimated, along with the effects of model covariates. Examples illustrating usage and features of MIXOR are provided.

Comparing the reliability of related populations with the probability of agreement

DOE PAGES

Stevens, Nathaniel T.; Anderson-Cook, Christine M.

2016-07-26

Combining information from different populations to improve precision, simplify future predictions, or improve underlying understanding of relationships can be advantageous when considering the reliability of several related sets of systems. Using the probability of agreement to help quantify the similarities of populations can help to give a realistic assessment of whether the systems have reliability that are sufficiently similar for practical purposes to be treated as a homogeneous population. In addition, the new method is described and illustrated with an example involving two generations of a complex system where the reliability is modeled using either a logistic or probit regressionmore » model. Note that supplementary materials including code, datasets, and added discussion are available online.« less

Comparing the reliability of related populations with the probability of agreement

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stevens, Nathaniel T.; Anderson-Cook, Christine M.

Combining information from different populations to improve precision, simplify future predictions, or improve underlying understanding of relationships can be advantageous when considering the reliability of several related sets of systems. Using the probability of agreement to help quantify the similarities of populations can help to give a realistic assessment of whether the systems have reliability that are sufficiently similar for practical purposes to be treated as a homogeneous population. In addition, the new method is described and illustrated with an example involving two generations of a complex system where the reliability is modeled using either a logistic or probit regressionmore » model. Note that supplementary materials including code, datasets, and added discussion are available online.« less

Price, tax and tobacco product substitution in Zambia.

PubMed

Stoklosa, Michal; Goma, Fastone; Nargis, Nigar; Drope, Jeffrey; Chelwa, Grieve; Chisha, Zunda; Fong, Geoffrey T

2018-03-24

In Zambia, the number of cigarette users is growing, and the lack of strong tax policies is likely an important cause. When adjusted for inflation, levels of tobacco tax have not changed since 2007. Moreover, roll-your-own (RYO) tobacco, a less-costly alternative to factory-made (FM) cigarettes, is highly prevalent. We modelled the probability of FM and RYO cigarette smoking using individual-level data obtained from the 2012 and 2014 waves of the International Tobacco Control (ITC) Zambia Survey. We used two estimation methods: the standard estimation method involving separate random effects probit models and a method involving a system of equations (incorporating bivariate seemingly unrelated random effects probit) to estimate price elasticities of FM and RYO cigarettes and their cross-price elasticities. The estimated price elasticities of smoking prevalence are -0.20 and -0.03 for FM and RYO cigarettes, respectively. FM and RYO are substitutes; that is, when the price of one of the products goes up, some smokers switch to the other product. The effects are stronger for substitution from FM to RYO than vice versa. This study affirms that increasing cigarette tax with corresponding price increases could significantly reduce cigarette use in Zambia. Furthermore, reducing between-product price differences would reduce substitution from FM to RYO. Since RYO use is associated with lower socioeconomic status, efforts to decrease RYO use, including through tax/price approaches and cessation assistance, would decrease health inequalities in Zambian society and reduce the negative economic consequences of tobacco use experienced by the poor. © 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.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carson, K.S.

The presence of overpopulation or unsustainable population growth may place pressure on the food and water supplies of countries in sensitive areas of the world. Severe air or water pollution may place additional pressure on these resources. These pressures may generate both internal and international conflict in these areas as nations struggle to provide for their citizens. Such conflicts may result in United States intervention, either unilaterally, or through the United Nations. Therefore, it is in the interests of the United States to identify potential areas of conflict in order to properly train and allocate forces. The purpose of thismore » research is to forecast the probability of conflict in a nation as a function of it s environmental conditions. Probit, logit and ordered probit models are employed to forecast the probability of a given level of conflict. Data from 95 countries are used to estimate the models. Probability forecasts are generated for these 95 nations. Out-of sample forecasts are generated for an additional 22 nations. These probabilities are then used to rank nations from highest probability of conflict to lowest. The results indicate that the dependence of a nation`s economy on agriculture, the rate of deforestation, and the population density are important variables in forecasting the probability and level of conflict. These results indicate that environmental variables do play a role in generating or exacerbating conflict. It is unclear that the United States military has any direct role in mitigating the environmental conditions that may generate conflict. A more important role for the military is to aid in data gathering to generate better forecasts so that the troops are adequntely prepared when conflicts arises.« less

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

NASA Astrophysics Data System (ADS)

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.

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

NASA Astrophysics Data System (ADS)

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.

Preventing land loss in coastal Louisiana: estimates of WTP and WTA.

PubMed

Petrolia, Daniel R; Kim, Tae-Goun

2011-03-01

A dichotomous-choice contingent-valuation survey was conducted in the State of Louisiana (USA) to estimate compensating surplus (CS) and equivalent surplus (ES) welfare measures for the prevention of future coastal wetland losses in Louisiana. Valuations were elicited using both willingness to pay (WTP) and willingness to accept compensation (WTA) payment vehicles. Mean CS (WTP) estimates based on a probit model using a Box-Cox specification on income was $825 per household annually, and mean ES (WTA) was estimated at $4444 per household annually. Regression results indicate that the major factors influencing support for land-loss prevention were income (positive, WTP model only), perceived hurricane protection benefits (positive), environmental and recreation protection (positive), distrust of government (negative), age (positive, WTA model only), and race (positive for whites). Copyright © 2010 Elsevier Ltd. All rights reserved.

Fertility decisions and desires in Bangladesh: an econometric investigation.

PubMed

Sirageldin, I; Khan, M A; Shah, F; Ariturk, A

1976-07-01

2 models are developed to examine fertility behavior in Bangladesh. The 1st model deals with the total number of ever-born children to a couple; the 2nd examines sequential decisions that characterize the desire for an additional child. The "Chicago-Columbia" or "New Home Economics" approach is used, but to the usual economic variables are added sociological and demographic variables; and fertility is examined in relation to the prices of child services consumed as well as a valuation of the mother's time. The data for the study were drawn from a sample of 3088 currently married women respondents to the 1968/69 Impact Survey (an extended KAP survey). The model for completed family size uses 4 endogenous variables: total live births, number of dead children, current income, and female labor force participation; these are examined in terms of 14 exogenous variables, including property ownership, age, literacy, awareness of family planning, rural vs urban, type of family, size of family, and schooling. The model is built on 4 equations with parameters estimated by 2-stage least squares technic and then subjected to multivariate analysis. The model for demand for additional children added 5 exogenous variables including sex of children, desire for children, and perceived need for education of children. This model was examined using standard probit analysis. Interpretation of the 2 models showed that 1) Income was positively related to completed family size but has no effect on desire for additional children; 2) female education, female employment, and cost of fertility control had no effect in either model; 3) Age at marriage had a positive effect on completed family size but none on desire for additional children; 4) Urban women had more live births, but rural women were more likely to want additional children; 5) Sex preference for boys is intense in Bangladesh. The study concludes that: 1) Economic well-being effects fertility; 2) The more adequate couples consider their income, the more likely they are to want more children; 3) Female education and employment have no effect on either completed family size or desire for more children; 4) There are no clear effects of family planning programs on either; and 5) desire for more children decreases as the number of children, particularly sons, increases.

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

Heterogeneous impact of the "Seguro Popular" program on the utilization of obstetrical services in Mexico, 2001-2006: a multinomial probit model with a discrete endogenous variable.

PubMed

Sosa-Rubí, Sandra G; Galárraga, Omar; Harris, Jeffrey E

2009-01-01

We evaluated the impact of Seguro Popular (SP), a program introduced in 2001 in Mexico primarily to finance health care for the poor. We focused on the effect of household enrollment in SP on pregnant women's access to obstetrical services, an important outcome measure of both maternal and infant health. We relied upon data from the cross-sectional 2006 National Health and Nutrition Survey (ENSANUT) in Mexico. We analyzed the responses of 3890 women who delivered babies during 2001-2006 and whose households lacked employer-based health care coverage. We formulated a multinomial probit model that distinguished between three mutually exclusive sites for delivering a baby: a health unit specifically accredited by SP; a non-SP-accredited clinic run by the Department of Health (Secretaría de Salud, or SSA); and private obstetrical care. Our model accounted for the endogeneity of the household's binary decision to enroll in the SP program. Women in households that participated in the SP program had a much stronger preference for having a baby in a SP-sponsored unit rather than paying out of pocket for a private delivery. At the same time, participation in SP was associated with a stronger preference for delivering in the private sector rather than at a state-run SSA clinic. On balance, the Seguro Popular program reduced pregnant women's attendance at an SSA clinic much more than it reduced the probability of delivering a baby in the private sector. The quantitative impact of the SP program varied with the woman's education and health, as well as the assets and location (rural vs. urban) of the household. The SP program had a robust, significantly positive impact on access to obstetrical services. Our finding that women enrolled in SP switched from non-SP state-run facilities, rather than from out-of-pocket private services, is important for public policy and requires further exploration.

Substitution of Formal and Informal Home Care Service Use and Nursing Home Service Use: Health Outcomes, Decision-Making Preferences, and Implications for a Public Health Policy.

PubMed

Chen, Chia-Ching; Yamada, Tetsuji; Nakashima, Taeko; Chiu, I-Ming

2017-01-01

The purposes of this study are: (1) to empirically identify decision-making preferences of long-term health-care use, especially informal and formal home care (FHC) service use; (2) to evaluate outcomes vs. costs based on substitutability of informal and FHC service use; and (3) to investigate health outcome disparity based on substitutability. The methods of ordinary least squares, a logit model, and a bivariate probit model are used by controlling for socioeconomic, demographic, and physical/mental health factors to investigate outcomes and costs based substitutability of informal and formal health-care use. The data come from the 2013 Japanese Study of Aging and Retirement (JSTAR), which is designed by Keizai-Sangyo Kenkyu-jo, Hitotsubashi University, and the University of Tokyo. The JSTAR is a globally comparable data survey of the elderly. There exists a complement relationship between the informal home care (IHC) and community-based FHC services, and the elasticity's ranges from 0.18 to 0.22. These are reasonable results, which show that unobservable factors are positively related to IHC and community-based FHC, but negatively related to nursing home (NH) services based on our bivariate probit model. Regarding health-care outcome efficiency issue, the IHC is the best one among three types of elderly care: IHC, community-based FHC, and NH services. Health improvement/outcome of elderly with the IHC is heavier concentrated on IHC services than the elderly care services by community-based FHC and NH care services. Policy makers need to address a diversity of health outcomes and efficiency of services based on providing services to elderly through resource allocation to the different types of long-term care. A provision of partial or full compensation for elderly care at home is recommendable and a viable option to improve their quality of lives.

Reducing prenatal smoking: the role of state policies.

PubMed

Adams, E Kathleen; Markowitz, Sara; Kannan, Viji; Dietz, Patricia M; Tong, Van T; Malarcher, Ann M

2012-07-01

Maternal smoking causes adverse health outcomes for both mothers and infants and leads to excess healthcare costs at delivery and beyond. Even with substantial declines over the past decade, around 23% of women enter pregnancy as a smoker and though almost half quit during pregnancy, half or more quitters resume smoking soon after delivery. To examine the independent effects of higher cigarette taxes and prices, smokefree policies, and tobacco control spending on maternal smoking prior to, during, and after a pregnancy during a period in which states have made changes in such policies. Data from pooled cross-sections of women with live births during 2000-2005 in 29 states plus New York City (n=225,445) were merged with cigarette price data inclusive of federal, state, and local excise taxes, full or partial bans on smoking in public places, and tobacco control spending. Probit regression models using a mixed panel, state fixed effects, and time indicators were used to assess effect of policies on smoking (during 3 months before pregnancy); quitting by last 3 months of pregnancy; and having sustained quitting at the time of completing the postpartum survey. Multivariate analysis indicated that a $1.00 increase in taxes and prices increases third-trimester quits by between 4 and 5 percentage points after controlling for the other policies and covariates. Implementing a full private worksite smoking ban increases quits by the third trimester by an estimated 5 percentage points. Cumulative spending on tobacco control had no effect on pregnancy smoking rates overall. Association of tobacco control policies with maternal smoking varied by age. States can use multiple tobacco control policies to reduce maternal smoking. Combining higher taxes with smokefree policies particularly can be effective. Copyright © 2012 American Journal of Preventive Medicine. All rights reserved.

Is Exposure to Income Inequality a Public Health Concern? Lagged Effects of Income Inequality on Individual and Population Health

PubMed Central

Mellor, Jennifer M; Milyo, Jeffrey

2003-01-01

Objective To examine the health consequences of exposure to income inequality. Data Sources Secondary analysis employing data from several publicly available sources. Measures of individual health status and other individual characteristics are obtained from the March Current Population Survey (CPS). State-level income inequality is measured by the Gini coefficient based on family income, as reported by the U.S. Census Bureau and Al-Samarrie and Miller (1967). State-level mortality rates are from the Vital Statistics of the United States; other state-level characteristics are from U.S. census data as reported in the Statistical Abstract of the United States. Study Design We examine the effects of state-level income inequality lagged from 5 to 29 years on individual health by estimating probit models of poor/fair health status for samples of adults aged 25–74 in the 1995 through 1999 March CPS. We control for several individual characteristics, including educational attainment and household income, as well as regional fixed effects. We use multivariate regression to estimate the effects of income inequality lagged 10 and 20 years on state-level mortality rates for 1990, 1980, 1970, and 1960. Principal Findings Lagged income inequality is not significantly associated with individual health status after controlling for regional fixed effects. Lagged income inequality is not associated with all cause mortality, but associated with reduced mortality from cardiovascular disease and malignant neoplasms, after controlling for state fixed-effects. Conclusions In contrast to previous studies that fail to control for regional variations in health outcomes, we find little support for the contention that exposure to income inequality is detrimental to either individual or population health. PMID:12650385

How Does Gender Affect Sustainable Intensification of Cereal Production in the West African Sahel? Evidence from Burkina Faso.

PubMed

Theriault, Veronique; Smale, Melinda; Haider, Hamza

2017-04-01

Better understanding of gender differences in the adoption of agricultural intensification strategies is crucial for designing effective policies to close the gender gap while sustainably enhancing farm productivity. We examine gender differences in adoption rates, likelihood and determinants of adopting strategy sets that enhance yields, protect crops, and restore soils in the West African Sahel, based on analysis of cereal production in Burkina Faso. Applying a multivariate probit model to a nationally representative household panel, we exploit the individual plot as unit of analysis and control for plot manager characteristics along with other covariates. Reflecting the socio-cultural context of farming combined with the economic attributes of inputs, we find that female managers of individual cereal fields are less likely than their male counterparts to adopt yield-enhancing and soil-restoring strategies, although no differential is apparent for yield-protecting strategies. More broadly, gender-disaggregated regressions demonstrate that adoption determinants differ by gender. Plot manager characteristics, including age, marital status, and access to credit or extension services do influence adoption decisions. Furthermore, household resources influence the probability of adopting intensification strategy sets differently by gender of the plot manager. Variables expressing the availability of household labor strongly influence the adoption of soil-restoring strategies by female plot managers. By contrast, household resources such as extent of livestock owned, value of non-farm income, and area planted to cotton affect the adoption choices of male plot managers. Rectifying the male bias in extension services along with improving access to credit, income, and equipment to female plot managers could contribute to sustainable agricultural intensification.

Risk Score Algorithm for Treatment of Persistent Apical Periodontitis

PubMed Central

Yu, V.S.; Khin, L.W.; Hsu, C.S.; Yee, R.; Messer, H.H.

2014-01-01

Persistent apical periodontitis related to a nonvital tooth that does not resolve following root canal treatment may be compatible with health and may not require further intervention. This research aimed to develop a Deterioration Risk Score (DRS) to differentiate lesions requiring further intervention from lesions likely to be compatible with health. In this cross-sectional study, patient records (2003-2008) were screened for root-filled teeth with periapical radiolucency visible on periapical radiographs taken at treatment and at recruitment at least 4 yr later. The final sample consisted of 228 lesions in 182 patients. Potential demographic and treatment risk factors were screened against 3 categorical outcomes (improved/unchanged/deteriorated), and a multivariate independent multinomial probit regression model was built. A 5-level DRS was constructed by summing values of adjusted regression coefficients in the model, based on predicted probabilities of deterioration. Most lesions (127, 55.7%) had improved over time, while 32 (14.0%) remained unchanged, and 69 (30.3%) had deteriorated. Significant predictors of deterioration were as follows: time since treatment (relative risk [RR]: 1.11, 95% confidence interval [CI]: 1.01-1.22, p = .030, rounded beta value = 1, for every year increase after 4 yr), current pain (RR: 3.79, 95% CI: 1.48-9.70, p = .005, rounded beta value = 13), sinus tract present (RR: 4.13, 95% CI: 1.11-15.29, p = .034, rounded beta value = 14), and lesion size (RR: 7.20, 95% CI: 3.70-14.02, p < .001, rounded beta value = 20). Persistent apical periodontitis with DRS <15 represented very low risk; 15-20, low risk; 21-30, moderate risk; 31-40, high risk; and >40, very high risk. DRS could help the clinician identify persistent apical periodontitis at low risk for deterioration, and it would not require intervention. When validated, this tool could reduce the risk of overtreatment and contribute toward targeted care and better efficiency in the timely management of disease. PMID:25190267

Poverty and childhood undernutrition in developing countries: a multi-national cohort study.

PubMed

Petrou, Stavros; Kupek, Emil

2010-10-01

The importance of reducing childhood undernutrition has been enshrined in the United Nations' Millennium Development Goals. This study explores the relationship between alternative indicators of poverty and childhood undernutrition in developing countries within the context of a multi-national cohort study (Young Lives). Approximately 2000 children in each of four countries - Ethiopia, India (Andhra Pradesh), Peru and Vietnam - had their heights measured and were weighed when they were aged between 6 and 17 months (survey one) and again between 4.5 and 5.5 years (survey two). The anthropometric outcomes of stunted, underweight and wasted were calculated using World Health Organization 2006 reference standards. Maximum-likelihood probit estimation was employed to model the relationship within each country and survey between alternative measures of living standards (principally a wealth index developed using principal components analysis) and each anthropometric outcome. An extensive set of covariates was incorporated into the models to remove as much individual heterogeneity as possible. The fully adjusted models revealed a negative and statistically significant coefficient on wealth for all outcomes in all countries, with the exception of the outcome of wasted in India (Andhra Pradesh) and Vietnam (survey one) and the outcome of underweight in Vietnam (surveys one and two). In survey one, the partial effects of wealth on the probabilities of stunting, being underweight and wasting was to reduce them by between 1.4 and 5.1 percentage points, 1.0 and 6.4 percentage points, and 0.3 and 4.5 percentage points, respectively, with each unit (10%) increase in wealth. The partial effects of wealth on the probabilities of anthropometric outcomes were larger in the survey two models. In both surveys, children residing in the lowest wealth quintile households had significantly increased probabilities of being stunted in all four study countries and of being underweight in Ethiopia, India (Andhra Pradesh) and Peru in comparison to children residing in the highest wealth quintile households. Random effects probit models confirmed the statistical significance of increased wealth in reducing the probability of being stunted and underweight across all four study countries. We conclude that, although multi-faceted, childhood undernutrition in developing countries is strongly rooted in poverty.

Corruption, inequality and population perception of healthcare quality in Europe

PubMed Central

2013-01-01

Background Evaluating the quality of healthcare and patient safety using general population questionnaires is important from research and policy perspective. Using a special wave of the Eurobarometer survey, we analysed the general population’s perception of health care quality and patient safety in a cross-country setting. Methods We used ordered probit, ordinary least squares and probit analysis to estimate the determinants of health care quality, and ordered logit analysis to analyse the likelihood of being harmed by a specific medical procedure. The models used population weights as well as country-clustered standard errors. Results We found robust evidence for the impact of socio-demographic variables on the perception of quality of health care. More specifically, we found a non-linear impact of age on the perception of quality of health care and patient safety, as well as a negative impact of poverty on both perception of quality and patient safety. We also found robust evidence that countries with higher corruption levels were associated with worse perceptions of quality of health care. Finally, we found evidence that income inequality affects patients’ perception vis-à-vis safety, thus feeding into the poverty/health care quality nexus. Conclusions Socio-demographic factors and two macro variables (corruption and income inequality) explain the perception of quality of health care and likelihood of being harmed by adverse events. The results carry significant policy weight and could explain why targeting only the health care sector (without an overall reform of the public sector) could potentially be challenging. PMID:24215401

The H-ARS Dose Response Relationship (DRR): Validation and Variables.

PubMed

Plett, P Artur; Sampson, Carol H; Chua, Hui Lin; Jackson, William; Vemula, Sasidhar; Sellamuthu, Rajendran; Fisher, Alexa; Feng, Hailin; Wu, Tong; MacVittie, Thomas J; Orschell, Christie M

2015-11-01

Manipulations of lethally-irradiated animals, such as for administration of pharmaceuticals, blood sampling, or other laboratory procedures, have the potential to induce stress effects that may negatively affect morbidity and mortality. To investigate this in a murine model of the hematopoietic acute radiation syndrome, 20 individual survival efficacy studies were grouped based on the severity of the administration (Admn) schedules of their medical countermeasure (MCM) into Admn 1 (no injections), Admn 2 (1-3 injections), or Admn 3 (29 injections or 6-9 oral gavages). Radiation doses ranged from LD30/30 to LD95/30. Thirty-day survival of vehicle controls in each group was used to construct radiation dose lethality response relationship (DRR) probit plots, which were compared statistically to the original DRR from which all LDXX/30 for the studies were obtained. The slope of the Admn 3 probit was found to be significantly steeper (5.190) than that of the original DRR (2.842) or Admn 2 (2.009), which were not significantly different. The LD50/30 for Admn 3 (8.43 Gy) was less than that of the original DRR (8.53 Gy, p < 0.050), whereas the LD50/30 of other groups were similar. Kaplan-Meier survival curves showed significantly worse survival of Admn 3 mice compared to the three other groups (p = 0.007). Taken together, these results show that stressful administration schedules of MCM can negatively impact survival and that dosing regimens should be considered when constructing DRR to use in survival studies.

Corruption, inequality and population perception of healthcare quality in Europe.

PubMed

Nikoloski, Zlatko; Mossialos, Elias

2013-11-11

Evaluating the quality of healthcare and patient safety using general population questionnaires is important from research and policy perspective. Using a special wave of the Eurobarometer survey, we analysed the general population's perception of health care quality and patient safety in a cross-country setting. We used ordered probit, ordinary least squares and probit analysis to estimate the determinants of health care quality, and ordered logit analysis to analyse the likelihood of being harmed by a specific medical procedure. The models used population weights as well as country-clustered standard errors. We found robust evidence for the impact of socio-demographic variables on the perception of quality of health care. More specifically, we found a non-linear impact of age on the perception of quality of health care and patient safety, as well as a negative impact of poverty on both perception of quality and patient safety. We also found robust evidence that countries with higher corruption levels were associated with worse perceptions of quality of health care. Finally, we found evidence that income inequality affects patients' perception vis-à-vis safety, thus feeding into the poverty/health care quality nexus. Socio-demographic factors and two macro variables (corruption and income inequality) explain the perception of quality of health care and likelihood of being harmed by adverse events. The results carry significant policy weight and could explain why targeting only the health care sector (without an overall reform of the public sector) could potentially be challenging.

Extending health insurance in Ghana: effects of the National Health Insurance Scheme on maternity care.

PubMed

Brugiavini, Agar; Pace, Noemi

2016-12-01

There is considerable interest in exploring the potential of social health insurance in Africa where a number of countries are currently experimenting with different approaches. Since these schemes have been introduced recently and are continuously evolving, it is important to evaluate their effectiveness in the enhancement of health care utilization and reduction of out-of-pocket expenses for potential policy suggestions. To investigate how the National Health Insurance Schemes (NHIS) in Ghana affects the utilization of maternal health care services and medical out-of-pocket expenses. We used nationally-representative household data from the Ghana Demographic and Health Survey (GDHS). We analyzed the 2014 GDHS focusing on four outcome variables, i.e. antenatal check up, delivery in a health facility, delivery assisted by a trained person and out-of-pocket expenditure. We estimated probit and bivariate probit models to take into account the issue of self selection into the health insurance schemes. The results suggest that, also taking into account the issue of self selection into the health insurance schemes, the NHIS enrollment positively affects the probability of formal antenatal check-ups before delivery, the probability of delivery in an institution and the probability of being assisted during delivery by a trained person. On the contrary, we find that, once the issue of self-selection is taken into account, the NHIS enrollment does not have a significant effect on out-of-pocket expenditure at the extensive margin. Since a greater utilization of health-care services has a strong positive effect on the current and future health status of women and their children, the health-care authorities in Ghana should make every effort to extend this coverage. In particular, since the results of the first step of the bivariate probit regressions suggest that the educational attainment of women is a strong determinant of enrollment, and those with low education and unable to read are less likely to enroll, information on the NHIS should be disseminated in ways that reach those with little or no education. Moreover, the availability of government health facilities in a region is associated with higher likelihood of enrollment in the NHIS. Accordingly, extending geographical access is an important strategy for expanding NHIS membership and improving access to health-care.

Catch-up growth in stunted children: Definitions and predictors

PubMed Central

2017-01-01

This paper examines the incidence and correlates of linear growth catch up in early childhood among stunted children, using a range of definitions of catch up. Catch-up growth between two and five years of age is defined in both absolute terms (i.e. the centimetre height deficit from the healthy reference population mean is reduced) and relative terms (the height-for-age z-score improved or passed the -2SD or -1SD cut-off points). Data from a cohort study from urban South Africa are used to estimate the percentage of children who caught up and the predictors of catch-up growth according to these varying definitions. The results show that our sample of stunted children exhibits catch-up growth regardless of the definition used, however prevalence of catch up is highly sensitive to the way catch up is classified, ranging from 19%-93%. Of the biological, early growth, socioeconomic status and maternal reproductive variables included in the multivariate probit regressions, only a few were found to be consistent predictors of the incidence of catch-up growth. Mother’s height was positively correlated with the incidence of catch-up growth and early stunting at one year was associated with a lower likelihood of subsequent catch up. PMID:29236728

Catch-up growth in stunted children: Definitions and predictors.

PubMed

Desmond, Chris; Casale, Daniela

2017-01-01

This paper examines the incidence and correlates of linear growth catch up in early childhood among stunted children, using a range of definitions of catch up. Catch-up growth between two and five years of age is defined in both absolute terms (i.e. the centimetre height deficit from the healthy reference population mean is reduced) and relative terms (the height-for-age z-score improved or passed the -2SD or -1SD cut-off points). Data from a cohort study from urban South Africa are used to estimate the percentage of children who caught up and the predictors of catch-up growth according to these varying definitions. The results show that our sample of stunted children exhibits catch-up growth regardless of the definition used, however prevalence of catch up is highly sensitive to the way catch up is classified, ranging from 19%-93%. Of the biological, early growth, socioeconomic status and maternal reproductive variables included in the multivariate probit regressions, only a few were found to be consistent predictors of the incidence of catch-up growth. Mother's height was positively correlated with the incidence of catch-up growth and early stunting at one year was associated with a lower likelihood of subsequent catch up.

Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models

PubMed Central

Baldwin, Scott A.; Imel, Zac E.; Braithwaite, Scott R.; Atkins, David C.

2014-01-01

Objective Multilevel models have become a standard data analysis approach in intervention research. Although the vast majority of intervention studies involve multiple outcome measures, few studies use multivariate analysis methods. The authors discuss multivariate extensions to the multilevel model that can be used by psychotherapy researchers. Method and Results Using simulated longitudinal treatment data, the authors show how multivariate models extend common univariate growth models and how the multivariate model can be used to examine multivariate hypotheses involving fixed effects (e.g., does the size of the treatment effect differ across outcomes?) and random effects (e.g., is change in one outcome related to change in the other?). An online supplemental appendix provides annotated computer code and simulated example data for implementing a multivariate model. Conclusions Multivariate multilevel models are flexible, powerful models that can enhance clinical research. PMID:24491071

Selfie Aging Index: An Index for the Self-assessment of Healthy and Active Aging.

PubMed

Gonçalves, Judite; Gomes, Maria Isabel; Fonseca, Miguel; Teodoro, Tomás; Barros, Pedro Pita; Botelho, Maria-Amália

2017-01-01

Governments across Europe want to promote healthy and active aging, as a matter of both public health and economic sustainability. Designing policies focused on the most vulnerable groups requires information at the individual level. However, a measure of healthy and active aging at the individual level does not yet exist. This paper develops the Selfie Aging Index (SAI), an individual-level index of healthy and active aging. The SAI is developed thinking about a tool that would allow each person to take a selfie of her aging status. Therefore, it is based entirely on self-assessed indicators. This paper also illustrates how the SAI may look like in practice. The SAI is based on the Biopsychosocial Assessment Model (MAB), a tool for the multidimensional assessment of older adults along three domains: biological, psychological, and social. Indicators are selected and their weights determined based on an ordered probit model that relates the MAB indicators to self-assessed health, which proxies healthy and active aging. The ordered probit model predicts the SAI based on the estimated parameters. Finally, predictions are rescaled to the 0-1 interval. Data for the SAI development come from the Study of the Aging Profiles of the Portuguese Population and the Survey of Health, Aging, and Retirement in Europe. The selected indicators are BMI, having difficulties moving around indoors and performing the activities of daily living, feeling depressed, feeling nervous, lacking energy, time awareness score, marital status, having someone to confide in, education, type of job, exercise, and smoking status. The model also determines their weights. Results shed light on various factors that contribute significantly to healthy and active aging. Two examples are mental health and exercise, which deserve more attention from individuals themselves, health-care professionals, and public health policy. The SAI has the potential to put the individual at the center of the healthy and active aging discussion, contribute to patient empowerment, and promote patient-centered care. It can become a useful instrument to monitor healthy and active aging for different actors, including individuals themselves, health-care professionals, and policy makers.

Physical activity and healthy diet: determinants and implicit relationship.

PubMed

Tavares, Aida Isabel

2014-06-01

People who decide to lose weight by dieting often do so without participating in any associated physical activity. Although some people who participate in sports are unconcerned about their diet, it is generally believed that people who exercise tend to eat a healthy diet and those who do not exercise eat a less healthy diet. There is no clear relationship between the decisions regarding participation in physical activity and eating a healthy diet when choices are taken freely and not influenced by policy factors promoting healthy behaviour. However, these decisions may reveal some common explanatory factors and an implicit link. As such the aim of this study was to identify the common explanatory factors and investigate the existence of an implicit relationship. Econometric estimate - bivariate probit estimation. Using data from the Portuguese National Health Survey, a bivariate probit was undertaken for decisions regarding participation in physical activity and eating a healthy diet. The correlation between the residuals gives information on the implicit relationship between the healthy choices. Common explanatory factors were found between the decisions to eat healthy snacks and participate in physical activity, such as being married. However, holding voluntary private health insurance, smoking, getting older, living alone and unemployment were found to dissuade people from making healthy choices. Positive correlation was found between the residuals of the probit estimations, indicating that other unmeasurable variables have a similar influence on both decisions, such as peer pressure, cultural values, fashion, advertising and risk aversion. Further research is needed to improve understanding of decision making related to participation in physical activity and eating a healthy diet. This will facilitate the design of policies that will make a greater contribution to healthy lifestyles. Copyright © 2014 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials.

PubMed

Chen, Yong; Luo, Sheng; Chu, Haitao; Wei, Peng

2013-05-01

Multivariate meta-analysis is useful in combining evidence from independent studies which involve several comparisons among groups based on a single outcome. For binary outcomes, the commonly used statistical models for multivariate meta-analysis are multivariate generalized linear mixed effects models which assume risks, after some transformation, follow a multivariate normal distribution with possible correlations. In this article, we consider an alternative model for multivariate meta-analysis where the risks are modeled by the multivariate beta distribution proposed by Sarmanov (1966). This model have several attractive features compared to the conventional multivariate generalized linear mixed effects models, including simplicity of likelihood function, no need to specify a link function, and has a closed-form expression of distribution functions for study-specific risk differences. We investigate the finite sample performance of this model by simulation studies and illustrate its use with an application to multivariate meta-analysis of adverse events of tricyclic antidepressants treatment in clinical trials.

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

PubMed

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.

Health insurance and use of alternative medicine in Mexico

PubMed Central

van Gameren, Edwin

2014-01-01

Objectives I analyze the effect of coverage by health insurance on the use of alternative medicine such as folk healers and homeopaths, in particular if it complements or substitutes conventional services. Methods Panel data from the Mexican Health and Aging Study (MHAS) is used to estimate bivariate probit models in order to explain the use of alternative medicine while allowing the determinant of interest, access to health insurance, to be an endogenous factor. Results The findings indicate that households with insurance coverage less often use alternative medicine, and that the effect is much stronger among poor than among rich households. Conclusions Poor households substitute away from traditional medicine towards conventional medicine. PMID:20546965

Timing of Puberty in Overweight Versus Obese Boys.

PubMed

Lee, Joyce M; Wasserman, Richard; Kaciroti, Niko; Gebremariam, Achamyeleh; Steffes, Jennifer; Dowshen, Steven; Harris, Donna; Serwint, Janet; Abney, Dianna; Smitherman, Lynn; Reiter, Edward; Herman-Giddens, Marcia E

2016-02-01

Studies of the relationship of weight status with timing of puberty in boys have been mixed. This study examined whether overweight and obesity are associated with differences in the timing of puberty in US boys. We reanalyzed recent community-based pubertal data from the American Academy of Pediatrics' Pediatric Research in Office Settings study in which trained clinicians assessed boys 6 to 16 years for height, weight, Tanner stages, testicular volume (TV), and other pubertal variables. We classified children based on BMI as normal weight, overweight, or obese and compared median age at a given Tanner stage or greater by weight class using probit and ordinal probit models and a Bayesian approach. Half of boys (49.9%, n = 1931) were white, 25.8% (n = 1000) were African American, and 24.3% (n = 941) were Hispanic. For genital development in white and African American boys across a variety of Tanner stages, we found earlier puberty in overweight compared with normal weight boys, and later puberty in obese compared with overweight, but no significant differences for Hispanics. For TV (≥3 mL or ≥4 mL), our findings support earlier puberty for overweight compared with normal weight white boys. In a large, racially diverse, community-based sample of US boys, we found evidence of earlier puberty for overweight compared with normal or obese, and later puberty for obese boys compared with normal and overweight boys. Additional studies are needed to understand the possible relationships among race/ethnicity, gender, BMI, and the timing of pubertal development. Copyright © 2016 by the American Academy of Pediatrics.

Multilevel Effects of Wealth on Women's Contraceptive Use in Mozambique

PubMed Central

Dias, José G.; de Oliveira, Isabel Tiago

2015-01-01

Objective This paper analyzes the impact of wealth on the use of contraception in Mozambique unmixing the contextual effects due to community wealth from the individual effects associated with the women's situation within the community of residence. Methods Data from the 2011 Mozambican Demographic and Health Survey on women who are married or living together are analyzed for the entire country and also for the rural and urban areas separately. We used single level and multilevel probit regression models. Findings A single level probit regression reveals that region, religion, age, previous fertility, education, and wealth impact contraceptive behavior. The multilevel analysis shows that average community wealth and the women’s relative socioeconomic position within the community have significant positive effects on the use of modern contraceptives. The multilevel framework proved to be necessary in rural settings but not relevant in urban areas. Moreover, the contextual effects due to community wealth are greater in rural than in urban areas and this feature is associated with the higher socioeconomic heterogeneity within the richest communities. Conclusion This analysis highlights the need for the studies on contraceptive behavior to specifically address the individual and contextual effects arising from the poverty-wealth dimension in rural and urban areas separately. The inclusion in a particular community of residence is not relevant in urban areas, but it is an important feature in rural areas. Although the women's individual position within the community of residence has a similar effect on contraceptive adoption in rural and urban settings, the impact of community wealth is greater in rural areas and smaller in urban areas. PMID:25786228

DOE Office of Scientific and Technical Information (OSTI.GOV)

Takahashi, Ryo, E-mail: inter.takahashi@gmail.com; Todo, Yasuyuki, E-mail: yastodo@k.u-tokyo.ac.jp

In recent years, shade coffee certification programs have attracted increasing attention from forest conservation and development organizations. The certification programs could be expected to promote forest conservation by providing a premium price to shade coffee producers. However, little is known about the significance of the conservation efforts generated by certification programs. In particular, the relationship between the impact of the certification and producer characteristics has yet to be examined. The purpose of this study, which was conducted in Ethiopia, was to examine the impact of a shade coffee certification program on forest conservation and its relationship with the socioeconomic characteristicsmore » of the producers. Remote sensing data of 2005 and 2010 was used to gauge the changes in forest area. Employing a probit model, we found that a forest coffee area being certified increased the probability of forest conservation by 19.3 percentage points relative to forest coffee areas lacking certification. We also found that although economically poor producers tended to engage in forest clearing, the forest coffee certification program had a significant impact on these producers. This result suggests that the certification program significantly affects the behaviors of economically poor producers and motivates these producers to conserve the forest. -- Highlights: • We employed the probit mode to evaluate the impact of the shade coffee certification on forest conservation in Ethiopia. • We estimated how the impact of the certification varied among producers with different characteristics. • The certification increased the probability of conserving forest by 19.3 percentage points. • Certification program motivated the economically poor producers to conserve the forest.« less

Child Schooling in Ethiopia: The Role of Maternal Autonomy.

PubMed

Gebremedhin, Tesfaye Alemayehu; Mohanty, Itismita

2016-01-01

This paper examines the effects of maternal autonomy on child schooling outcomes in Ethiopia using a nationally representative Ethiopian Demographic and Health survey for 2011. The empirical strategy uses a Hurdle Negative Binomial Regression model to estimate years of schooling. An ordered probit model is also estimated to examine age grade distortion using a trichotomous dependent variable that captures three states of child schooling. The large sample size and the range of questions available in this dataset allow us to explore the influence of individual and household level social, economic and cultural factors on child schooling. The analysis finds statistically significant effects of maternal autonomy variables on child schooling in Ethiopia. The roles of maternal autonomy and other household-level factors on child schooling are important issues in Ethiopia, where health and education outcomes are poor for large segments of the population.

Rigorously testing multialternative decision field theory against random utility models.

PubMed

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.

A new multivariate zero-adjusted Poisson model with applications to biomedicine.

PubMed

Liu, Yin; Tian, Guo-Liang; Tang, Man-Lai; Yuen, Kam Chuen

2018-05-25

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, ) cannot be used to fit multivariate count data with excess zero-vectors; (ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases; (iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Implications of Middle School Behavior Problems for High School Graduation and Employment Outcomes of Young Adults: Estimation of a Recursive Model.

PubMed

Karakus, Mustafa C; Salkever, David S; Slade, Eric P; Ialongo, Nicholas; Stuart, Elizabeth

2012-01-01

The potentially serious adverse impacts of behavior problems during adolescence on employment outcomes in adulthood provide a key economic rationale for early intervention programs. However, the extent to which lower educational attainment accounts for the total impact of adolescent behavior problems on later employment remains unclear As an initial step in exploring this issue, we specify and estimate a recursive bivariate probit model that 1) relates middle school behavior problems to high school graduation and 2) models later employment in young adulthood as a function of these behavior problems and of high school graduation. Our model thus allows for both a direct effect of behavior problems on later employment as well as an indirect effect that operates via graduation from high school. Our empirical results, based on analysis of data from the NELS, suggest that the direct effects of externalizing behavior problems on later employment are not significant but that these problems have important indirect effects operating through high school graduation.

A comparison of three random effects approaches to analyze repeated bounded outcome scores with an application in a stroke revalidation study.

PubMed

Molas, Marek; Lesaffre, Emmanuel

2008-12-30

Discrete bounded outcome scores (BOS), i.e. discrete measurements that are restricted on a finite interval, often occur in practice. Examples are compliance measures, quality of life measures, etc. In this paper we examine three related random effects approaches to analyze longitudinal studies with a BOS as response: (1) a linear mixed effects (LM) model applied to a logistic transformed modified BOS; (2) a model assuming that the discrete BOS is a coarsened version of a latent random variable, which after a logistic-normal transformation, satisfies an LM model; and (3) a random effects probit model. We consider also the extension whereby the variability of the BOS is allowed to depend on covariates. The methods are contrasted using a simulation study and on a longitudinal project, which documents stroke rehabilitation in four European countries using measures of motor and functional recovery. Copyright 2008 John Wiley & Sons, Ltd.

Divorce as Risky Behavior

PubMed Central

LIGHT, AUDREY; AHN, TAEHYUN

2010-01-01

Given that divorce often represents a high-stakes income gamble, we ask how individual levels of risk tolerance affect the decision to divorce. We extend the orthodox divorce model by assuming that individuals are risk averse, that marriage is risky, and that divorce is even riskier. The model predicts that conditional on the expected gains to marriage and divorce, the probability of divorce increases with relative risk tolerance because risk averse individuals require compensation for the additional risk that is inherent in divorce. To implement the model empirically, we use data for first-married women and men from the 1979 National Longitudinal Survey of Youth to estimate a probit model of divorce in which a measure of risk tolerance is among the covariates. The estimates reveal that a 1-point increase in risk tolerance raises the predicted probability of divorce by 4.3% for a representative man and by 11.4% for a representative woman. These findings are consistent with the notion that divorce entails a greater income gamble for women than for men. PMID:21308563

Divorce as risky behavior.

PubMed

Light, Audrey; Ahn, Taehyun

2010-11-01

Given that divorce often represents a high-stakes income gamble, we ask how individual levels of risk tolerance affect the decision to divorce. We extend the orthodox divorce model by assuming that individuals are risk averse, that marriage is risky, and that divorce is even riskier. The model predicts that conditional on the expected gains to marriage and divorce, the probability of divorce increases with relative risk tolerance because risk averse individuals require compensation for the additional risk that is inherent in divorce. To implement the model empirically, we use data for first-married women and men from the 1979 National Longitudinal Survey of Youth to estimate a probit model of divorce in which a measure of risk tolerance is among the covariates. The estimates reveal that a 1-point increase in risk tolerance raises the predicted probability of divorce by 4.3% for a representative man and by 11.4% for a representative woman. These findings are consistent with the notion that divorce entails a greater income gamble for women than for men.

A Heckman selection model for the safety analysis of signalized intersections

PubMed Central

Wong, S. C.; Zhu, Feng; Pei, Xin; Huang, Helai; Liu, Youjun

2017-01-01

Purpose The objective of this paper is to provide a new method for estimating crash rate and severity simultaneously. Methods 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. Results The results of the proposed two-step Heckman selection model illustrate the necessity of different crash rates for different crash severity levels. Conclusions 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. PMID:28732050

A spatial panel ordered-response model with application to the analysis of urban land-use development intensity patterns

NASA Astrophysics Data System (ADS)

Ferdous, Nazneen; Bhat, Chandra R.

2013-01-01

This paper proposes and estimates a spatial panel ordered-response probit model with temporal autoregressive error terms to analyze changes in urban land development intensity levels over time. Such a model structure maintains a close linkage between the land owner's decision (unobserved to the analyst) and the land development intensity level (observed by the analyst) and accommodates spatial interactions between land owners that lead to spatial spillover effects. In addition, the model structure incorporates spatial heterogeneity as well as spatial heteroscedasticity. The resulting model is estimated using a composite marginal likelihood (CML) approach that does not require any simulation machinery and that can be applied to data sets of any size. A simulation exercise indicates that the CML approach recovers the model parameters very well, even in the presence of high spatial and temporal dependence. In addition, the simulation results demonstrate that ignoring spatial dependency and spatial heterogeneity when both are actually present will lead to bias in parameter estimation. A demonstration exercise applies the proposed model to examine urban land development intensity levels using parcel-level data from Austin, Texas.

Statistical considerations in the analysis of data from replicated bioassays

USDA-ARS?s Scientific Manuscript database

Multiple-dose bioassay is generally the preferred method for characterizing virulence of insect pathogens. Linear regression of probit mortality on log dose enables estimation of LD50/LC50 and slope, the latter having substantial effect on LD90/95s (doses of considerable interest in pest management)...

Multivariate Strategies in Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

Investigating College and Graduate Students' Multivariable Reasoning in Computational Modeling

ERIC Educational Resources Information Center

Wu, Hsin-Kai; Wu, Pai-Hsing; Zhang, Wen-Xin; Hsu, Ying-Shao

2013-01-01

Drawing upon the literature in computational modeling, multivariable reasoning, and causal attribution, this study aims at characterizing multivariable reasoning practices in computational modeling and revealing the nature of understanding about multivariable causality. We recruited two freshmen, two sophomores, two juniors, two seniors, four…

Heterogeneous Impact of the “Seguro Popular” Program on the Utilization of Obstetrical Services in Mexico, 2001–2006: A Multinomial Probit Model with a Discrete Endogenous Variable

PubMed Central

Sosa-Rubi, Sandra G.; Galárraga, Omar

2009-01-01

Objective We evaluated the impact of Seguro Popular (SP), a program introduced in 2001 in Mexico primarily to finance health care for the poor. We focused on the effect of household enrollment in SP on pregnant women’s access to obstetrical services, an important outcome measure of both maternal and infant health. Data We relied upon data from the cross-sectional 2006 National Health and Nutrition Survey (ENSANUT) in Mexico. We analyzed the responses of 3,890 women who delivered babies during 2001–2006 and whose households lacked employer-based health care coverage. Methods We formulated a multinomial probit model that distinguished between three mutually exclusive sites for delivering a baby: a health unit specifically accredited by SP; a non-SP-accredited clinic run by the Department of Health (Secretaría de Salud, or SSA); and private obstetrical care. Our model accounted for the endogeneity of the household’s binary decision to enroll in the SP program. Results Women in households that participated in the SP program had a much stronger preference for having a baby in a SP-sponsored unit rather than paying out of pocket for a private delivery. At the same time, participation in SP was associated with a stronger preference for delivering in the private sector rather than at a state-run SSA clinic. On balance, the Seguro Popular program reduced pregnant women’s attendance at an SSA clinic much more than it reduced the probability of delivering a baby in the private sector. The quantitative impact of the SP program varied with the woman’s education and health, as well as the assets and location (rural versus urban) of the household. Conclusions The SP program had a robust, significantly positive impact on access to obstetrical services. Our finding that women enrolled in SP switched from non-SP state-run facilities, rather than from out-of-pocket private services, is important for public policy and requires further exploration. PMID:18824268

A Multivariate Model for the Study of Parental Acceptance-Rejection and Child Abuse.

ERIC Educational Resources Information Center

Rohner, Ronald P.; Rohner, Evelyn C.

This paper proposes a multivariate strategy for the study of parental acceptance-rejection and child abuse and describes a research study on parental rejection and child abuse which illustrates the advantages of using a multivariate, (rather than a simple-model) approach. The multivariate model is a combination of three simple models used to study…

Psychological and behavioral interventions to reduce HIV risk: evidence from a randomized control trial among orphaned and vulnerable adolescents in South Africa

PubMed Central

Thurman, T. R.; Kidman, R.; Carton, T. W.; Chiroro, P.

2016-01-01

ABSTRACT Evidence-based approaches are needed to address the high levels of sexual risk behavior and associated HIV infection among orphaned and vulnerable adolescents. This study recruited adolescents from a support program for HIV-affected families and randomly assigned them by cluster to receive one of the following: (1) a structured group-based behavioral health intervention; (2) interpersonal psychotherapy group sessions; (3) both interventions; or (4) no new interventions. With 95% retention, 1014 adolescents were interviewed three times over a 22-month period. Intent-to-treat analyses, applying multivariate difference-in-difference probit regressions, were performed separately for boys and girls to assess intervention impacts on sexual risk behaviors. Exposure to a single intervention did not impact behaviors. Exposure to both interventions was associated with risk-reduction behaviors, but the outcomes varied by gender: boys reported fewer risky sexual partnerships (β = −.48, p = .05) and girls reported more consistent condom (β = 1.37, p = .02). There was no difference in the likelihood of sexual debut for either gender. Providing both psychological and behavioral interventions resulted in long-term changes in sexual behavior that were not present when either intervention was provided in isolation. Multifaceted approaches for reducing sexual risk behaviors among vulnerable adolescents hold significant promise for mitigating the HIV epidemic among this priority population. PMID:26886261

Internal and external scope in willingness-to-pay estimates for threatened and endangered wildlife

USGS Publications Warehouse

Giraud, K.L.; Loomis, J.B.; Johnson, R.L.

1999-01-01

Economic theory suggests willingness-to-pay (WTP) should be significantly higher for a comprehensive good than for a subset of that good. We tested this using both a split sample design (external scope test) and paired responses (internal scope test) for WTP for several endangered fish and wildlife species in the US. In the paired response case we corrected for correlation of willingness-to-pay responses using a bivariate probit model. Surprisingly, the independent split samples passed the scope test but the paired samples did not. As the results contradict each other, questions of validity for policy implications are raised. However, using either approach, the benefit of maintaining critical habitat for these species exceeds the costs.

Factors affecting Taiwanese smokers' identification of smuggled cigarettes.

PubMed

Hsieh, Chi-Jung; Cheng, Chun-Hao; Lee, Jie-Min

2015-05-01

To analyze whether the perception that smuggled cigarettes are a greater health risk than legal cigarettes affects Taiwanese smokers' intention to distinguish smuggled cigarettes from legal cigarettes. The study used the Recursive Bivariate Probit Model to analyze data from a survey conducted in 2013 of 450 smokers of smuggled cigarettes. The study found that when smokers believe they are more likely to get lung cancer from consuming smuggled cigarettes than they are from consuming legal cigarettes, the probability of the intention to identify smuggled cigarettes increased by 42.46%. The government should strengthen educational policies and programs that teach consumers about the health risks of smoking in general and the even greater health risks of smoking smuggled cigarettes in particular.

Contractual conditions, working conditions and their impact on health and well-being.

PubMed

Robone, Silvana; Jones, Andrew M; Rice, Nigel

2011-10-01

Given changes in the labour market in past decades, it is of interest to evaluate whether and how contractual and working conditions affect health and psychological well-being in society today. We consider the effects of contractual and working conditions on self-assessed health and psychological well-being using twelve waves (1991/1992-2002/2003) of the British Household Panel Survey. For self-assessed health, the dependent variable is categorical, and we estimate non-linear dynamic panel ordered probit models, while for psychological well-being, we estimate a dynamic linear specification. The results show that both contractual and working conditions have an influence on health and psychological well-being and that the impact is different for men and women.

Acceptability of GM foods among Pakistani consumers.

PubMed

Ali, Akhter; Rahut, Dil Bahadur; Imtiaz, Muhammad

2016-04-02

In Pakistan majority of the consumers do not have information about genetically modified (GM) foods. In developing countries particularly in Pakistan few studies have focused on consumers' acceptability about GM foods. Using comprehensive primary dataset collected from 320 consumers in 2013 from Pakistan, this study analyzes the determinants of consumers' acceptability of GM foods. The data was analyzed by employing the bivariate probit model and censored least absolute deviation (CLAD) models. The empirical results indicated that urban consumers are more aware of GM foods compared to rural consumers. The acceptance of GM foods was more among females' consumers as compared to male consumers. In addition, the older consumers were more willing to accept GM food compared to young consumers. The acceptability of GM foods was also higher among wealthier households. Low price is the key factor leading to the acceptability of GM foods. The acceptability of the GM foods also reduces the risks among Pakistani consumers.

Child Schooling in Ethiopia: The Role of Maternal Autonomy

PubMed Central

Mohanty, Itismita

2016-01-01

This paper examines the effects of maternal autonomy on child schooling outcomes in Ethiopia using a nationally representative Ethiopian Demographic and Health survey for 2011. The empirical strategy uses a Hurdle Negative Binomial Regression model to estimate years of schooling. An ordered probit model is also estimated to examine age grade distortion using a trichotomous dependent variable that captures three states of child schooling. The large sample size and the range of questions available in this dataset allow us to explore the influence of individual and household level social, economic and cultural factors on child schooling. The analysis finds statistically significant effects of maternal autonomy variables on child schooling in Ethiopia. The roles of maternal autonomy and other household-level factors on child schooling are important issues in Ethiopia, where health and education outcomes are poor for large segments of the population. PMID:27942039

Acceptability of GM foods among Pakistani consumers

PubMed Central

Ali, Akhter; Rahut, Dil Bahadur; Imtiaz, Muhammad

2016-01-01

ABSTRACT In Pakistan majority of the consumers do not have information about genetically modified (GM) foods. In developing countries particularly in Pakistan few studies have focused on consumers' acceptability about GM foods. Using comprehensive primary dataset collected from 320 consumers in 2013 from Pakistan, this study analyzes the determinants of consumers' acceptability of GM foods. The data was analyzed by employing the bivariate probit model and censored least absolute deviation (CLAD) models. The empirical results indicated that urban consumers are more aware of GM foods compared to rural consumers. The acceptance of GM foods was more among females' consumers as compared to male consumers. In addition, the older consumers were more willing to accept GM food compared to young consumers. The acceptability of GM foods was also higher among wealthier households. Low price is the key factor leading to the acceptability of GM foods. The acceptability of the GM foods also reduces the risks among Pakistani consumers. PMID:27494790

Extensions to Multivariate Space Time Mixture Modeling of Small Area Cancer Data.

PubMed

Carroll, Rachel; Lawson, Andrew B; Faes, Christel; Kirby, Russell S; Aregay, Mehreteab; Watjou, Kevin

2017-05-09

Oral cavity and pharynx cancer, even when considered together, is a fairly rare disease. Implementation of multivariate modeling with lung and bronchus cancer, as well as melanoma cancer of the skin, could lead to better inference for oral cavity and pharynx cancer. The multivariate structure of these models is accomplished via the use of shared random effects, as well as other multivariate prior distributions. The results in this paper indicate that care should be taken when executing these types of models, and that multivariate mixture models may not always be the ideal option, depending on the data of interest.

Wrong-way driving crashes: A random-parameters ordered probit analysis of injury severity.

PubMed

Jalayer, Mohammad; Shabanpour, Ramin; Pour-Rouholamin, Mahdi; Golshani, Nima; Zhou, Huaguo

2018-04-23

In the context of traffic safety, whenever a motorized road user moves against the proper flow of vehicle movement on physically divided highways or access ramps, this is referred to as wrong-way driving (WWD). WWD is notorious for its severity rather than frequency. Based on data from the U.S. National Highway Traffic Safety Administration, an average of 355 deaths occur in the U.S. each year due to WWD. This total translates to 1.34 fatalities per fatal WWD crashes, whereas the same rate for other crash types is 1.10. Given these sobering statistics, WWD crashes, and specifically their severity, must be meticulously analyzed using the appropriate tools to develop sound and effective countermeasures. The objectives of this study were to use a random-parameters ordered probit model to determine the features that best describe WWD crashes and to evaluate the severity of injuries in WWD crashes. This approach takes into account unobserved effects that may be associated with roadway, environmental, vehicle, crash, and driver characteristics. To that end and given the rareness of WWD events, 15 years of crash data from the states of Alabama and Illinois were obtained and compiled. Based on this data, a series of contributing factors including responsible driver characteristics, temporal variables, vehicle characteristics, and crash variables are determined, and their impacts on the severity of injuries are explored. An elasticity analysis was also performed to accurately quantify the effect of significant variables on injury severity outcomes. According to the obtained results, factors such as driver age, driver condition, roadway surface conditions, and lighting conditions significantly contribute to the injury severity of WWD crashes. Copyright © 2018 Elsevier Ltd. All rights reserved.

Bus-based park-and-ride system: a stochastic model on multimodal network with congestion pricing schemes

NASA Astrophysics Data System (ADS)

Liu, Zhiyuan; Meng, Qiang

2014-05-01

This paper focuses on modelling the network flow equilibrium problem on a multimodal transport network with bus-based park-and-ride (P&R) system and congestion pricing charges. The multimodal network has three travel modes: auto mode, transit mode and P&R mode. A continuously distributed value-of-time is assumed to convert toll charges and transit fares to time unit, and the users' route choice behaviour is assumed to follow the probit-based stochastic user equilibrium principle with elastic demand. These two assumptions have caused randomness to the users' generalised travel times on the multimodal network. A comprehensive network framework is first defined for the flow equilibrium problem with consideration of interactions between auto flows and transit (bus) flows. Then, a fixed-point model with unique solution is proposed for the equilibrium flows, which can be solved by a convergent cost averaging method. Finally, the proposed methodology is tested by a network example.

Assessing the Impact of Drug Use on Hospital Costs

PubMed Central

Stuart, Bruce C; Doshi, Jalpa A; Terza, Joseph V

2009-01-01

Objective To assess whether outpatient prescription drug utilization produces offsets in the cost of hospitalization for Medicare beneficiaries. Data Sources/Study Setting The study analyzed a sample (N=3,101) of community-dwelling fee-for-service U.S. Medicare beneficiaries drawn from the 1999 and 2000 Medicare Current Beneficiary Surveys. Study Design Using a two-part model specification, we regressed any hospital admission (part 1: probit) and hospital spending by those with one or more admissions (part 2: nonlinear least squares regression) on drug use in a standard model with strong covariate controls and a residual inclusion instrumental variable (IV) model using an exogenous measure of drug coverage as the instrument. Principal Findings The covariate control model predicted that each additional prescription drug used (mean=30) raised hospital spending by $16 (p<.001). The residual inclusion IV model prediction was that each additional prescription fill reduced hospital spending by $104 (p<.001). Conclusions The findings indicate that drug use is associated with cost offsets in hospitalization among Medicare beneficiaries, once omitted variable bias is corrected using an IV technique appropriate for nonlinear applications. PMID:18783453

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dias, Felipe F.; Lavieri, Patrícia S.; Garikapati, Venu M.

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 aremore » 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.« less

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

NASA Astrophysics Data System (ADS)

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.

Factors Affecting Smoking Tendency and Smoking Intensity

ERIC Educational Resources Information Center

David, Nissim Ben; Zion, Uri Ben

2009-01-01

Purpose: The purpose of this paper is to measure the relative effect of relevant explanatory variable on smoking tendency and smoking intensity. Design/methodology/approach: Using survey data collected by the Israeli Bureau of Statistics in 2003-2004, a probit procedure is estimated for analyzing factors that affect the probability of being a…

Child Labour and Child Schooling in Rural Ethiopia: Nature and Trade-Off

ERIC Educational Resources Information Center

Haile, Getinet; Haile, Beliyou

2012-01-01

We examine work participation and schooling for children aged 7-15 using survey data from rural Ethiopia. Bivariate probit and age-adjusted educational attainment equations have been estimated. Male children are found to be more likely to attend school than their female counterparts. "Specialization" in child labour is also found, with…

Competencies for Young European Higher Education Graduates: Labor Market Mismatches and Their Payoffs

ERIC Educational Resources Information Center

Garcia-Aracil, Adela; Van der Velden, Rolf

2008-01-01

Labor market rewards based on competencies are analyzed using a sample of young European higher education (HE) graduates. Estimates of monetary rewards are obtained from conventional earnings regressions, while estimates total rewards are based on job satisfaction and derived through ordered probit regressions. Results for income show that jobs…

Conjoint analysis of nature tourism values in Bahia, Brazil

Treesearch

Thomas Holmes; Chris Zinkhan; Keith Alger; D. Evan Mercer

1996-01-01

This paper uses conjoint analysis to estimate the value of nature tourism attributes in a threatened forest ecosystem in northeastern Brazil. Computerized interviews were conducted using a paired comparison design. An ordinal interpretation of the rating scale was used and marginal utilities were estimated using ordered probit. The empirical results showed that the...

Bioassay of the Nucleopolyhedrosis Virus of Neodiprion sertifer (Hymenoptera: Diprionidae)

Treesearch

M.A. Mohamed; J.D. Podgwaite

1982-01-01

Linear regression analysis of probit mortality versus several concentrations of nucleopolyhedrosis virus of Neodiprion sertifer resulted in the equation Y = 2.170 + 0.872X. An LC50 was calculated at 1758 PIB/ml. Also, the incubation time of the virus was dependent on its concentration. Most insect viruses possess the potential...

Calcium spiking activity and baseline calcium levels in ROS 17/2.8 cells exposed to extremely low frequency electromagnetic fields (ELF EMF).

PubMed

Shahidain, R; Mullins, R D; Sisken, J E

2001-02-01

To determine whether extremely low frequency electromagnetic fields can alter average free cytosolic calcium ion concentrations [Ca2+]i and transient increases in [Ca2+]i in populations of ROS 17/2.8 cells. Cells loaded with the calcium-selective luminescent photoprotein, aequorin, were placed in the bottom of a sample chamber, which was inserted into the gap of a previously described air gap reactor system where they were exposed either to sinusoidal magnetic fields at a variety of frequencies and flux densities or to sham conditions. Real-time recordings of photon counts due to aequorin luminescence were obtained and data were analysed with the use of probit plots. Probit plots of data obtained from cells exposed to the various magnetic fields were virtually superimposable over the data obtained for the same cultures during pre- and post-exposure sham or no-field periods. These experiments provided no evidence for any effects of ELF EMF, either positive or negative, on either average [Ca2+]i or on transient increases in [Ca2+]i.

A Cross-sectional Examination of What Smokers Perceive to be Important and Their Willingness to Pay for Tobacco Cessation Medications.

PubMed

Dube, Shanta R; Pesko, Michael F; Xu, Xin

2016-01-01

Tobacco smoking is the leading cause of preventable morbidity and mortality in the United States, and smoking cessation has multiple health benefits. The purpose of this study was to assess cigarette smokers' perceived importance toward characteristics of tobacco cessation medications using a willingness-to-pay approach. Cross-sectional analysis of data from the 2008 HealthStyles survey, a mail-based probability sample of 5399 adults aged 18 years and older.Point estimates and 95% confidence intervals were calculated overall and by sociodemographic and smoking behavior characteristics. Multivariate Probit regression analysis was used to evaluate smokers' willingness to pay in relation to perceived importance of 3 cessation medication characteristics: convenience of use, over-the-counter availability, and efficiency to help quit. All models controlled for sociodemographic characteristics, smoking behavior characteristics, and US regional fixed effects. A total of 914 current cigarette smokers. Interest in quitting, interest in using cessation medications, and willingness to pay for 6 types of cessation medications. Approximately 68.4% of current cigarette smokers were interested in quitting. Among these individuals, 45.6% indicated that they were interested in using cessation medications, and of these, 47.3% indicated that they were willing to pay $150 or more out-of-pocket for these medications. Convenience of use and the effectiveness of these medications to help quit were positively associated with current smokers' willingness to pay for $300 or more (P < .05); however, no association was observed for over-the-counter availability. Self-reported exposure to telephone quitline advertisements was also positively associated with the willingness to pay. Approximately 68% of current smokers are interested in quitting, and about half of those smokers interested in quitting are also interested in using cessation medications. Convenience of use and the medication's effectiveness are important characteristics of cessation medication for smokers with quit intentions. Understanding preferences for these cessation medication characteristics may help inform smoking cessation efforts.

Perception of e-cigarette harm and its correlation with use among U.S. adolescents.

PubMed

Amrock, Stephen M; Zakhar, Joseph; Zhou, Sherry; Weitzman, Michael

2015-03-01

U.S. adolescents increasingly use e-cigarettes. The perceived harm of e-cigarettes has not been described, nor has the correlation between harm perception and e-cigarette use been assessed. This study examines correlates of e-cigarette harm perception and use of e-cigarettes in a national survey. We used cross-sectional nationally representative data from the 2012 National Youth Tobacco Survey (n = 24,658). Cross-tabulations and multivariate ordered probit and logistic regression models were employed to assess relative harm perception and e-cigarette use. Half of U.S. adolescents had heard of e-cigarettes. Of these, 13.2% (95% confidence interval [CI] = 11.7-14.9) and 4.0% (95% CI = 3.4-4.7) reported ever or currently using e-cigarettes, respectively. Of those aware of e-cigarettes, 34.2% (95% CI = 32.8-35.6) believed e-cigarettes were less harmful than cigarettes. Among those trying e-cigarettes, 71.8% (95% CI = 69.0-74.5) believed e-cigarettes were comparatively less harmful. Females and those ≥ 17 years old were more likely to perceive e-cigarettes as more harmful relative to cigarettes, while on average Whites, users of other tobacco products, and those with family members who used tobacco were more likely to perceive e-cigarettes as comparatively safer. Among cigarette-naive e-cigarette users, use of other tobacco products and perceived harm reduction by e-cigarettes were, respectively, on average associated with 1.6 and 4.1 percentage-point increases in e-cigarette use. Perception of e-cigarettes as less harmful than conventional cigarettes was associated with increased e-cigarette use, including among cigarette-naive e-cigarette users. These findings should prompt further scientific investigation and merit attention from regulators. © The Author 2014. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A multivariate time series approach to modeling and forecasting demand in the emergency department.

PubMed

Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L

2009-02-01

The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

Comparison of Multidimensional Item Response Models: Multivariate Normal Ability Distributions versus Multivariate Polytomous Ability Distributions. Research Report. ETS RR-08-45

ERIC Educational Resources Information Center

Haberman, Shelby J.; von Davier, Matthias; Lee, Yi-Hsuan

2008-01-01

Multidimensional item response models can be based on multivariate normal ability distributions or on multivariate polytomous ability distributions. For the case of simple structure in which each item corresponds to a unique dimension of the ability vector, some applications of the two-parameter logistic model to empirical data are employed to…

Implications of Middle School Behavior Problems for High School Graduation and Employment Outcomes of Young Adults: Estimation of a Recursive Model

PubMed Central

Karakus, Mustafa C.; Salkever, David S.; Slade, Eric P.; Ialongo, Nicholas; Stuart, Elizabeth

2013-01-01

The potentially serious adverse impacts of behavior problems during adolescence on employment outcomes in adulthood provide a key economic rationale for early intervention programs. However, the extent to which lower educational attainment accounts for the total impact of adolescent behavior problems on later employment remains unclear As an initial step in exploring this issue, we specify and estimate a recursive bivariate probit model that 1) relates middle school behavior problems to high school graduation and 2) models later employment in young adulthood as a function of these behavior problems and of high school graduation. Our model thus allows for both a direct effect of behavior problems on later employment as well as an indirect effect that operates via graduation from high school. Our empirical results, based on analysis of data from the NELS, suggest that the direct effects of externalizing behavior problems on later employment are not significant but that these problems have important indirect effects operating through high school graduation. PMID:23576834

What Determines Basic School Attainment in Developing Countries? Evidence from Rural China

ERIC Educational Resources Information Center

Zhao, Meng; Glewwe, Paul

2010-01-01

This paper analyzes recent household survey data from Gansu, a less developed province in Northwest China, to examine school attainment in a poor rural area of China. Censored ordered probit regressions are used to estimate the determinants of years of schooling. Child nutritional status, as measured by height-for-age Z-scores, and household…

The effect of trends in forest and ownership characteristics on recreational use of private forests

Treesearch

Donald F. Dennis

1992-01-01

Probit analysis was used to estimate correlations between recreational use of private woodland and forest, owner, and surrounding community characteristics. Land held by more highly educated owners or those reared in large cities was more likely to be used for recreation, while the opposite was true for land held by older owners.

Factors Influencing Recreational Use of Private Woodland

Treesearch

Donald F. Dennis; Donald F. Dennis

1990-01-01

Probit analysis was used to estimate relationships between the probability that forest land was used for recreation and characteristics of the forest, owner, and surrounding community. Land held by owners with more formal education or those reared in large cities was more likely to be used for recreation while the opposite was true for land held by older owners....

Site occupancy of brown-headed nuthatches varies with habitat restoration and range-limit context

Treesearch

Richard A. Stanton; Frank R. Thompson; Dylan C. Kesler

2015-01-01

Knowledge about species’ responses to habitat restoration can inform subsequent management and reintroduction planning. We used repeated call-response surveys to study brown-headed nuthatch (Sitta pusilla) patch occupancy at the current limits of its apparently expanding range in an area with active habitat restoration. We fit a probit occupancy...

Religious Background and Educational Attainment: The Effects of Buddhism, Islam, and Judaism

ERIC Educational Resources Information Center

Sander, William

2010-01-01

The effects of Buddhism, Islam, and Judaism on educational attainment in the United States are examined. OLS estimates of educational attainment and Probit estimates of college attainment are undertaken. It is shown that Islam and Judaism have similar positive effects on attainment relative to Protestants and Catholics. The effect of Buddhism is…

Human Capital Background and the Educational Attainment of Second-Generation Immigrants in France

ERIC Educational Resources Information Center

Dos Santos, Manon Domingues; Wolff, Francois-Charles

2011-01-01

In this paper, we study the impact of parental human capital background on ethnic educational gaps between second-generation immigrants using a large data set conducted in France in 2003. Estimates from censored random effect ordered Probit regressions show that the skills of immigrants explain in the most part, the ethnic educational gap between…

An alternate property tax program requiring a forest management plan and scheduled harvesting

Treesearch

D.F. Dennis; P.E. Sendak

1991-01-01

Vermont's Use Value Appraisal property tax program, designed to address problems such as tax inequity and forced development caused by taxing agricultural and forest land based on speculative values, requires a forest management plan and scheduled harvests. A probit analysis of enrollment provides evidence of the program's success in attracting large parcels...

Factors Influencing the Likelihood of Overeducation: A Bivariate Probit with Sample Selection Framework

ERIC Educational Resources Information Center

Rubb, Stephen

2014-01-01

Contrary to expectations, the likelihood of overeducation is shown to be inversely related to unemployment rates when not control for selectivity. Furthermore, incidence data show that overeducation is more common among men than women and among Whites than Blacks. At issue is selectivity: employment must be selected for overeducation to occur.…

The Impact of School Socioeconomic Status on Student-Generated Teacher Ratings

ERIC Educational Resources Information Center

Agnew, Steve

2011-01-01

This paper uses ordinary least squares, logit and probit regressions, along with chi-square analysis applied to nationwide data from the New Zealand ratemyteacher website to establish if there is any correlation between student ratings of their teachers and the socioeconomic status of the school the students attend. The results show that students…

Declining Fertility and the Use of Cesarean Delivery: Evidence from a Population-Based Study in Taiwan

PubMed Central

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

2010-01-01

Objective To test the hypothesis that declining fertility would affect the number of cesarean sections (c-sections) on maternal demand, but not medically indicated c-sections. Data Sources The 1996–2004 National Health Insurance Research Database in Taiwan for all singleton deliveries. Study Design Retrospective population-based, longitudinal study. Estimation was performed using multinomial probit models. Principal Findings Results revealed that declining fertility had a significant positive effect on the probability of having a c-section on maternal request but not medically indicated c-section. Conclusions Our findings offer a precautionary note to countries experiencing a fertility decline. Policies to contain the rise of c-sections should understand the role of women's preferences, especially regarding cesarean deliveries on maternal request. PMID:20545781

Stochastic modelling of temperatures affecting the in situ performance of a solar-assisted heat pump: The multivariate approach and physical interpretation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Loveday, D.L.; Craggs, C.

Box-Jenkins-based multivariate stochastic modeling is carried out using data recorded from a domestic heating system. The system comprises an air-source heat pump sited in the roof space of a house, solar assistance being provided by the conventional tile roof acting as a radiation absorber. Multivariate models are presented which illustrate the time-dependent relationships between three air temperatures - at external ambient, at entry to, and at exit from, the heat pump evaporator. Using a deterministic modeling approach, physical interpretations are placed on the results of the multivariate technique. It is concluded that the multivariate Box-Jenkins approach is a suitable techniquemore » for building thermal analysis. Application to multivariate Box-Jenkins approach is a suitable technique for building thermal analysis. Application to multivariate model-based control is discussed, with particular reference to building energy management systems. It is further concluded that stochastic modeling of data drawn from a short monitoring period offers a means of retrofitting an advanced model-based control system in existing buildings, which could be used to optimize energy savings. An approach to system simulation is suggested.« less

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

Multivariate Longitudinal Analysis with Bivariate Correlation Test.

PubMed

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated.

Global Fund grant programmes: an analysis of evaluation scores.

PubMed

Radelet, Steven; Siddiqi, Bilal

2007-05-26

The Global Fund to Fight AIDS, Tuberculosis and Malaria evaluates programme performance after 2 years to help decide whether to continue funding. We aimed to identify the correlation between programme evaluation scores and characteristics of the programme, the health sector, and the recipient country. We obtained data on the first 140 Global Fund grants evaluated in 2006, and analysed 134 of these. We used an ordered probit multivariate analysis to link evaluation scores to different characteristics, allowing us to record the association between changes in those characteristics and the probability of a programme receiving a particular evaluation score. Programmes that had government agencies as principal recipients, had a large amount of funding, were focused on malaria, had weak initial proposals, or were evaluated by the accounting firm KPMG, scored lowest. Countries with a high number of doctors per head, high measles immunisation rates, few health-sector donors, and high disease-prevalence rates had higher evaluation scores. Poor countries, those with small government budget deficits, and those that have or have had socialist governments also received higher scores. Our results show associations, not causality, and they focus on evaluation scores rather than actual performance of the programmes. Yet they provide some early indications of characteristics that can help the Global Fund identify and monitor programmes that might be at risk. The results should not be used to influence the distribution of funding, but rather to allocate resources for oversight and risk management.

The Socioeconomic and Institutional Determinants of Participation in India’s Health Insurance Scheme for the Poor

PubMed Central

Nandi, Arindam; Ashok, Ashvin; Laxminarayan, Ramanan

2013-01-01

The Rashtriya Swasthya Bima Yojana (RSBY), which was introduced in 2008 in India, is a social health insurance scheme that aims to improve healthcare access and provide financial risk protection to the poor. In this study, we analyse the determinants of participation and enrolment in the scheme at the level of districts. We used official data on RSBY enrolment, socioeconomic data from the District Level Household Survey 2007–2008, and additional state-level information on fiscal health, political affiliation, and quality of governance. Results from multivariate probit and OLS analyses suggest that political and institutional factors are among the strongest determinants explaining the variation in participation and enrolment in RSBY. In particular, districts in state governments that are politically affiliated with the opposition or neutral parties at the centre are more likely to participate in RSBY, and have higher levels of enrolment. Districts in states with a lower quality of governance, a pre-existing state-level health insurance scheme, or with a lower level of fiscal deficit as compared to GDP, are significantly less likely to participate, or have lower enrolment rates. Among socioeconomic factors, we find some evidence of weak or imprecise targeting. Districts with a higher share of socioeconomically backward castes are less likely to participate, and their enrolment rates are also lower. Finally, districts with more non-poor households may be more likely to participate, although with lower enrolment rates. PMID:23805211

Natural gas projects in the developing world: An empirical evaluation of merits, obstacles, and risks

NASA Astrophysics Data System (ADS)

Mor, Amit

Significant amounts of natural gas have been discovered in developing countries throughout the years during the course of oil exploration. The vast majority of these resources have not been utilized. Some developing countries may benefit from a carefully planned utilization of their indigenous resources, which can either be exported or used domestically to substitute imported or exportable fuels or feedstock. Governments, potential private sector investors, and financiers have been searching for strategies to promote natural gas schemes, some of which have been in the pipeline for more than two decades. The purpose of this thesis is to identify the crucial factors determining the success or failure of launching natural gas projects in the developing world. The methodology used to evaluate these questions included: (1) establishing a representative sample of natural gas projects in developing countries that were either implemented or failed to materialize during the 1980-1995 period, (2) utilizing a Probit limited dependent variable econometric model in which the explained variable is project success or failure, and (3) choosing representing indicators to reflect the assumed factors affecting project success. The study identified two conditions for project success: (1) the economic viability of the project and (2) securing financing for the investment. The factors that explain the ability or inability of the sponsors to secure financing were: (1) the volume of investment that represented the large capital costs of gas transportation, distribution, and storage, (2) the level of foreign exchange constraint in the host country, and (3) the level of development of the country. The conditions for private sector participation in natural gas projects in developing countries were identified in the study by a Probit model in which the explained variable was private sector participation. The results showed that a critical condition for private sector participation is the financial profitability of a project. Other factors that explained private sector participation and the ability of the private-sector sponsor to secure financing for a project were: (1) the political risk associated with the project, (2) the foreign exchange constraint associated with the project, and (3) whether the project was domestic or export-oriented.

Tumor dose-volume response in image-guided adaptive brachytherapy for cervical cancer: A meta-regression analysis.

PubMed

Mazeron, Renaud; Castelnau-Marchand, Pauline; Escande, Alexandre; Rivin Del Campo, Eleonor; Maroun, Pierre; Lefkopoulos, Dimitri; Chargari, Cyrus; Haie-Meder, Christine

2016-01-01

Image-guided adaptive brachytherapy is a high precision technique that allows dose escalation and adaptation to tumor response. Two monocentric studies reported continuous dose-volume response relationships, however, burdened by large confidence intervals. The aim was to refine these estimations by performing a meta-regression analysis based on published series. Eligibility was limited to series reporting dosimetric parameters according to the Groupe Européen de Curiethérapie-European SocieTy for Radiation Oncology recommendations. The local control rates reported at 2-3 years were confronted to the mean D90 clinical target volume (CTV) in 2-Gy equivalent using the probit model. The impact of each series on the relationships was pondered according to the number of patients reported. An exhaustive literature search retrieved 13 series reporting on 1299 patients. D90 high-risk CTV ranged from 70.9 to 93.1 Gy. The probit model showed a significant correlation between the D90 and the probability of achieving local control (p < 0.0001). The D90 associated to a 90% probability of achieving local control was 81.4 Gy (78.3-83.8 Gy). The planning aim of 90 Gy corresponded to a 95.0% probability (92.8-96.3%). For the intermediate-risk CTV, less data were available, with 873 patients from eight institutions. Reported mean D90 intermediate-risk CTV ranged from 61.7 to 69.1 Gy. A significant dose-volume effect was observed (p = 0.009). The D90 of 60 Gy was associated to a 79.4% (60.2-86.0%) local control probability. Based on published data from a high number of patients, significant dose-volume effect relationships were confirmed and refined between the D90 of both CTV and the probability of achieving local control. Further studies based on individual data are required to develop nomograms including nondosimetric prognostic criteria. Copyright © 2016 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.

Joint Analysis of Preschool Attendance and School Performance in the Short and Long-Run

ERIC Educational Resources Information Center

Aguilar, Renato; Tansini, Ruben

2012-01-01

This paper aims at explaining the academic performance of a sample of children starting their first year at public schools in Montevideo, Uruguay, during 1999. We are mainly interested in the effect of pre-school education on the children's academic results. Previous probit and OLS estimations suggested that pre-school education has a positive…

Visual Determination of Industrial Color-Difference Tolerances Using Probit Analysis

DTIC Science & Technology

1991-06-01

determine the median tolerance values of 45 color-difference vectors in CIELAB color space using surface mode viewing of paint samples. Nine different...8 4. Distribution Design for Color Centers in CIELAB Color Space ............................. 13 5. CIE Recommended Color Centers...compared to a near neutral anchor color- difference stimulus. The experiment concentrated on nine color centers systematically distributed in CIELAB color

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

ERIC Educational Resources Information Center

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

2009-01-01

Because statistical analysis requires 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, many students find it difficult to learn business statistics. In this study, we use an ordered probit…

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

ERIC Educational Resources Information Center

Price, Larry R.

2012-01-01

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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.

Accounting for misclassification error in retrospective smoking data.

PubMed

Kenkel, Donald S; Lillard, Dean R; Mathios, Alan D

2004-10-01

Recent waves of major longitudinal surveys in the US and other countries include retrospective questions about the timing of smoking initiation and cessation, creating a potentially important but under-utilized source of information on smoking behavior over the life course. In this paper, we explore the extent of, consequences of, and possible solutions to misclassification errors in models of smoking participation that use data generated from retrospective reports. In our empirical work, we exploit the fact that the National Longitudinal Survey of Youth 1979 provides both contemporaneous and retrospective information about smoking status in certain years. We compare the results from four sets of models of smoking participation. The first set of results are from baseline probit models of smoking participation from contemporaneously reported information. The second set of results are from models that are identical except that the dependent variable is based on retrospective information. The last two sets of results are from models that take a parametric approach to account for a simple form of misclassification error. Our preliminary results suggest that accounting for misclassification error is important. However, the adjusted maximum likelihood estimation approach to account for misclassification does not always perform as expected. Copyright 2004 John Wiley & Sons, Ltd.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jama, M.A.

This study sought to examine energy-consumption patterns in a cross section of rural households in Kenya and to analyze how these use patterns relate to socio-economic, demographic, institutional, and energy market factors. The models specified were demands for fuelwood, charcoal, kerosene, commercial heat energy, and aggregate energy. For fuelwood, a probit analysis was utilized to determine the conditional probability of fuelwood consumption and a least-squares regression to determine quantity consumed. Ordinary regression was used to estimate demand for the other fuels. The research indicates that household incomes, family size, improved ceramic stoves, other fuels, and occupation are the most influentialmore » variables on consumption of various fuels. The quantities of fuelwood, charcoal, and kerosene consumed are not very responsive to changes in income. Aggregate energy is income-inelastic and a normal good, while woodfuel and kerosene are inferior products. The model indicates that redirection of a 10% increase in income, so that only the low-income households benefit, would cause only a small, 1% increase in fuelwood consumption.« less

Health investment decisions in response to diabetes information in older Americans.

PubMed

Slade, Alexander N

2012-05-01

Diabetes is a very common and serious chronic disease, and one of the fastest growing disease burdens in the United States. Further, health behaviors, such as exercise, smoking, drinking, as well as weight status, are instrumental to diabetes management and the reduction of its medical consequences. Nine waves of the Health and Retirement Study are used to model the role of a recent diabetes diagnosis and medication on present and subsequent weight status, exercise, drinking and smoking activity. Several non-linear dynamic population average probit models are estimated. Results suggest that compared to non-diagnosed individuals at risk for high blood sugar, diagnosed diabetics respond initially in terms of increasing exercise, losing weight, and curbing smoking and drinking behavior, but the effect diminishes after diagnosis. Evidence of recidivism is also found in these outcomes, especially weight status and physical activity, suggesting that some behavioral responses to diabetes may be short-lived. Copyright © 2012 Elsevier B.V. All rights reserved.

"Birds of a Feather" Fail Together: Exploring the Nature of Dependency in SME Defaults.

PubMed

Calabrese, Raffaella; Andreeva, Galina; Ansell, Jake

2017-08-11

This article studies the effects of incorporating the interdependence among London small business defaults into a risk analysis framework using the data just before the financial crisis. We propose an extension from standard scoring models to take into account the spatial dimensions and the demographic characteristics of small and medium-sized enterprises (SMEs), such as legal form, industry sector, and number of employees. We estimate spatial probit models using different distance matrices based only on the spatial location or on an interaction between spatial locations and demographic characteristics. We find that the interdependence or contagion component defined on spatial and demographic characteristics is significant and that it improves the ability to predict defaults of non-start-ups in London. Furthermore, including contagion effects among SMEs alters the parameter estimates of risk determinants. The approach can be extended to other risk analysis applications where spatial risk may incorporate correlation based on other aspects. © 2017 Society for Risk Analysis.

The Effect of Widowhood on Mental Health - an Analysis of Anticipation Patterns Surrounding the Death of a Spouse.

PubMed

Siflinger, Bettina

2017-12-01

This study explores the effects of widowhood on mental health by taking into account the anticipation and adaptation to the partner's death. The empirical analysis uses representative panel data from the USA that are linked to administrative death records of the National Death Index. I estimate static and dynamic specifications of the panel probit model in which unobserved heterogeneity is modeled with correlated random effects. I find strong anticipation effects of the partner's death on the probability of depression, implying that the partner's death event cannot be assumed to be exogenous in econometric models. In the absence of any anticipation effects, the partner's death has long-lasting mental health consequences, leading to a significantly slower adaptation to widowhood. The results suggest that both anticipation effects and adaptation effects can be attributed to a caregiver burden and to the cause of death. The findings of this study have important implications for designing adequate social policies for the elderly US population that alleviate the negative consequences of bereavement. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Ethnic variations in immigrant poverty exit and female employment: the missing link.

PubMed

Kaida, Lisa

2015-04-01

Despite widespread interest in poverty among recent immigrants and female immigrant employment, research on the link between the two is limited. This study evaluates the effect of recently arrived immigrant women's employment on the exit from family poverty and considers the implications for ethnic differences in poverty exit. It uses the bivariate probit model and the Fairlie decomposition technique to analyze data from the Longitudinal Survey of Immigrants to Canada (LSIC), a nationally representative survey of immigrants arriving in Canada, 2000-2001. Results show that the employment of recently arrived immigrant women makes a notable contribution to lifting families out of poverty. Moreover, the wide ethnic variations in the probability of exit from poverty between European and non-European groups are partially explained by the lower employment rates among non-European women. The results suggest that the equal earner/female breadwinner model applies to low-income recent immigrant families in general, but the male breadwinner model explains the low probability of poverty exit among select non-European groups whose female employment rates are notably low.

A New Monte Carlo Method for Estimating Marginal Likelihoods.

PubMed

Wang, Yu-Bo; Chen, Ming-Hui; Kuo, Lynn; Lewis, Paul O

2018-06-01

Evaluating the marginal likelihood in Bayesian analysis is essential for model selection. Estimators based on a single Markov chain Monte Carlo sample from the posterior distribution include the harmonic mean estimator and the inflated density ratio estimator. We propose a new class of Monte Carlo estimators based on this single Markov chain Monte Carlo sample. This class can be thought of as a generalization of the harmonic mean and inflated density ratio estimators using a partition weighted kernel (likelihood times prior). We show that our estimator is consistent and has better theoretical properties than the harmonic mean and inflated density ratio estimators. In addition, we provide guidelines on choosing optimal weights. Simulation studies were conducted to examine the empirical performance of the proposed estimator. We further demonstrate the desirable features of the proposed estimator with two real data sets: one is from a prostate cancer study using an ordinal probit regression model with latent variables; the other is for the power prior construction from two Eastern Cooperative Oncology Group phase III clinical trials using the cure rate survival model with similar objectives.

Spatial Dependence and Determinants of Dairy Farmers' Adoption of Best Management Practices for Water Protection in New Zealand

NASA Astrophysics Data System (ADS)

Yang, Wei; Sharp, Basil

2017-04-01

This paper analyses spatial dependence and determinants of the New Zealand dairy farmers' adoption of best management practices to protect water quality. A Bayesian spatial durbin probit model is used to survey data collected from farmers in the Waikato region of New Zealand. The results show that farmers located near each other exhibit similar choice behaviour, indicating the importance of farmer interactions in adoption decisions. The results also address that information acquisition is the most important determinant of farmers' adoption of best management practices. Financial problems are considered a significant barrier to adopting best management practices. Overall, the existence of distance decay effect and spatial dependence in farmers' adoption decisions highlights the importance of accounting for spatial effects in farmers' decision-making, which emerges as crucial to the formulation of sustainable agriculture policy.

Is there evidence for dual causation between malaria and socioeconomic status? Findings from rural Tanzania.

PubMed

Somi, Masha F; Butler, James R G; Vahid, Farshid; Njau, Joseph; Kachur, S Patrick; Abdulla, Salim

2007-12-01

Malaria's relationship with socioeconomic status at the macroeconomic level has been established. This is the first study to explore this relationship at the microeconomic (household) level and estimate the direction of association. Malaria prevalence was measured by parasitemia, and household socioeconomic status was measured using an asset based index. Results from an instrumental variable probit model suggest that socioeconomic status is negatively associated with malaria parasitemia. Other variables that are significantly associated with parasitemia include age of the individual, use of a mosquito net on the night before interview, the number of people living in the household, whether the household was residing at their farm home at the time of interview, household wall construction, and the region of residence. Matching estimators indicate that malaria parasitemia is associated with reduced household socioeconomic status.

Spatial Dependence and Determinants of Dairy Farmers' Adoption of Best Management Practices for Water Protection in New Zealand.

PubMed

Yang, Wei; Sharp, Basil

2017-04-01

This paper analyses spatial dependence and determinants of the New Zealand dairy farmers' adoption of best management practices to protect water quality. A Bayesian spatial durbin probit model is used to survey data collected from farmers in the Waikato region of New Zealand. The results show that farmers located near each other exhibit similar choice behaviour, indicating the importance of farmer interactions in adoption decisions. The results also address that information acquisition is the most important determinant of farmers' adoption of best management practices. Financial problems are considered a significant barrier to adopting best management practices. Overall, the existence of distance decay effect and spatial dependence in farmers' adoption decisions highlights the importance of accounting for spatial effects in farmers' decision-making, which emerges as crucial to the formulation of sustainable agriculture policy.

The impact of federal bioterrorism funding programs on local health department preparedness activities.

PubMed

Avery, George H; Zabriskie-Timmerman, Jennifer

2009-06-01

Using the 2005 National Association of County and City Health Officers Profile of Local Health Departments data set, bivariate probit and Heckman selection models were used to test the hypothesis that the level of federal funding received for bioterrorism preparedness is related to the preparedness activities undertaken by local health departments. Overall budget, leadership, and crisis experience are found to be the most important determinants of local preparedness activity, but Centers for Disease Control and Prevention preparedness funding plays a mediating role by building capacity through the hiring of one key leadership position, the emergency preparedness coordinator. Additional research is needed to determine the potential impact of these funds on other aspects of the local public health system, such as the scope of services delivered, to determine secondary effects of the program.

Review Of Implementation In Bunut Shoes Assistance Program In Order Of Micro, Small And Medium Enterprises Economic In Asahan Regency

NASA Astrophysics Data System (ADS)

Saleh Malawat, M.; Putra, M. Umar Maya

2018-03-01

This paper studies the implementation of business opportunities that can improve the revenue of Bunut Shoes Micro, Small and Medium Enterprises. Probit model with E Views 6 program was used to see how far the opportunity of variable efforts to improve the revenue such as education, training, capital assistance, technological procurement of them. The data used was the primary data by conducting a survey using questionnaires to members of them with the observation period from 2013 to 2015. The results showed that all variables of implementation did not have a business opportunity correlation to the increase in revenue and Asahan District Governments are asked to create a creative breakthrough in order to achieve optimal business revenue and cooperate with other private institutions related to increase the business income.

Multivariate Models of Parent-Late Adolescent Gender Dyads: The Importance of Parenting Processes in Predicting Adjustment

ERIC Educational Resources Information Center

McKinney, Cliff; Renk, Kimberly

2008-01-01

Although parent-adolescent interactions have been examined, relevant variables have not been integrated into a multivariate model. As a result, this study examined a multivariate model of parent-late adolescent gender dyads in an attempt to capture important predictors in late adolescents' important and unique transition to adulthood. The sample…

A multivariate model and statistical method for validating tree grade lumber yield equations

Treesearch

Donald W. Seegrist

1975-01-01

Lumber yields within lumber grades can be described by a multivariate linear model. A method for validating lumber yield prediction equations when there are several tree grades is presented. The method is based on multivariate simultaneous test procedures.

Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data

PubMed Central

Xiong, Lie; Kuan, Pei-Fen; Tian, Jianan; Keles, Sunduz; Wang, Sijian

2015-01-01

In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies. PMID:26609213

The relationship between obesity and body compositions with respect to the timing of puberty in Chongqing adolescents: a cross-sectional study.

PubMed

He, Fang; Guan, Peiyu; Liu, Qin; Crabtree, Donna; Peng, Linli; Wang, Hong

2017-08-18

It is well known that excess adiposity during childhood may influence pubertal development. However, the extent to which body compositions vary in throughout puberty in boys and girls is currently unknown. The aim of this study was to investigate whether obesity and body compositions correlate with the timing of puberty in boys and girls. By random cluster sampling, our study analyzed data from 1472 students (690 girls, 782 boys) aged 6-17 years from two schools in the Chongqing area. Data were collected by physical examination of weight, height, and skinfold thicknesses. Testicular volume was measured in boys and breast development in girls. By which we got the indicators of obesity, timing of puberty and body compositions. Probit regression analysis was used to group subjects into early puberty (>P 25 ), on-time puberty (P 25  ~ P 75 ), and delayed puberty (

 0.05). In girls, delayed puberty was negatively correlated with Obesity, percentage of body fat, fat mass and fat-free mass, and positively correlated with body density. But in boys, delayed puberty was only negatively correlated with Obesity, the relation between puberty and body compositions was not found.

'Whatever She May Study, She Can't Escape from Washing Dishes': Gender Inequity in Secondary Education--Evidence from a Longitudinal Study in India

ERIC Educational Resources Information Center

Singh, Renu; Mukherjee, Protap

2018-01-01

Using unique panel data from Young Lives study conducted in undivided Andhra Pradesh, India, this mixed-method paper analyses gender differentials in completion of secondary education. Results show biased secondary school completion rates in favor of boys. Probit regression results highlight certain variables such as mothers' education, wealth,…

Filter Paper Blood Spot Enzyme Linked Immunoassay for Adiponectin and Application in the Evaluation of Determinants of Child Insulin Sensitivity

PubMed Central

Martin, Richard M.; Patel, Rita; Oken, Emily; Thompson, Jennifer; Zinovik, Alexander; Kramer, Michael S.; Vilchuck, Konstantin; Bogdanovich, Natalia; Sergeichick, Natalia; Foo, Ying; Gusina, Nina

2013-01-01

Background Adiponectin is an adipocyte-derived hormone that acts as a marker of insulin sensitivity. Bloodspot sampling by fingerstick onto filter paper may increase the feasibility of large-scale studies of the determinants of insulin sensitivity. We first describe the validation of an enzyme-linked immunoassay (ELISA) for quantifying adiponectin from dried blood spots and then demonstrate its application in a large trial (PROBIT). Methods We quantified adiponectin from 3-mm diameter discs (≈3 µL of blood) punched from dried blood spots obtained from: i) whole blood standards (validation); and ii) PROBIT trial samples (application) in which paediatricians collected blood spots from 13,879 children aged 11.5 years from 31 sites across Belarus. We examined the distribution of bloodspot adiponectin by demographic and anthropometric factors, fasting insulin and glucose. Results In the validation study, mean intra-assay coefficients of variation (n = 162) were 15%, 13% and 10% for ‘low’ (6.78 µg/ml), ‘medium’ (18.18 µg/ml) and 'high’ (33.13 µg/ml) internal quality control (IQC) samples, respectively; the respective inter-assay values (n = 40) were 23%, 21% and 14%. The correlation coefficient between 50 paired whole bloodspot versus plasma samples, collected simultaneously, was 0.87 (95% CI: 0.78 to 0.93). Recovery of known quantities of adiponectin (between 4.5 to 36 µg/ml) was 100.3–133%. Bloodspot adiponectin was stable for at least 30 months at −80°C. In PROBIT, we successfully quantified fasting adiponectin from dried blood spots in 13,329 of 13,879 (96%) children. Mean adiponectin (standard deviation) concentrations were 17.34 µg/ml (7.54) in boys and 18.41 µg/ml (7.92) in girls and were inversely associated with body mass index, fat mass, triceps and subscapular skin-fold thickness, waist circumference, height and fasting glucose. Conclusions Bloodspot ELISA is suitable for measuring adiponectin in very small volumes of blood collected on filter paper and can be applied to large-scale studies. PMID:23936498

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis

PubMed Central

Galván-Tejada, Carlos E.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L.

2017-01-01

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions. PMID:28216571

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis.

PubMed

Galván-Tejada, Carlos E; Zanella-Calzada, Laura A; Galván-Tejada, Jorge I; Celaya-Padilla, José M; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L

2017-02-14

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.

Multivariate Longitudinal Analysis with Bivariate Correlation Test

PubMed Central

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

Multivariate spatial models of excess crash frequency at area level: case of Costa Rica.

PubMed

Aguero-Valverde, Jonathan

2013-10-01

Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data. This paper aims to extent the concept of safety performance functions to be used in areal models of crash frequency. A multivariate spatial model is used for that purpose and compared to its univariate counterpart. Full Bayes hierarchical approach is used to estimate the models of crash frequency at canton level for Costa Rica. An intrinsic multivariate conditional autoregressive model is used for modeling spatial random effects. The results show that the multivariate spatial model performs better than its univariate counterpart in terms of the penalized goodness-of-fit measure Deviance Information Criteria. Additionally, the effects of the spatial smoothing due to the multivariate spatial random effects are evident in the estimation of excess equivalent property damage only crashes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Risk factors associated with the practice of child marriage among Roma girls in Serbia.

PubMed

Hotchkiss, David R; Godha, Deepali; Gage, Anastasia J; Cappa, Claudia

2016-02-01

Relatively little research on the issue of child marriage has been conducted in European countries where the overall prevalence of child marriage is relatively low, but relatively high among marginalized ethnic sub-groups. The purpose of this study is to assess the risk factors associated with the practice of child marriage among females living in Roma settlements in Serbia and among the general population and to explore the inter-relationship between child marriage and school enrollment decisions. The study is based on data from a nationally representative household survey in Serbia conducted in 2010 - and a separate survey of households living in Roma settlements in the same year. For each survey, we estimated a bivariate probit model of risk factors associated with being currently married and currently enrolled in school based on girls 15 to 17 years of age in the nationally representative and Roma settlements samples. The practice of child marriage among the Roma was found to be most common among girls who lived in poorer households, who had less education, and who lived in rural locations. The results of the bivariate probit analysis suggest that, among girls in the general population, decisions about child marriage school attendance are inter-dependent in that common unobserved factors were found to influence both decisions. However, among girls living in Roma settlements, there is only weak evidence of simultaneous decision making. The study finds evidence of the interdependence between marriage and school enrollment decisions among the general population and, to a lesser extent, among the Roma. Further research is needed on child marriage among the Roma and other marginalized sub-groups in Europe, and should be based on panel data, combined with qualitative data, to assess the role of community-level factors and the characteristics of households where girls grow up on child marriage and education decisions.

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

PubMed

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 provide more accurate estimates of PID.JEL Classification: I10, I19, C23, C25.

Cocoa Farmers’ Compliance with Safety Precautions in Spraying Agrochemicals and Use of Personal Protective Equipment (PPE) in Cameroon

PubMed Central

2018-01-01

The inability of farmers to comply with essential precautions in the course of spraying agrochemicals remains a policy dilemma, especially in developing countries. The objectives of this paper were to assess compliance of cocoa farmers with agrochemical safety measures, analyse the factors explaining involvement of cocoa farmers in the practice of reusing agrochemical containers and wearing of personal protective equipment (PPE). Data were collected with structured questionnaires from 667 cocoa farmers from the Centre and South West regions in Cameroon. Data analyses were carried out with Probit regression and Negative Binomial regression models. The results showed that average cocoa farm sizes were 3.55 ha and 2.82 ha in South West and Centre regions, respectively, and 89.80% and 42.64% complied with manufacturers’ instructions in the use of insecticides. Eating or drinking while spraying insecticides and fungicides was reported by 4.20% and 5.10% of all farmers in the two regions, respectively. However, 37.78% and 57.57% of all farmers wore hand gloves and safety boots while spraying insecticides in the South West and Centre regions of Cameroon, respectively. In addition, 7.80% of all the farmers would wash agrochemical containers and use them at home, while 42.43% would wash and use them on their farms. Probit regression results showed that probability of reusing agrochemical containers was significantly influenced (p < 0.05) by region of residence of cocoa farmers, gender, possession of formal education and farming as primary occupation. The Negative Binomial regression results showed that the log of number PPE worn was significantly influenced (p < 0.10) by region, marital status, attainment of formal education, good health, awareness of manufacturers’ instructions, land area and contact index. It was among others concluded that efforts to train farmers on the need to be familiar with manufacturers’ instructions and use PPE would enhance their safety in the course of spraying agrochemicals. PMID:29438333

Cocoa Farmers' Compliance with Safety Precautions in Spraying Agrochemicals and Use of Personal Protective Equipment (PPE) in Cameroon.

PubMed

Oyekale, Abayomi Samuel

2018-02-13

The inability of farmers to comply with essential precautions in the course of spraying agrochemicals remains a policy dilemma, especially in developing countries. The objectives of this paper were to assess compliance of cocoa farmers with agrochemical safety measures, analyse the factors explaining involvement of cocoa farmers in the practice of reusing agrochemical containers and wearing of personal protective equipment (PPE). Data were collected with structured questionnaires from 667 cocoa farmers from the Centre and South West regions in Cameroon. Data analyses were carried out with Probit regression and Negative Binomial regression models. The results showed that average cocoa farm sizes were 3.55 ha and 2.82 ha in South West and Centre regions, respectively, and 89.80% and 42.64% complied with manufacturers' instructions in the use of insecticides. Eating or drinking while spraying insecticides and fungicides was reported by 4.20% and 5.10% of all farmers in the two regions, respectively. However, 37.78% and 57.57% of all farmers wore hand gloves and safety boots while spraying insecticides in the South West and Centre regions of Cameroon, respectively. In addition, 7.80% of all the farmers would wash agrochemical containers and use them at home, while 42.43% would wash and use them on their farms. Probit regression results showed that probability of reusing agrochemical containers was significantly influenced ( p < 0.05) by region of residence of cocoa farmers, gender, possession of formal education and farming as primary occupation. The Negative Binomial regression results showed that the log of number PPE worn was significantly influenced ( p < 0.10) by region, marital status, attainment of formal education, good health, awareness of manufacturers' instructions, land area and contact index. It was among others concluded that efforts to train farmers on the need to be familiar with manufacturers' instructions and use PPE would enhance their safety in the course of spraying agrochemicals.

Exploring unobserved household living conditions in multilevel choice modeling: An application to contraceptive adoption by Indian women.

PubMed

Dias, José G; de Oliveira, Isabel Tiago

2018-01-01

This research analyzes the effect of the poverty-wealth dimension on contraceptive adoption by Indian women when no direct measures of income/expenditures are available to use as covariates. The index-Household Living Conditions (HLC)-is based on household assets and dwelling characteristics and is computed by an item response model simultaneously with the choice model in a new single-step approach. That is, the HLC indicator is treated as a latent covariate measured by a set of items, it depends on a set of concomitant variables, and explains contraceptive choices in a probit regression. Additionally, the model accounts for complex survey design and sample weights in a multilevel framework. Regarding our case study on contraceptive adoption by Indian women, results show that women with better household living conditions tend to adopt contraception more often than their counterparts. This effect is significant after controlling other factors such as education, caste, and religion. The external validation of the indicator shows that it can also be used at aggregate levels of analysis (e.g., county or state) whenever no other indicators of household living conditions are available.

Health insurance for the poor: impact on catastrophic and out-of-pocket health expenditures in Mexico

PubMed Central

Galárraga, Omar; Salinas-Rodríguez, Aarón; Sesma-Vázquez, Sergio

2009-01-01

The goal of Seguro Popular (SP) in Mexico was to improve the financial protection of the uninsured population against excessive health expenditures. This paper estimates the impact of SP on catastrophic health expenditures (CHE), as well as out-of-pocket (OOP) health expenditures, from two different sources. First, we use the SP Impact Evaluation Survey (2005–2006), and compare the instrumental variables (IV) results with the experimental benchmark. Then, we use the same IV methods with the National Health and Nutrition Survey (ENSANUT 2006). We estimate naïve models, assuming exogeneity, and contrast them with IV models that take advantage of the specific SP implementation mechanisms for identification. The IV models estimated included two-stage least squares (2SLS), bivariate probit, and two-stage residual inclusion (2SRI) models. Instrumental variables estimates resulted in comparable estimates against the “gold standard.” Instrumental variables estimates indicate a reduction of 54% in catastrophic expenditures at the national level. SP beneficiaries also had lower expenditures on outpatient and medicine expenditures. The selection-corrected protective effect is found not only in the limited experimental dataset, but also at the national level. PMID:19756796

Health insurance for the poor: impact on catastrophic and out-of-pocket health expenditures in Mexico.

PubMed

Galárraga, Omar; Sosa-Rubí, Sandra G; Salinas-Rodríguez, Aarón; Sesma-Vázquez, Sergio

2010-10-01

The goal of Seguro Popular (SP) in Mexico was to improve the financial protection of the uninsured population against excessive health expenditures. This paper estimates the impact of SP on catastrophic health expenditures (CHE), as well as out-of-pocket (OOP) health expenditures, from two different sources. First, we use the SP Impact Evaluation Survey (2005-2006), and compare the instrumental variables (IV) results with the experimental benchmark. Then, we use the same IV methods with the National Health and Nutrition Survey (ENSANUT 2006). We estimate naïve models, assuming exogeneity, and contrast them with IV models that take advantage of the specific SP implementation mechanisms for identification. The IV models estimated included two-stage least squares (2SLS), bivariate probit, and two-stage residual inclusion (2SRI) models. Instrumental variables estimates resulted in comparable estimates against the "gold standard." Instrumental variables estimates indicate a reduction of 54% in catastrophic expenditures at the national level. SP beneficiaries also had lower expenditures on outpatient and medicine expenditures. The selection-corrected protective effect is found not only in the limited experimental dataset, but also at the national level.

Does performance of breast self-exams increase the probability of using mammography: evidence from Malaysia.

PubMed

Dunn, Richard A; Tan, Andrew; Samad, Ismail

2010-01-01

Breast self-examination (BSE) was evaluated to see if it is a significant predictor of mammography. The decisions of females above age 40 in Malaysia to test for breast cancer using BSE and mammography are jointly modeled using a bivariate probit so that unobserved attributes affecting mammography usage are also allowed to affect BSE. Data come from the Malaysia Non-Communicable Disease Surveillance-1, which was collected between September 2005 and February 2006. Having ever performed BSE is positively associated with having ever undergone mammography among Malay (adjusted OR=7.343, CI=2.686, 20.079) and Chinese (adjusted OR=3.466, CI=1.330, 9.031) females after adjusting for household income, education, marital status and residential location. Neither relationship is affected by jointly modelling the decision problem. Although the association is also positive for Indian females when mammography is modelled separately (adjusted OR=5.959, CI=1.546 - 22.970), the relationship is reversed when both decisions are modelled separately. De-emphasizing BSE in Malaysia may reduce mammography screening among a large proportion of the population. Previous work on the issue in developed countries may not apply to nations with limited resources.

Modeling employer self-insurance decisions after the Affordable Care Act.

PubMed

Cordova, Amado; Eibner, Christine; Vardavas, Raffaele; Broyles, James; Girosi, Federico

2013-04-01

To present a microsimulation model that addresses the methodological challenge of estimating the firm decision to self-insure. The model considers the risk that the firm bears when self-insuring and the opportunity to mitigate that risk by purchasing stop-loss insurance. The model makes use of a structural, utility maximization framework to account for numerous aspects of the firm decision, and a multinomial probit to reproduce the elasticity of the firm's demand for health insurance. Our simulations provide three important conclusions. First, they project significant increases in self-insurance rates among small firms--presumably induced by the desire to avoid ACA's rate-banding and risk adjustment regulations-only if generous stop-loss policies become widely available. Second, they show that this increase would be due to this hypothetical adoption of widespread, generous reinsurance by the market and not by passage of the ACA. Third, even with a substantial increase of self-insurance rates among small firms, they project negligible adverse selection in the exchanges, as indicated by our finding that the increase in exchange premium is less than 0.5% when assuming very generous stop-loss policies after implementation of the ACA. © Health Research and Educational Trust.

Exploring unobserved household living conditions in multilevel choice modeling: An application to contraceptive adoption by Indian women

PubMed Central

2018-01-01

This research analyzes the effect of the poverty-wealth dimension on contraceptive adoption by Indian women when no direct measures of income/expenditures are available to use as covariates. The index–Household Living Conditions (HLC)–is based on household assets and dwelling characteristics and is computed by an item response model simultaneously with the choice model in a new single-step approach. That is, the HLC indicator is treated as a latent covariate measured by a set of items, it depends on a set of concomitant variables, and explains contraceptive choices in a probit regression. Additionally, the model accounts for complex survey design and sample weights in a multilevel framework. Regarding our case study on contraceptive adoption by Indian women, results show that women with better household living conditions tend to adopt contraception more often than their counterparts. This effect is significant after controlling other factors such as education, caste, and religion. The external validation of the indicator shows that it can also be used at aggregate levels of analysis (e.g., county or state) whenever no other indicators of household living conditions are available. PMID:29385187

Modeling Employer Self-Insurance Decisions after the Affordable Care Act

PubMed Central

Cordova, Amado; Eibner, Christine; Vardavas, Raffaele; Broyles, James; Girosi, Federico

2013-01-01

Objective To present a microsimulation model that addresses the methodological challenge of estimating the firm decision to self-insure. Methodology The model considers the risk that the firm bears when self-insuring and the opportunity to mitigate that risk by purchasing stop-loss insurance. The model makes use of a structural, utility maximization framework to account for numerous aspects of the firm decision, and a multinomial probit to reproduce the elasticity of the firm's demand for health insurance. Findings and Conclusions Our simulations provide three important conclusions. First, they project significant increases in self-insurance rates among small firms--presumably induced by the desire to avoid ACA's rate-banding and risk adjustment regulations—only if generous stop-loss policies become widely available. Second, they show that this increase would be due to this hypothetical adoption of widespread, generous reinsurance by the market and not by passage of the ACA. Third, even with a substantial increase of self-insurance rates among small firms, they project negligible adverse selection in the exchanges, as indicated by our finding that the increase in exchange premium is less than 0.5% when assuming very generous stop-loss policies after implementation of the ACA. PMID:23346976

A Robust Bayesian Approach for Structural Equation Models with Missing Data

ERIC Educational Resources Information Center

Lee, Sik-Yum; Xia, Ye-Mao

2008-01-01

In this paper, normal/independent distributions, including but not limited to the multivariate t distribution, the multivariate contaminated distribution, and the multivariate slash distribution, are used to develop a robust Bayesian approach for analyzing structural equation models with complete or missing data. In the context of a nonlinear…

GENES AS INSTRUMENTS FOR STUDYING RISK BEHAVIOR EFFECTS: AN APPLICATION TO MATERNAL SMOKING AND OROFACIAL CLEFTS

PubMed Central

Jugessur, Astanand; Murray, Jeffrey C.; Moreno, Lina; Wilcox, Allen; Lie, Rolv T.

2011-01-01

This study uses instrumental variable (IV) models with genetic instruments to assess the effects of maternal smoking on the child’s risk of orofacial clefts (OFC), a common birth defect. The study uses genotypic variants in neurotransmitter and detoxification genes relateded to smoking as instruments for cigarette smoking before and during pregnancy. Conditional maximum likelihood and two-stage IV probit models are used to estimate the IV model. The data are from a population-level sample of affected and unaffected children in Norway. The selected genetic instruments generally fit the IV assumptions but may be considered “weak” in predicting cigarette smoking. We find that smoking before and during pregnancy increases OFC risk substantially under the IV model (by about 4–5 times at the sample average smoking rate). This effect is greater than that found with classical analytic models. This may be because the usual models are not able to consider self-selection into smoking based on unobserved confounders, or it may to some degree reflect limitations of the instruments. Inference based on weak-instrument robust confidence bounds is consistent with standard inference. Genetic instruments may provide a valuable approach to estimate the “causal” effects of risk behaviors with genetic-predisposing factors (such as smoking) on health and socioeconomic outcomes. PMID:22102793

Gram-Negative Bacterial Wound Infections

DTIC Science & Technology

2014-05-01

shows an effect with increasing concentration, however survival analysis does not show a significant difference between treatment groups and controls ...with 3 dead larvae in the 25 mM group compared to a single dead larva in the control group (Fig. 7). Probit analysis estimates the lethal...statistically differ- ent from that of the control group . The levels (CFU/g) of bacteria in lung tissue correlated with the survival curves. The median

Potential for hypobaric storage as a phytosanitary treatment: Mortality of Rhagoletis pomonella (Diptera: Tephritidae) in apples and effects on fruit quality

USDA-ARS?s Scientific Manuscript database

The efficacy of low-oxygen atmospheres using low pressure, referred to as hypobaric conditions, to kill egg and 3rd instar Rhagoletis pomonella (Walsh) in apples was investigated. Infested apples were exposed to 3.33 and 6.67 kPa in glass jars at 25 and 30°C for 3-120 h. Probit analyses and lethal-d...

"You Pay Your Share, We'll Pay Our Share": The College Cost Burden and the Role of Race, Income, and College Assets

ERIC Educational Resources Information Center

Elliott, William; Friedline, Terri

2013-01-01

Changes in financial aid policies raise questions about students being asked to pay too much for college and whether parents' college savings for their children helps reduce the burden on students to pay for college. Using trivariate probit analysis with predicted probabilities, in this exploratory study we find recent changes in the financial aid…

Ordinal probability effect measures for group comparisons in multinomial cumulative link models.

PubMed

Agresti, Alan; Kateri, Maria

2017-03-01

We consider simple ordinal model-based probability effect measures for comparing distributions of two groups, adjusted for explanatory variables. An "ordinal superiority" measure summarizes the probability that an observation from one distribution falls above an independent observation from the other distribution, adjusted for explanatory variables in a model. The measure applies directly to normal linear models and to a normal latent variable model for ordinal response variables. It equals Φ(β/2) for the corresponding ordinal model that applies a probit link function to cumulative multinomial probabilities, for standard normal cdf Φ and effect β that is the coefficient of the group indicator variable. For the more general latent variable model for ordinal responses that corresponds to a linear model with other possible error distributions and corresponding link functions for cumulative multinomial probabilities, the ordinal superiority measure equals exp(β)/[1+exp(β)] with the log-log link and equals approximately exp(β/2)/[1+exp(β/2)] with the logit link, where β is the group effect. Another ordinal superiority measure generalizes the difference of proportions from binary to ordinal responses. We also present related measures directly for ordinal models for the observed response that need not assume corresponding latent response models. We present confidence intervals for the measures and illustrate with an example. © 2016, The International Biometric Society.

A Comparison of Three Multivariate Models for Estimating Test Battery Reliability.

ERIC Educational Resources Information Center

Wood, Terry M.; Safrit, Margaret J.

1987-01-01

A comparison of three multivariate models (canonical reliability model, maximum generalizability model, canonical correlation model) for estimating test battery reliability indicated that the maximum generalizability model showed the least degree of bias, smallest errors in estimation, and the greatest relative efficiency across all experimental…

Application of multivariate Gaussian detection theory to known non-Gaussian probability density functions

NASA Astrophysics Data System (ADS)

Schwartz, Craig R.; Thelen, Brian J.; Kenton, Arthur C.

1995-06-01

A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The model assumes target detection algorithms and their performance models which are based on data assumed to obey multivariate Gaussian probability distribution functions (PDFs). The applicability of these algorithms and performance models can be generalized to data having non-Gaussian PDFs through the use of transforms which convert non-Gaussian data to Gaussian (or near-Gaussian) data. An example of one such transform is the Box-Cox power law transform. In practice, such a transform can be applied to non-Gaussian data prior to the introduction of a detection algorithm that is formally based on the assumption of multivariate Gaussian data. This paper presents an extension of these techniques to the case where the joint multivariate probability density function of the non-Gaussian input data is known, and where the joint estimate of the multivariate Gaussian statistics, under the Box-Cox transform, is desired. The jointly estimated multivariate Gaussian statistics can then be used to predict the performance of a target detection algorithm which has an associated Gaussian performance model.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

PubMed

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section.

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol lowering drugs

PubMed Central

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G.; Shah, Arvind K.; Lin, Jianxin

2013-01-01

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the Deviance Information Criterion (DIC) is used to select the best transformation model. Since the model is quite complex, a novel Monte Carlo Markov chain (MCMC) sampling scheme is developed to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional response consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). Since the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate since these variables are correlated with each other. A detailed analysis of these data is carried out using the proposed methodology. PMID:23580436

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs.

PubMed

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G; Shah, Arvind K; Lin, Jianxin

2013-10-15

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the deviance information criterion is used to select the best transformation model. Because the model is quite complex, we develop a novel Monte Carlo Markov chain sampling scheme to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol-lowering drugs where the goal is to jointly model the three-dimensional response consisting of low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). Because the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately; however, a multivariate approach would be more appropriate because these variables are correlated with each other. We carry out a detailed analysis of these data by using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.

Semiparametric Thurstonian Models for Recurrent Choices: A Bayesian Analysis

ERIC Educational Resources Information Center

Ansari, Asim; Iyengar, Raghuram

2006-01-01

We develop semiparametric Bayesian Thurstonian models for analyzing repeated choice decisions involving multinomial, multivariate binary or multivariate ordinal data. Our modeling framework has multiple components that together yield considerable flexibility in modeling preference utilities, cross-sectional heterogeneity and parameter-driven…

Heterogeneous Effects of a Nonlinear Price Schedule for Outpatient Care.

PubMed

Farbmacher, Helmut; Ihle, Peter; Schubert, Ingrid; Winter, Joachim; Wuppermann, Amelie

2017-10-01

Nonlinear price schedules generally have heterogeneous effects on health-care demand. We develop and apply a finite mixture bivariate probit model to analyze whether there are heterogeneous reactions to the introduction of a nonlinear price schedule in the German statutory health insurance system. In administrative insurance claims data from the largest German health insurance plan, we find that some individuals strongly react to the new price schedule while a second group of individuals does not react. Post-estimation analyses reveal that the group of the individuals who do not react to the reform includes the relatively sick. These results are in line with forward-looking behavior: Individuals who are already sick expect that they will hit the kink in the price schedule and thus are less sensitive to the co-payment. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

HIV prevalence, sociodemographic characteristics, and sexual behaviors among transwomen in Mexico City.

PubMed

Colchero, M Arantxa; Cortés-Ortiz, María Alejandra; Romero-Martínez, Martín; Vega, Hamid; González, Andrea; Román, Ricardo; Franco-Núñez, Aurora; Bautista-Arredondo, Sergio

2015-01-01

To present results from HIV testing, knowledge of HIV status and socioeconomic factors associated with the probability of having a HIV positive result among transwomen (TW) in Mexico. In 2012, we conducted an HIV seroprevalence survey to 585 TW in Mexico City in three strata: gathering places, the Condesa HIV Clinic and in four detention centers. We estimated the prevalence of HIV in each strata and applied a probit model to the overall sample to analyze factors associated with the probability of a HIV positive result. The prevalence of HIV was 19.8% in meeting places; 31.9% in detention centers and 64% among the participants of the clinic. Age, low education and number of sexual partners was positively associated with HIV. Results from the study provide relevant information to design HIV prevention interventions tailored to the needs of the TW population.

Willingness to use safety belt and levels of injury in car accidents.

PubMed

de Lapparent, Matthieu

2008-05-01

In this article, we develop a bivariate ordered Probit model to analyze the decision to fasten the safety belt in a car and the resulting severity of accidents if it happens. The approach takes into account the fact that the decision to fasten the safety belt has a direct causal effect on the category of injury if an accident happens. Our application to a sample drawn from the database of French accident reports in 2003 for three populations of car users (drivers, front passengers, rear passengers) shows that fastening the safety belt is significantly related to a decrease in severe injuries but it shows also that these car users compensate partly for this safety benefit. Furthermore, it is observed that demographic characteristics of car users, as well as transport facilities, play important roles in decisions to fasten safety belts and in the eventual resulting accident injuries.

On the reliability of self-reported health: evidence from Albanian data.

PubMed

Vaillant, Nicolas; Wolff, François-Charles

2012-06-01

This paper investigates the reliability of self-assessed measures of health using panel data collected in Albania by the World Bank in 2002, 2003 and 2004 through the Living Standard Measurement Study project. As the survey includes questions on a self-assessed measure of health and on more objective health problems, both types of information are combined with a view to understanding how respondents change their answers to the self-reported measures over time. Estimates from random effects ordered Probit models show that differences in self-reported subjective health between individuals are much more marked than those over time, suggesting a strong state dependence in subjective health status. The empirical analysis also reveals respondent consistency, from both a subjective and an objective viewpoint. Self-reported health is much more influenced by permanent shocks than by more transitory illness or injury. Copyright © 2012 Ministry of Health, Saudi Arabia. Published by Elsevier Ltd. All rights reserved.

Racial/Ethnic Disparities in Mental Health Care Utilization among U.S. College Students: Applying the Institution of Medicine Definition of Health Care Disparities.

PubMed

Hunt, Justin B; Eisenberg, Daniel; Lu, Liya; Gathright, Molly

2015-10-01

The authors apply the Institute of Medicine's definition of health care disparities to college students. The analysis pools data from the first two waves of the Healthy Minds Study, a multicampus survey of students' mental health (N = 13,028). A probit model was used for any past-year service utilization, and group differences in health status were adjusted by transforming the entire distribution for each minority population to approximate the white distribution. Disparities existed between whites and all minority groups. Compared to other approaches, the predicted service disparities were greater because this method included the effects of mediating SES variables. Health care disparities persist in the college setting despite improved access and nearly universal insurance coverage. Our findings emphasize the importance of investigating potential sources of disparities beyond geography and coverage.

Childhood Health Status and Adulthood Cardiovascular Disease Morbidity in Rural China: Are They Related?

PubMed

Wang, Qing; Shen, Jay J

2016-06-06

Cardiovascular diseases (CVDs) are among the top health problems of the Chinese population. Although mounting evidence suggests that early childhood health status has an enduring effect on late life chronic morbidity, no study so far has analyzed the issue in China. Using nationally representative data from the 2013 China Health and Retirement Longitudinal Study (CHARLS), a Probit model and Two-Stage Residual Inclusion estimation estimator were applied to analyze the relationship between childhood health status and adulthood cardiovascular disease in rural China. Good childhood health was associated with reduced risk of adult CVDs. Given the long-term effects of childhood health on adulthood health later on, health policy and programs to improve the health status and well-being of Chinese populations over the entire life cycle, especially in persons' early life, are expected to be effective and successful.

Marital status, childlessness, and social support among older Canadians.

PubMed

Penning, Margaret J; Wu, Zheng

2014-12-01

Despite evidence of increasing diversification of family structures, little is known regarding implications of marital and parental status for access to social support in later life. Using data from Statistics Canada's 2007 General Social Survey, this study assessed the impact of marital and parental status intersections on social support among adults aged 60 and older (n = 11,503). Two-stage probit regression models indicated that among those who were currently married or separated/divorced, childless individuals were more likely to report instrumental (domestic, transportation) and emotional support from people outside the household. Conversely, among never-married or widowed older adults, being childless was associated with reduced domestic support but without differences in other support domains. Findings suggest that marital and parental status intersections are not uniformly positive, neutral, or negative regarding implications for extra-household social support. Future work should address complexities of these relationships in order to better understand rapidly changing family structures.

[Factor associated with medicines utilization and expenditure in Mexico].

PubMed

Wirtz, Veronika J; Serván-Mori, Edson; Heredia-Pi, Ileana; Dreser, Anahí; Ávila-Burgos, Leticia

2013-01-01

To analyze medicine utilization and expenditure and associated factors in Mexico, as well as to discuss their implications for pharmaceutical policy. Analysis of a sample of 193,228 individuals from the Mexican National Health and Nutrition Survey 2012. Probability and amount of expenditure were estimated using logit, probit and quantile regression models, evaluating three dimensions of access to medicines: (1) likelihood of utilization of medicines in the event of a health problem, (2) probability of incurring expenses and (3) amount spent on medicines. Individuals affiliated to IMSS were more likely to use medicines (OR=1.2, p<0.05). Being affiliated to the IMSS, ISSSTE or SP reduced the likelihood of spending compared to those without health insurance (about RM 0.7, p<0.01). Median expenditures varied between 195.3 and 274.2 pesos. Factors associated with the use and expenditure on medicines indicate that inequities in the access to medicines persist.

The relationship between organizational culture and performance in acute hospitals.

PubMed

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.

Childhood Health Status and Adulthood Cardiovascular Disease Morbidity in Rural China: Are They Related?

PubMed Central

Wang, Qing; Shen, Jay J.

2016-01-01

Cardiovascular diseases (CVDs) are among the top health problems of the Chinese population. Although mounting evidence suggests that early childhood health status has an enduring effect on late life chronic morbidity, no study so far has analyzed the issue in China. Using nationally representative data from the 2013 China Health and Retirement Longitudinal Study (CHARLS), a Probit model and Two-Stage Residual Inclusion estimation estimator were applied to analyze the relationship between childhood health status and adulthood cardiovascular disease in rural China. Good childhood health was associated with reduced risk of adult CVDs. Given the long-term effects of childhood health on adulthood health later on, health policy and programs to improve the health status and well-being of Chinese populations over the entire life cycle, especially in persons’ early life, are expected to be effective and successful. PMID:27275829

The fatter are happier in Indonesia.

PubMed

Sohn, Kitae

2017-02-01

Although obesity and happiness are known to be negatively related in the developed world, little attention has been paid to this relationship in the developing world. We thus investigated the relationship in Indonesia and attempted to explain the underlying rationale. We considered about 12,000 respondents aged 15+ for each gender obtained from the Indonesian Family Life Survey 2007 by relating a measure of happiness to weight-related measures in ordered probit models. The relationship between obesity and happiness was positive in Indonesia, and this relationship was robust. Our evidence suggests that the contrasting results for the two worlds result from affordability of obesity. That is, while even low socioeconomic status (SES) individuals in the developed world can afford to be obese, only high SES individuals in the developing world can do. Our findings imply that obesity prevention in the developing world requires different measures than those used in the developed world.

UNDERSTANDING THE ASSOCIATION BETWEEN MATERNAL EDUCATION AND USE OF HEALTH SERVICES IN GHANA: EXPLORING THE ROLE OF HEALTH KNOWLEDGE

PubMed Central

GREENAWAY, EMILY SMITH; LEON, JUAN; BAKER, DAVID P.

2013-01-01

Summary This paper examines the role of health knowledge in the association between mothers’ education and use of maternal and child health services in Ghana. The study uses data from a nationally representative sample of female respondents to the 2008 Ghana Demographic and Health Survey. Ordered probit regression models evaluate whether women’s health knowledge helps to explain use of three specific maternal and child health services: antenatal care, giving birth with the supervision of a trained professional and complete child vaccination. The analyses reveal that mothers’ years of formal education are strongly associated with health knowledge; health knowledge helps explain the association between maternal education and use of health services; and, net of a set of stringent demographic and socioeconomic controls, mothers’ health knowledge is a key factor associated with use of health services. PMID:22377424

Effect of Prior Health-Related Employment on the Registered Nurse Workforce Supply.

PubMed

Yoo, Byung-kwan; Lin, Tzu-chun; Kim, Minchul; Sasaki, Tomoko; Spetz, Joanne

2016-01-01

Registered nurses (RN) who held prior health-related employment in occupations other than licensed practical or vocational nursing (LPN/LVN) are reported to have increased rapidly in the past decades. Researchers examined whether prior health-related employment affects RN workforce supply. A cross-sectional bivariate probit model using the 2008 National Sample Survey of Registered Nurses was esti- mated. Prior health-related employment in relatively lower-wage occupations, such as allied health, clerk, or nursing aide, was positively associated with working s an RN. ~>Prior health-related employ- ment in relatively higher-wage categories, such as a health care manager or LPN/LVN, was positively associated with working full-time as an RN. Policy implications are to promote an expanded career ladder program and a nursing school admission policy that targets non-RN health care workers with an interest in becoming RNs.

ENGAGEMENT IN OUTPATIENT SUBSTANCE ABUSE TREATMENT AND EMPLOYMENT OUTCOMES

PubMed Central

Dunigan, Robert; Acevedo, Andrea; Campbell, Kevin; Garnick, Deborah W.; Horgan, Constance M.; Huber, Alice; Lee, Margaret T.; Panas, Lee; Ritter, Grant A.

2013-01-01

This study, a collaboration between an academic research center and Washington State’s health, employment and correction departments, investigates the extent to which treatment engagement, a widely adopted performance measure, is associated with employment, an important outcome for individuals receiving treatment for substance use disorders. Two-stage Heckman probit regressions were conducted using 2008 administrative data for 7,570 adults receiving publicly-funded treatment. The first stage predicted employment in the year following the first treatment visit and three separate second stages models predicted number of quarters employed, wages, and hours worked. Engagement as a main effect was not significant for any of the employment outcomes. However, for clients with prior criminal justice involvement, engagement was associated with both employment and higher wages following treatment. Clients with criminal justice involvement face greater challenge regarding employment, so the identification of any actionable step which increases the likelihood of employment or wages is an important result. PMID:23686216

Exploring the link between ambulatory care and avoidable hospitalizations at the Veteran Health Administration.

PubMed

Pracht, Etienne E; Bass, Elizabeth

2011-01-01

This paper explores the link between utilization of ambulatory care and the likelihood of rehospitalization for an avoidable reason in veterans served by the Veteran Health Administration (VA). The analysis used administrative data containing healthcare utilization and patient characteristics stored at the national VA data warehouse, the Corporate Franchise Data Center. The study sample consisted of 284 veterans residing in Florida who had been hospitalized at least once for an avoidable reason. A bivariate probit model with instrumental variables was used to estimate the probability of rehospitalization. Veterans who had at least 1 ambulatory care visit per month experienced a significant reduction in the probability of rehospitalization for the same avoidable hospitalization condition. The findings suggest that ambulatory care can serve as an important substitute for more expensive hospitalization for the conditions characterized as avoidable. © 2011 National Association for Healthcare Quality.

The effect of hospital organizational characteristics on postoperative complications.

PubMed

Knight, Margaret

2013-12-01

To determine if there is a relationship between the risk of postoperative complications and the nonclinical hospital characteristics of bed size, ownership structure, relative urbanicity, regional location, teaching status, and area income status. This study involved a secondary analysis of 2006 administrative hospital data from a number of U.S. states. This data, gathered annually by the Agency for Healthcare Research and Quality (AHRQ) via the National Inpatient Sample (NIS) Healthcare Utilization Project (HCUP), was analyzed using probit regressions to measure the effects of several nonclinical hospital categories on seven diagnostic groupings. The study model included postoperative complications as well as additional potentially confounding variables. The results showed mixed outcomes for each of the hospital characteristic groupings. Subdividing these groupings to correspond with the HCUP data analysis allowed a greater understanding of how hospital characteristics' may affect postoperative outcomes. Nonclinical hospital characteristics do affect the various postoperative complications, but they do so inconsistently.

Flexible link functions in nonparametric binary regression with Gaussian process priors.

PubMed

Li, Dan; Wang, Xia; Lin, Lizhen; Dey, Dipak K

2016-09-01

In many scientific fields, it is a common practice to collect a sequence of 0-1 binary responses from a subject across time, space, or a collection of covariates. Researchers are interested in finding out how the expected binary outcome is related to covariates, and aim at better prediction in the future 0-1 outcomes. Gaussian processes have been widely used to model nonlinear systems; in particular to model the latent structure in a binary regression model allowing nonlinear functional relationship between covariates and the expectation of binary outcomes. A critical issue in modeling binary response data is the appropriate choice of link functions. Commonly adopted link functions such as probit or logit links have fixed skewness and lack the flexibility to allow the data to determine the degree of the skewness. To address this limitation, we propose a flexible binary regression model which combines a generalized extreme value link function with a Gaussian process prior on the latent structure. Bayesian computation is employed in model estimation. Posterior consistency of the resulting posterior distribution is demonstrated. The flexibility and gains of the proposed model are illustrated through detailed simulation studies and two real data examples. Empirical results show that the proposed model outperforms a set of alternative models, which only have either a Gaussian process prior on the latent regression function or a Dirichlet prior on the link function. © 2015, The International Biometric Society.

Flexible Link Functions in Nonparametric Binary Regression with Gaussian Process Priors

PubMed Central

Li, Dan; Lin, Lizhen; Dey, Dipak K.

2015-01-01

Summary In many scientific fields, it is a common practice to collect a sequence of 0-1 binary responses from a subject across time, space, or a collection of covariates. Researchers are interested in finding out how the expected binary outcome is related to covariates, and aim at better prediction in the future 0-1 outcomes. Gaussian processes have been widely used to model nonlinear systems; in particular to model the latent structure in a binary regression model allowing nonlinear functional relationship between covariates and the expectation of binary outcomes. A critical issue in modeling binary response data is the appropriate choice of link functions. Commonly adopted link functions such as probit or logit links have fixed skewness and lack the flexibility to allow the data to determine the degree of the skewness. To address this limitation, we propose a flexible binary regression model which combines a generalized extreme value link function with a Gaussian process prior on the latent structure. Bayesian computation is employed in model estimation. Posterior consistency of the resulting posterior distribution is demonstrated. The flexibility and gains of the proposed model are illustrated through detailed simulation studies and two real data examples. Empirical results show that the proposed model outperforms a set of alternative models, which only have either a Gaussian process prior on the latent regression function or a Dirichlet prior on the link function. PMID:26686333

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

Effects of Covariance Heterogeneity on Three Procedures for Analyzing Multivariate Repeated Measures Designs.

ERIC Educational Resources Information Center

Vallejo, Guillermo; Fidalgo, Angel; Fernandez, Paula

2001-01-01

Estimated empirical Type I error rate and power rate for three procedures for analyzing multivariate repeated measures designs: (1) the doubly multivariate model; (2) the Welch-James multivariate solution (H. Keselman, M. Carriere, a nd L. Lix, 1993); and (3) the multivariate version of the modified Brown-Forsythe procedure (M. Brown and A.…

Effects of preference heterogeneity among landowners on spatial conservation prioritization.

PubMed

Nielsen, Anne Sofie Elberg; Strange, Niels; Bruun, Hans Henrik; Jacobsen, Jette Bredahl

2017-06-01

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 and historical participation data to examine the likelihood of participation of Danish forest owners in a voluntary conservation program. We used the results to spatially predict the likelihood of participation of all forest owners in Denmark. We merged spatial data on the presence of forest, cadastral information on participation contracts, and individual-level socioeconomic information about the forest owners and their households. We included predicted participation in a probability model for species survival. Uninformed and informed (included land owner characteristics) models were then incorporated 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. © 2016 Society for Conservation Biology.

Visible lesion thresholds and model predictions for Q-switched 1318-nm and 1540-nm laser exposures to porcine skin

NASA Astrophysics Data System (ADS)

Zohner, Justin J.; Schuster, Kurt J.; Chavey, Lucas J.; Stolarski, David J.; Kumru, Semih S.; Rockwell, Benjamin A.; Thomas, Robert J.; Cain, Clarence P.

2006-02-01

Skin damage thresholds were measured and compared with theoretical predictions using a skin thermal model for near-IR laser pulses at 1318 nm and 1540 nm. For the 1318-nm data, a Q-switched, 50-ns pulse with a spot size of 5 mm was applied to porcine skin and the damage thresholds were determined at 1 hour and 24 hours postexposure using Probit analysis. The same analysis was conducted for a Q-switched, 30-ns pulse at 1540 nm with a spot size of 5 mm. The Yucatan mini-pig was used as the skin model for human skin due to its similarity to pigmented human skin. The ED 50 for these skin exposures at 24 hours postexposure was 10.5 J/cm2 for the 1318-nm exposures, and 6.1 J/cm2 for the 1540-nm exposures. These results were compared to thermal model predictions. We show that the thermal model fails to account for the ED 50 values observed. A brief discussion of the possible causes of this discrepancy is presented. These thresholds are also compared with previously published skin minimum visible lesion (MVL) thresholds and with the ANSI Standard's MPE for 1318-nm lasers at 50 ns and 1540-nm lasers at 30 ns.

Bayesian Analysis of High Dimensional Classification

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Subhadeep; Liang, Faming

2009-12-01

Modern data mining and bioinformatics have presented an important playground for statistical learning techniques, where the number of input variables is possibly much larger than the sample size of the training data. In supervised learning, logistic regression or probit regression can be used to model a binary output and form perceptron classification rules based on Bayesian inference. In these cases , there is a lot of interest in searching for sparse model in High Dimensional regression(/classification) setup. we first discuss two common challenges for analyzing high dimensional data. The first one is the curse of dimensionality. The complexity of many existing algorithms scale exponentially with the dimensionality of the space and by virtue of that algorithms soon become computationally intractable and therefore inapplicable in many real applications. secondly, multicollinearities among the predictors which severely slowdown the algorithm. In order to make Bayesian analysis operational in high dimension we propose a novel 'Hierarchical stochastic approximation monte carlo algorithm' (HSAMC), which overcomes the curse of dimensionality, multicollinearity of predictors in high dimension and also it possesses the self-adjusting mechanism to avoid the local minima separated by high energy barriers. Models and methods are illustrated by simulation inspired from from the feild of genomics. Numerical results indicate that HSAMC can work as a general model selection sampler in high dimensional complex model space.

On the Numerical Formulation of Parametric Linear Fractional Transformation (LFT) Uncertainty Models for Multivariate Matrix Polynomial Problems

NASA Technical Reports Server (NTRS)

Belcastro, Christine M.

1998-01-01

Robust control system analysis and design is based on an uncertainty description, called a linear fractional transformation (LFT), which separates the uncertain (or varying) part of the system from the nominal system. These models are also useful in the design of gain-scheduled control systems based on Linear Parameter Varying (LPV) methods. Low-order LFT models are difficult to form for problems involving nonlinear parameter variations. This paper presents a numerical computational method for constructing and LFT model for a given LPV model. The method is developed for multivariate polynomial problems, and uses simple matrix computations to obtain an exact low-order LFT representation of the given LPV system without the use of model reduction. Although the method is developed for multivariate polynomial problems, multivariate rational problems can also be solved using this method by reformulating the rational problem into a polynomial form.

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.

PubMed

Meeker, Daniella; Jiang, Xiaoqian; Matheny, Michael E; Farcas, Claudiu; D'Arcy, Michel; Pearlman, Laura; Nookala, Lavanya; Day, Michele E; Kim, Katherine K; Kim, Hyeoneui; Boxwala, Aziz; El-Kareh, Robert; Kuo, Grace M; Resnic, Frederic S; Kesselman, Carl; Ohno-Machado, Lucila

2015-11-01

Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

MULTIVARIATE RECEPTOR MODELS AND MODEL UNCERTAINTY. (R825173)

EPA Science Inventory

Abstract

Estimation of the number of major pollution sources, the source composition profiles, and the source contributions are the main interests in multivariate receptor modeling. Due to lack of identifiability of the receptor model, however, the estimation cannot be...

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution

PubMed Central

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section. PMID:28983398

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2012-01-01

Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I2, which we call . We also provide a multivariate H2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22763950

Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Schröter, Kai; Merz, Bruno

2016-05-01

Flood risk management increasingly relies on risk analyses, including loss modelling. Most of the flood loss models usually applied in standard practice have in common that complex damaging processes are described by simple approaches like stage-damage functions. Novel multi-variable models significantly improve loss estimation on the micro-scale and may also be advantageous for large-scale applications. However, more input parameters also reveal additional uncertainty, even more in upscaling procedures for meso-scale applications, where the parameters need to be estimated on a regional area-wide basis. To gain more knowledge about challenges associated with the up-scaling of multi-variable flood loss models the following approach is applied: Single- and multi-variable micro-scale flood loss models are up-scaled and applied on the meso-scale, namely on basis of ATKIS land-use units. Application and validation is undertaken in 19 municipalities, which were affected during the 2002 flood by the River Mulde in Saxony, Germany by comparison to official loss data provided by the Saxon Relief Bank (SAB).In the meso-scale case study based model validation, most multi-variable models show smaller errors than the uni-variable stage-damage functions. The results show the suitability of the up-scaling approach, and, in accordance with micro-scale validation studies, that multi-variable models are an improvement in flood loss modelling also on the meso-scale. However, uncertainties remain high, stressing the importance of uncertainty quantification. Thus, the development of probabilistic loss models, like BT-FLEMO used in this study, which inherently provide uncertainty information are the way forward.

An error bound for a discrete reduced order model of a linear multivariable system

NASA Technical Reports Server (NTRS)

Al-Saggaf, Ubaid M.; Franklin, Gene F.

1987-01-01

The design of feasible controllers for high dimension multivariable systems can be greatly aided by a method of model reduction. In order for the design based on the order reduction to include a guarantee of stability, it is sufficient to have a bound on the model error. Previous work has provided such a bound for continuous-time systems for algorithms based on balancing. In this note an L-infinity bound is derived for model error for a method of order reduction of discrete linear multivariable systems based on balancing.

CLUSTERING SOUTH AFRICAN HOUSEHOLDS BASED ON THEIR ASSET STATUS USING LATENT VARIABLE MODELS

PubMed Central

McParland, Damien; Gormley, Isobel Claire; McCormick, Tyler H.; Clark, Samuel J.; Kabudula, Chodziwadziwa Whiteson; Collinson, Mark A.

2014-01-01

The Agincourt Health and Demographic Surveillance System has since 2001 conducted a biannual household asset survey in order to quantify household socio-economic status (SES) in a rural population living in northeast South Africa. The survey contains binary, ordinal and nominal items. In the absence of income or expenditure data, the SES landscape in the study population is explored and described by clustering the households into homogeneous groups based on their asset status. A model-based approach to clustering the Agincourt households, based on latent variable models, is proposed. In the case of modeling binary or ordinal items, item response theory models are employed. For nominal survey items, a factor analysis model, similar in nature to a multinomial probit model, is used. Both model types have an underlying latent variable structure—this similarity is exploited and the models are combined to produce a hybrid model capable of handling mixed data types. Further, a mixture of the hybrid models is considered to provide clustering capabilities within the context of mixed binary, ordinal and nominal response data. The proposed model is termed a mixture of factor analyzers for mixed data (MFA-MD). The MFA-MD model is applied to the survey data to cluster the Agincourt households into homogeneous groups. The model is estimated within the Bayesian paradigm, using a Markov chain Monte Carlo algorithm. Intuitive groupings result, providing insight to the different socio-economic strata within the Agincourt region. PMID:25485026

Posttreatment Motivation and Alcohol Treatment Outcome 9 Months Later: Findings From Structural Equation Modeling

PubMed Central

2014-01-01

Objective: To investigate the association between posttreatment motivation to change as measured by the Readiness to Change Questionnaire Treatment Version and drinking outcomes 9 months after the conclusion of treatment for alcohol problems. Method: Data from 392 participants in the United Kingdom Alcohol Treatment Trial were used to fit structural equation models investigating relationships between motivation to change pre- and posttreatment and 5 outcomes 9 months later. The models included pathways through changes in drinking behavior during treatment and adjustment for sociodemographic information. Results: Greater posttreatment motivation (being in action vs. preaction) was associated with 3 times higher odds of the most stringent definition of positive outcome (being abstinent or entirely a nonproblem drinker) 9 months later (odds ratio = 3.10, 95% confidence interval [1.83, 5.25]). A smaller indirect effect of pretreatment motivation on this outcome was seen from pathways through drinking behavior during treatment and posttreatment motivation (probit coefficient = 0.08, 95% confidence interval [0.03, 0.14]). A similar pattern of results was seen for other outcomes evaluated. Conclusion: Posttreatment motivation to change has hitherto been little studied and is identified here as a clearly important predictor of longer term treatment outcome. PMID:25244390

Injury severity analysis of commercially-licensed drivers in single-vehicle crashes: Accounting for unobserved heterogeneity and age group differences.

PubMed

Osman, Mohamed; Mishra, Sabyasachee; Paleti, Rajesh

2018-05-18

This study analyzes the injury severity of commercially-licensed drivers involved in single-vehicle crashes. Considering the discrete ordinal nature of injury severity data, the ordered response modeling framework was adopted. The moderating effect of driver's age on all other factors was examined by segmenting the parameters by driver's age group. Additional effects of the different drivers' age groups are taken into consideration through interaction terms. Unobserved heterogeneity of the different covariates was investigated using the Mixed Generalized Ordered Response Probit (MGORP) model. The empirical analysis was conducted using four years of the Highway Safety Information System (HSIS) data that included 6247 commercially-licensed drivers involved in single-vehicle crashes in the state of Minnesota. The MGORP model elasticity effects indicate that key factors that increase the likelihood of severe crashes for commercially-licensed drivers across all age groups include: lack of seatbelt usage, collision with a fixed object, speeding, vehicle age of 11 years or more, wind, night time, weekday, and female drivers. Also, the effects of several covariates were found to vary across different age groups. Copyright © 2018 Elsevier Ltd. All rights reserved.

Family Structure and Adolescent Substance Use: An International Perspective.

PubMed

Hoffmann, John P

2017-11-10

Numerous studies indicate that family structure is a key correlate of adolescent substance use. Yet there are some important limitations to this research. Studies have been conducted mainly in the United States, with relatively few studies that have compared family structure and youth substance use across nations. There is also a lack of recognition of the complexity of family types prevalent in contemporary global society. Moreover, there remains a need to consider personal, interpersonal, and macro-level characteristics that may help account for the association between family structure and youth substance use. This study uses data from 37 countries to examine several models that purport to explain the association between family structure and substance use. The data are from the 2005-2006 WHO-sponsored Health Behaviour in School-Aged Children (HBSC) (n = 193,202). Multilevel models, including linear, probit, and structural equation models (SEMs), were used to test several hypotheses. The results suggest that time spent with friends largely accounted for the association between specific types of family structures and frequency of alcohol use and getting drunk, but that cannabis use was negatively associated with living with both biological parents irrespective of other factors.

Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI

PubMed Central

Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Treviño, Victor; Tamez-Peña, José G.

2015-01-01

In this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. The pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects. Scores were analyzed at the time of pain incidence (T-0), the year prior incidence (T-1), and two years before pain incidence (T-2). Multivariate models were created by a cross validated elastic-net regularized generalized linear models feature selection tool. Univariate differences between cases and controls were reported by AUC, C-statistics, and ODDs ratios. Univariate analysis indicated that the medial osteophytes were significantly more prevalent in cases than controls: C-stat 0.62, 0.62, and 0.61, at T-0, T-1, and T-2, respectively. The multivariate JSW models significantly predicted pain: AUC = 0.695, 0.623, and 0.620, at T-0, T-1, and T-2, respectively. Semiquantitative multivariate models predicted paint with C-stat = 0.671, 0.648, and 0.645 at T-0, T-1, and T-2, respectively. Multivariate models derived from plain X-ray radiography assessments may be used to predict subjects that are at risk of developing knee pain. PMID:26504490

Changes in Quality of Health Care Delivery after Vertical Integration.

PubMed

Carlin, Caroline S; Dowd, Bryan; Feldman, Roger

2015-08-01

To fill an empirical gap in the literature by examining changes in quality of care measures occurring when multispecialty clinic systems were acquired by hospital-owned, vertically integrated health care delivery systems in the Twin Cities area. Administrative data for health plan enrollees attributed to treatment and control clinic systems, merged with U.S. Census data. We compared changes in quality measures for health plan enrollees in the acquired clinics to enrollees in nine control groups using a differences-in-differences model. Our dataset spans 2 years prior to and 4 years after the acquisitions. We estimated probit models with errors clustered within enrollees. Data were assembled by the health plan's informatics team. Vertical integration is associated with increased rates of colorectal and cervical cancer screening and more appropriate emergency department use. The probability of ambulatory care-sensitive admissions increased when the acquisition caused disruption in admitting patterns. Moving a clinic system into a vertically integrated delivery system resulted in limited increases in quality of care indicators. Caution is warranted when the acquisition causes disruption in referral patterns. © Health Research and Educational Trust.

Preliminary Multi-Variable Parametric Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Hendrichs, Todd

2010-01-01

This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.

Multivariate Models for Normal and Binary Responses in Intervention Studies

ERIC Educational Resources Information Center

Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen

2016-01-01

Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

NASA Astrophysics Data System (ADS)

Cannon, Alex J.

2018-01-01

Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.

Partial Least Squares Calibration Modeling Towards the Multivariate Limit of Detection for Enriched Isotopic Mixtures via Laser Ablation Molecular Isotopic Spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harris, Candace; Profeta, Luisa; Akpovo, Codjo

The psuedo univariate limit of detection was calculated to compare to the multivariate interval. ompared with results from the psuedounivariate LOD, the multivariate LOD includes other factors (i.e. signal uncertainties) and the reveals the significance in creating models that not only use the analyte’s emission line but also its entire molecular spectra.

Multiple imputation for handling missing outcome data when estimating the relative risk.

PubMed

Sullivan, Thomas R; Lee, Katherine J; Ryan, Philip; Salter, Amy B

2017-09-06

Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whether misspecification of the imputation model in this setting could lead to biased parameter estimates. Using simulated data, we evaluated the performance of multiple imputation for handling missing data prior to estimating adjusted relative risks from a correctly specified multivariable log binomial model. We considered an arbitrary pattern of missing data in both outcome and exposure variables, with missing data induced under missing at random mechanisms. Focusing on standard model-based methods of multiple imputation, missing data were imputed using multivariate normal imputation or fully conditional specification with a logistic imputation model for the outcome. Multivariate normal imputation performed poorly in the simulation study, consistently producing estimates of the relative risk that were biased towards the null. Despite outperforming multivariate normal imputation, fully conditional specification also produced somewhat biased estimates, with greater bias observed for higher outcome prevalences and larger relative risks. Deleting imputed outcomes from analysis datasets did not improve the performance of fully conditional specification. Both multivariate normal imputation and fully conditional specification produced biased estimates of the relative risk, presumably since both use a misspecified imputation model. Based on simulation results, we recommend researchers use fully conditional specification rather than multivariate normal imputation and retain imputed outcomes in the analysis when estimating relative risks. However fully conditional specification is not without its shortcomings, and so further research is needed to identify optimal approaches for relative risk estimation within the multiple imputation framework.

A simplified parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, a simplified parsimonious higher-order multivariate Markov chain model (SPHOMMCM) is presented. Moreover, parameter estimation method of TPHOMMCM is give. Numerical experiments shows the effectiveness of TPHOMMCM.

Piecewise multivariate modelling of sequential metabolic profiling data.

PubMed

Rantalainen, Mattias; Cloarec, Olivier; Ebbels, Timothy M D; Lundstedt, Torbjörn; Nicholson, Jeremy K; Holmes, Elaine; Trygg, Johan

2008-02-19

Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

A tridiagonal parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a tridiagonal parsimonious higher-order multivariate Markov chain model (TPHOMMCM). Moreover, estimation method of the parameters in TPHOMMCM is give. Numerical experiments illustrate the effectiveness of TPHOMMCM.

A Gibbs sampler for Bayesian analysis of site-occupancy data

USGS Publications Warehouse

Dorazio, Robert M.; Rodriguez, Daniel Taylor

2012-01-01

1. A Bayesian analysis of site-occupancy data containing covariates of species occurrence and species detection probabilities is usually completed using Markov chain Monte Carlo methods in conjunction with software programs that can implement those methods for any statistical model, not just site-occupancy models. Although these software programs are quite flexible, considerable experience is often required to specify a model and to initialize the Markov chain so that summaries of the posterior distribution can be estimated efficiently and accurately. 2. As an alternative to these programs, we develop a Gibbs sampler for Bayesian analysis of site-occupancy data that include covariates of species occurrence and species detection probabilities. This Gibbs sampler is based on a class of site-occupancy models in which probabilities of species occurrence and detection are specified as probit-regression functions of site- and survey-specific covariate measurements. 3. To illustrate the Gibbs sampler, we analyse site-occupancy data of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly species in Switzerland. Our analysis includes a comparison of results based on Bayesian and classical (non-Bayesian) methods of inference. We also provide code (based on the R software program) for conducting Bayesian and classical analyses of site-occupancy data.

Analysis of the injury severity of crashes by considering different lighting conditions on two-lane rural roads.

PubMed

Jafari Anarkooli, A; Hadji Hosseinlou, M

2016-02-01

Many studies have examined different factors contributing to the injury severity of crashes; however, relatively few studies have focused on the crashes by considering the specific effects of lighting conditions. This research investigates lighting condition differences in the injury severity of crashes using 3-year (2009-2011) crash data of two-lane rural roads of the state of Washington. Separate ordered-probit models were developed to predict the effects of a set of factors expected to influence injury severity in three lighting conditions; daylight, dark, and dark with street lights. A series of likelihood ratio tests were conducted to determine if these lighting condition models were justified. The modeling results suggest that injury severity in specific lighting conditions are associated with contributing factors in different ways, and that such differences cannot be uncovered by focusing merely on one aggregate model. Key differences include crash location, speed limit, shoulder width, driver action, and three collision types (head-on, rear-end, and right-side impact collisions). This paper highlights the importance of deploying street lights at and near intersections (or access points) on two-lane rural roads because injury severity highly increases when crashes occur at these points in dark conditions. Copyright © 2016 Elsevier Ltd and National Safety Council. All rights reserved.

MULTIVARIATE LINEAR MIXED MODELS FOR MULTIPLE OUTCOMES. (R824757)

EPA Science Inventory

We propose a multivariate linear mixed (MLMM) for the analysis of multiple outcomes, which generalizes the latent variable model of Sammel and Ryan. The proposed model assumes a flexible correlation structure among the multiple outcomes, and allows a global test of the impact of ...

Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

NASA Astrophysics Data System (ADS)

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Legacy Status as a Signal in College Admissions

DTIC Science & Technology

2006-01-01

but no equivalent procedure for probit. 24 Hausman and McFadden (1984). This test is performed using the suest command in Stata to verify that...312. Attiyeh, Gregory and Richard Attiyeh. "Testing for Bias in Graduate School Admissions." The Journal of Human Resources. Vol. 32, No. 3 ( Summer ...John. "The Effects of Public Policies on the Demand for Higher Education." The Journal of Human Resources. Vol. 12, No. 3 ( Summer 1977), 285-307

Anti-Fungal activity of essential oil from Baeckea frutescens L against Pleuratus ostreatus

NASA Astrophysics Data System (ADS)

Jemi, Renhart; Barus, Ade Irma; Nuwa, Sarinah, Luhan, Gimson

2017-11-01

Ujung Atap is an herb that have distinctive odor on its leaves. The plant's essential oil contains bioactive compounds but has not been investigated its anti-fungal activity against Pleurotus ostreatus. Essential oil from Ujung Atap leaves is one environmentally friendly natural preservative. This study consisted of distillation Ujung Atap leaves with boiled method, determining the number of acid, essential oil ester, and anti-fungal activity against Pleurotus ostreatus. Analysis of the data to calculate anti-fungal activity used probit analysis method to determine the IC50. Results for the distillation of leaves Ujung Atap produce essential oil yield of 0.071% and the average yield of the acid number and the ester of essential oils Ujung Atap leaves are 5.24 and 12.15. Anti-fungal activity Pleurotus ostreatus at a concentration of 1000 µg/mL, 100 µg/mL, 75 µg/mL, 50 µg/mL and 100 µg/mL BA defunct or fungi was declared dead, while at a concentration of 25 µg/mL, 10 µg/mL and 5 µg/mL still occur inhibitory processes. Results obtained probit analysis method IC50 of 35.48 mg/mL; means the essential oil of Ujung Atap leaf can inhibit fungal growth by 50 percent to 35.48 µg/mL concentration.

Satisfaction and responsiveness with health-care services in Qatar--evidence from a survey.

PubMed

Ali, Faleh Mohamed Hussain; Nikoloski, Zlatko; Reka, Husein

2015-11-01

Satisfaction and responsiveness with health care are some of the main outcome variables of a health system. Although health outcomes have been studied in countries with different levels of economic development, there is limited information on the health provision/satisfaction/responsiveness nexus in countries where rapid transitions from middle to high-income status have occurred. Using a 2012 survey conducted in Qatar (amongst both Qatari and non-Qatari respondents), we analysed satisfaction and responsiveness of health care. The sample consisted of 4083 respondents. We use logit analysis [as well as robustness checks involving ordered logit, ordered probit, ordinary least squares (OLS) and probit analysis] in order to estimate the determinants of satisfaction and responsiveness. Both, satisfaction and responsiveness rates were high. Gender, nationality and, to some extent, income and age were significant sociodemographic determinants of satisfaction, with non-Qataris and females, having higher levels of satisfaction. Cost, previous experience with the same health provider and provision of medical insurance for a particular health provider were the attributes significantly correlated with general satisfaction. The results are consistent when the analysis is applied to the correlates of responsiveness. Sociodemographic factors explain the satisfaction with quality of health care in the state of Qatar (both from the general population point of view and from the patient point of view). Copyright © 2015. Published by Elsevier Ireland Ltd.

An assessment of two-step linear regression and a multifactor probit analysis as alternatives to acute to chronic ratios in the estimation of chronic response from acute toxicity data to derive water quality guidelines.

PubMed

Slaughter, Andrew R; Palmer, Carolyn G; Muller, Wilhelmine J

2007-04-01

In aquatic ecotoxicology, acute to chronic ratios (ACRs) are often used to predict chronic responses from available acute data to derive water quality guidelines, despite many problems associated with this method. This paper explores the comparative protectiveness and accuracy of predicted guideline values derived from the ACR, linear regression analysis (LRA), and multifactor probit analysis (MPA) extrapolation methods applied to acute toxicity data for aquatic macroinvertebrates. Although the authors of the LRA and MPA methods advocate the use of extrapolated lethal effects in the 0.01% to 10% lethal concentration (LC0.01-LC10) range to predict safe chronic exposure levels to toxicants, the use of an extrapolated LC50 value divided by a safety factor of 5 was in addition explored here because of higher statistical confidence surrounding the LC50 value. The LRA LC50/5 method was found to compare most favorably with available experimental chronic toxicity data and was therefore most likely to be sufficiently protective, although further validation with the use of additional species is needed. Values derived by the ACR method were the least protective. It is suggested that there is an argument for the replacement of ACRs in developing water quality guidelines by the LRA LC50/5 method.

Comparing Within-Person Effects from Multivariate Longitudinal Models

ERIC Educational Resources Information Center

Bainter, Sierra A.; Howard, Andrea L.

2016-01-01

Several multivariate models are motivated to answer similar developmental questions regarding within-person (intraindividual) effects between 2 or more constructs over time, yet the within-person effects tested by each model are distinct. In this article, the authors clarify the types of within-person inferences that can be made from each model.…

Applying the multivariate time-rescaling theorem to neural population models

PubMed Central

Gerhard, Felipe; Haslinger, Robert; Pipa, Gordon

2011-01-01

Statistical models of neural activity are integral to modern neuroscience. Recently, interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However any statistical model must be validated by an appropriate goodness-of-fit test. Kolmogorov-Smirnov tests based upon the time-rescaling theorem have proven to be useful for evaluating point-process-based statistical models of single-neuron spike trains. Here we discuss the extension of the time-rescaling theorem to the multivariate (neural population) case. We show that even in the presence of strong correlations between spike trains, models which neglect couplings between neurons can be erroneously passed by the univariate time-rescaling test. We present the multivariate version of the time-rescaling theorem, and provide a practical step-by-step procedure for applying it towards testing the sufficiency of neural population models. Using several simple analytically tractable models and also more complex simulated and real data sets, we demonstrate that important features of the population activity can only be detected using the multivariate extension of the test. PMID:21395436

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

NASA Astrophysics Data System (ADS)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

Meta-Analytic Structural Equation Modeling (MASEM): Comparison of the Multivariate Methods

ERIC Educational Resources Information Center

Zhang, Ying

2011-01-01

Meta-analytic Structural Equation Modeling (MASEM) has drawn interest from many researchers recently. In doing MASEM, researchers usually first synthesize correlation matrices across studies using meta-analysis techniques and then analyze the pooled correlation matrix using structural equation modeling techniques. Several multivariate methods of…

MULTIVARIATE RECEPTOR MODELS-CURRENT PRACTICE AND FUTURE TRENDS. (R826238)

EPA Science Inventory

Multivariate receptor models have been applied to the analysis of air quality data for sometime. However, solving the general mixture problem is important in several other fields. This paper looks at the panoply of these models with a view of identifying common challenges and ...

Regression Models For Multivariate Count Data

PubMed Central

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

2016-01-01

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. PMID:28348500

Regression Models For Multivariate Count Data.

PubMed

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.

A "Model" Multivariable Calculus Course.

ERIC Educational Resources Information Center

Beckmann, Charlene E.; Schlicker, Steven J.

1999-01-01

Describes a rich, investigative approach to multivariable calculus. Introduces a project in which students construct physical models of surfaces that represent real-life applications of their choice. The models, along with student-selected datasets, serve as vehicles to study most of the concepts of the course from both continuous and discrete…

Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace

ERIC Educational Resources Information Center

Culpepper, Steven Andrew; Park, Trevor

2017-01-01

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series

ERIC Educational Resources Information Center

Zhang, Guangjian; Chow, Sy-Miin; Ong, Anthony D.

2011-01-01

Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a…

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

A joint modeling and estimation method for multivariate longitudinal data with mixed types of responses to analyze physical activity data generated by accelerometers.

PubMed

Li, Haocheng; Zhang, Yukun; Carroll, Raymond J; Keadle, Sarah Kozey; Sampson, Joshua N; Matthews, Charles E

2017-11-10

A mixed effect model is proposed to jointly analyze multivariate longitudinal data with continuous, proportion, count, and binary responses. The association of the variables is modeled through the correlation of random effects. We use a quasi-likelihood type approximation for nonlinear variables and transform the proposed model into a multivariate linear mixed model framework for estimation and inference. Via an extension to the EM approach, an efficient algorithm is developed to fit the model. The method is applied to physical activity data, which uses a wearable accelerometer device to measure daily movement and energy expenditure information. Our approach is also evaluated by a simulation study. Copyright © 2017 John Wiley & Sons, Ltd.

Load compensation in a lean burn natural gas vehicle

NASA Astrophysics Data System (ADS)

Gangopadhyay, Anupam

A new multivariable PI tuning technique is developed in this research that is primarily developed for regulation purposes. Design guidelines are developed based on closed-loop stability. The new multivariable design is applied in a natural gas vehicle to combine idle and A/F ratio control loops. This results in better recovery during low idle operation of a vehicle under external step torques. A powertrain model of a natural gas engine is developed and validated for steady-state and transient operation. The nonlinear model has three states: engine speed, intake manifold pressure and fuel fraction in the intake manifold. The model includes the effect of fuel partial pressure in the intake manifold filling and emptying dynamics. Due to the inclusion of fuel fraction as a state, fuel flow rate into the cylinders is also accurately modeled. A linear system identification is performed on the nonlinear model. The linear model structure is predicted analytically from the nonlinear model and the coefficients of the predicted transfer function are shown to be functions of key physical parameters in the plant. Simulations of linear system and model parameter identification is shown to converge to the predicted values of the model coefficients. The multivariable controller developed in this research could be designed in an algebraic fashion once the plant model is known. It is thus possible to implement the multivariable PI design in an adaptive fashion combining the controller with identified plant model on-line. This will result in a self-tuning regulator (STR) type controller where the underlying design criteria is the multivariable tuning technique designed in this research.

Practical robustness measures in multivariable control system analysis. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Lehtomaki, N. A.

1981-01-01

The robustness of the stability of multivariable linear time invariant feedback control systems with respect to model uncertainty is considered using frequency domain criteria. Available robustness tests are unified under a common framework based on the nature and structure of model errors. These results are derived using a multivariable version of Nyquist's stability theorem in which the minimum singular value of the return difference transfer matrix is shown to be the multivariable generalization of the distance to the critical point on a single input, single output Nyquist diagram. Using the return difference transfer matrix, a very general robustness theorem is presented from which all of the robustness tests dealing with specific model errors may be derived. The robustness tests that explicitly utilized model error structure are able to guarantee feedback system stability in the face of model errors of larger magnitude than those robustness tests that do not. The robustness of linear quadratic Gaussian control systems are analyzed.

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, whereas algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons. © The Author(s) 2017. Published by Oxford University Press on behalf of the Systematic Biology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Describing the Elephant: Structure and Function in Multivariate Data.

ERIC Educational Resources Information Center

McDonald, Roderick P.

1986-01-01

There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family of models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain. (Author)

Relation between lowered colloid osmotic pressure, respiratory failure, and death.

PubMed

Tonnesen, A S; Gabel, J C; McLeavey, C A

1977-01-01

Plasma colloid osmotic pressure was measured each day in 84 intensive care unit patients. Probit analysis demonstrated a direct relationship between colloid osmotic pressure (COP) and survival. The COP associated with a 50% survival rate was 15.0 torr. COP was higher in survivors than in nonsurvivors without respiratory failure and in patients who recovered from respiratory failure. We conclude that lowered COP is associated with an elevated mortality rate. However, the relationship to death is not explained by the relationship to respiratory failure.

Guidelines for Acute Toxicological Tests

DTIC Science & Technology

1979-11-01

with a group of individuals being exposed is independence (usually assured by randoruiza- tion) of the respondent. In the case of acute studies on plants...reasonable choice. If estimates of the EC50 are available and if the purpose of the pro- posed study is to check the median response rate Finney on page...appropriate test. Figure 2 shois the probit equation and the results on scale 1/X. In the transformed scale, EC50 = 59.83 with bounds of 54.53 and 63.92

Disruption of the Putative Vascular Leak Peptide Sequence in the Stabilized Ricin Vaccine Candidate RTA1-33/44-198

DTIC Science & Technology

2013-01-29

Time- dependence of calculated LD50. The data shown in Panel A were submitted to probit analysis to determine the LD50 of ricin at every 0.5-day...degenerate neutrophils and necrotic debris evident; (C) Only a limited region of the epithelium lining a bronchus remains viable (arrowheads); the...quantitative analysis of the dose dependent protective effects of the immunizations. All vaccine doses (2.5, 10 or 40 μg immunogen) resulted in significant

Expected effects of residual chlorine and nitrogen in sewage effluent on an estuarine pelagic ecosystem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hattis, D.; Lemerise, A.; Ratick, S.

1995-12-31

The authors used physical, toxicological, and system dynamic modeling tools to estimate the probable ecological effects caused by residual chlorine and nitrogen in sewage effluent discharged into Greenwich Cove, RI, USA. An energy systems model of the pelagic ecosystem in Narragansett Bay was developed and adapted for use in Greenwich Cove. This model allowed them to assess the indirect effects on organisms in the food web that result from a direct toxic effect on a given organism. Indirect food web mediated effects were the primary mode of loss for bluefish, but not for menhaden. The authors chose gross primary production,more » the flux of carbon to the benthos, fish out-migration, and fish harvest as outcome variables indicative of different valuable ecosystem functions. Organism responses were modeled using an assumption that lethal toxic responses occur as individual organism thresholds are exceeded, and that in general thresholds are lognormally distributed in a population of mixed individuals. They performed sensitivity analyses to assess the implications of different plausible values for the probit slopes used in the model. The putative toxic damage repair rate, combined with estimates of the exposure variability for each species, determined the averaging time that was likely to be most important in producing toxicity. Temperature was an important external factor in the physical, toxicological, and ecological models. These three models can be integrated into a single model applicable to other locations and stressors given the availability of appropriate data.« less

Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models.

PubMed

Chen, Zewei; Zhang, Xin; Zhang, Zhuoyong

2016-12-01

Timely risk assessment of chronic kidney disease (CKD) and proper community-based CKD monitoring are important to prevent patients with potential risk from further kidney injuries. As many symptoms are associated with the progressive development of CKD, evaluating risk of CKD through a set of clinical data of symptoms coupled with multivariate models can be considered as an available method for prevention of CKD and would be useful for community-based CKD monitoring. Three common used multivariate models, i.e., K-nearest neighbor (KNN), support vector machine (SVM), and soft independent modeling of class analogy (SIMCA), were used to evaluate risk of 386 patients based on a series of clinical data taken from UCI machine learning repository. Different types of composite data, in which proportional disturbances were added to simulate measurement deviations caused by environment and instrument noises, were also utilized to evaluate the feasibility and robustness of these models in risk assessment of CKD. For the original data set, three mentioned multivariate models can differentiate patients with CKD and non-CKD with the overall accuracies over 93 %. KNN and SVM have better performances than SIMCA has in this study. For the composite data set, SVM model has the best ability to tolerate noise disturbance and thus are more robust than the other two models. Using clinical data set on symptoms coupled with multivariate models has been proved to be feasible approach for assessment of patient with potential CKD risk. SVM model can be used as useful and robust tool in this study.

Repetitive pulses and laser-induced retinal injury thresholds

NASA Astrophysics Data System (ADS)

Lund, David J.

2007-02-01

Experimental studies with repetitively pulsed lasers show that the ED 50, expressed as energy per pulse, varies as the inverse fourth power of the number of pulses in the exposure, relatively independently of the wavelength, pulse duration, or pulse repetition frequency of the laser. Models based on a thermal damage mechanism cannot readily explain this result. Menendez et al. proposed a probability-summation model for predicting the threshold for a train of pulses based on the probit statistics for a single pulse. The model assumed that each pulse is an independent trial, unaffected by any other pulse in the train of pulses and assumes that the probability of damage for a single pulse is adequately described by the logistic curve. The requirement that the effect of each pulse in the pulse train be unaffected by the effects of other pulses in the train is a showstopper when the end effect is viewed as a thermal effect with each pulse in the train contributing to the end temperature of the target tissue. There is evidence that the induction of cell death by microcavitation bubbles around melanin granules heated by incident laser irradiation can satisfy the condition of pulse independence as required by the probability summation model. This paper will summarize the experimental data and discuss the relevance of the probability summation model given microcavitation as a damage mechanism.

A novel method for expediting the development of patient-reported outcome measures and an evaluation across several populations

PubMed Central

Garrard, Lili; Price, Larry R.; Bott, Marjorie J.; Gajewski, Byron J.

2016-01-01

Item response theory (IRT) models provide an appropriate alternative to the classical ordinal confirmatory factor analysis (CFA) during the development of patient-reported outcome measures (PROMs). Current literature has identified the assessment of IRT model fit as both challenging and underdeveloped (Sinharay & Johnson, 2003; Sinharay, Johnson, & Stern, 2006). This study evaluates the performance of Ordinal Bayesian Instrument Development (OBID), a Bayesian IRT model with a probit link function approach, through applications in two breast cancer-related instrument development studies. The primary focus is to investigate an appropriate method for comparing Bayesian IRT models in PROMs development. An exact Bayesian leave-one-out cross-validation (LOO-CV) approach (Vehtari & Lampinen, 2002) is implemented to assess prior selection for the item discrimination parameter in the IRT model and subject content experts’ bias (in a statistical sense and not to be confused with psychometric bias as in differential item functioning) toward the estimation of item-to-domain correlations. Results support the utilization of content subject experts’ information in establishing evidence for construct validity when sample size is small. However, the incorporation of subject experts’ content information in the OBID approach can be sensitive to the level of expertise of the recruited experts. More stringent efforts need to be invested in the appropriate selection of subject experts to efficiently use the OBID approach and reduce potential bias during PROMs development. PMID:27667878

A novel method for expediting the development of patient-reported outcome measures and an evaluation across several populations.

PubMed

Garrard, Lili; Price, Larry R; Bott, Marjorie J; Gajewski, Byron J

2016-10-01

Item response theory (IRT) models provide an appropriate alternative to the classical ordinal confirmatory factor analysis (CFA) during the development of patient-reported outcome measures (PROMs). Current literature has identified the assessment of IRT model fit as both challenging and underdeveloped (Sinharay & Johnson, 2003; Sinharay, Johnson, & Stern, 2006). This study evaluates the performance of Ordinal Bayesian Instrument Development (OBID), a Bayesian IRT model with a probit link function approach, through applications in two breast cancer-related instrument development studies. The primary focus is to investigate an appropriate method for comparing Bayesian IRT models in PROMs development. An exact Bayesian leave-one-out cross-validation (LOO-CV) approach (Vehtari & Lampinen, 2002) is implemented to assess prior selection for the item discrimination parameter in the IRT model and subject content experts' bias (in a statistical sense and not to be confused with psychometric bias as in differential item functioning) toward the estimation of item-to-domain correlations. Results support the utilization of content subject experts' information in establishing evidence for construct validity when sample size is small. However, the incorporation of subject experts' content information in the OBID approach can be sensitive to the level of expertise of the recruited experts. More stringent efforts need to be invested in the appropriate selection of subject experts to efficiently use the OBID approach and reduce potential bias during PROMs development.

Cole-Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass.

PubMed

Dabros, Michal; Dennewald, Danielle; Currie, David J; Lee, Mark H; Todd, Robert W; Marison, Ian W; von Stockar, Urs

2009-02-01

This work evaluates three techniques of calibrating capacitance (dielectric) spectrometers used for on-line monitoring of biomass: modeling of cell properties using the theoretical Cole-Cole equation, linear regression of dual-frequency capacitance measurements on biomass concentration, and multivariate (PLS) modeling of scanning dielectric spectra. The performance and robustness of each technique is assessed during a sequence of validation batches in two experimental settings of differing signal noise. In more noisy conditions, the Cole-Cole model had significantly higher biomass concentration prediction errors than the linear and multivariate models. The PLS model was the most robust in handling signal noise. In less noisy conditions, the three models performed similarly. Estimates of the mean cell size were done additionally using the Cole-Cole and PLS models, the latter technique giving more satisfactory results.

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

PubMed Central

2011-01-01

Background 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. Methods 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. Results 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. Conclusion 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 provide more accurate estimates of PID. JEL Classification: I10, I19, C23, C25 PMID:22828182

Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs

Treesearch

Daniel A. Yaussy; Robert L. Brisbin

1983-01-01

A multivariate regression model was developed to predict green board-foot yields for the seven common factory lumber grades processed from northern red oak (Quercus rubra L.) factory grade logs. The model uses the standard log measurements of grade, scaling diameter, length, and percent defect. It was validated with an independent data set. The model...

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

Power of Models in Longitudinal Study: Findings from a Full-Crossed Simulation Design

ERIC Educational Resources Information Center

Fang, Hua; Brooks, Gordon P.; Rizzo, Maria L.; Espy, Kimberly Andrews; Barcikowski, Robert S.

2009-01-01

Because the power properties of traditional repeated measures and hierarchical multivariate linear models have not been clearly determined in the balanced design for longitudinal studies in the literature, the authors present a power comparison study of traditional repeated measures and hierarchical multivariate linear models under 3…

Species distribution modelling for plant communities: Stacked single species or multivariate modelling approaches?

Treesearch

Emilie B. Henderson; Janet L. Ohmann; Matthew J. Gregory; Heather M. Roberts; Harold S.J. Zald

2014-01-01

Landscape management and conservation planning require maps of vegetation composition and structure over large regions. Species distribution models (SDMs) are often used for individual species, but projects mapping multiple species are rarer. We compare maps of plant community composition assembled by stacking results from many SDMs with multivariate maps constructed...

IRT-ZIP Modeling for Multivariate Zero-Inflated Count Data

ERIC Educational Resources Information Center

Wang, Lijuan

2010-01-01

This study introduces an item response theory-zero-inflated Poisson (IRT-ZIP) model to investigate psychometric properties of multiple items and predict individuals' latent trait scores for multivariate zero-inflated count data. In the model, two link functions are used to capture two processes of the zero-inflated count data. Item parameters are…

Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative

NASA Astrophysics Data System (ADS)

Luna-Gómez, Carlos D.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Galván-Tejada, Carlos E.; Celaya-Padilla, José M.

2017-03-01

Osteoarthritis is the most common rheumatic disease in the world. Knee pain is the most disabling symptom in the disease, the prediction of pain is one of the targets in preventive medicine, this can be applied to new therapies or treatments. Using the magnetic resonance imaging and the grading scales, a multivariate model based on genetic algorithms is presented. Using a predictive model can be useful to associate minor structure changes in the joint with the future knee pain. Results suggest that multivariate models can be predictive with future knee chronic pain. All models; T0, T1 and T2, were statistically significant, all p values were < 0.05 and all AUC > 0.60.

Multivariate-$t$ nonlinear mixed models with application to censored multi-outcome AIDS studies.

PubMed

Lin, Tsung-I; Wang, Wan-Lun

2017-10-01

In multivariate longitudinal HIV/AIDS studies, multi-outcome repeated measures on each patient over time may contain outliers, and the viral loads are often subject to a upper or lower limit of detection depending on the quantification assays. In this article, we consider an extension of the multivariate nonlinear mixed-effects model by adopting a joint multivariate-$t$ distribution for random effects and within-subject errors and taking the censoring information of multiple responses into account. The proposed model is called the multivariate-$t$ nonlinear mixed-effects model with censored responses (MtNLMMC), allowing for analyzing multi-outcome longitudinal data exhibiting nonlinear growth patterns with censorship and fat-tailed behavior. Utilizing the Taylor-series linearization method, a pseudo-data version of expectation conditional maximization either (ECME) algorithm is developed for iteratively carrying out maximum likelihood estimation. We illustrate our techniques with two data examples from HIV/AIDS studies. Experimental results signify that the MtNLMMC performs favorably compared to its Gaussian analogue and some existing approaches. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Multivariate analysis of longitudinal rates of change.

PubMed

Bryan, Matthew; Heagerty, Patrick J

2016-12-10

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed in the literature. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, 'accelerated time' methods have been developed which assume that covariates rescale time in longitudinal models for disease progression. In this manuscript, we detail an alternative multivariate model formulation that directly structures longitudinal rates of change and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging

PubMed Central

Naylor, Melissa G.; Cardenas, Valerie A.; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2015-01-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remains a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. PMID:23408378

Preliminary Multivariable Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2010-01-01

Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. Previously, the authors published two single variable cost models based on 19 flight missions. The current paper presents the development of a multi-variable space telescopes cost model. The validity of previously published models are tested. Cost estimating relationships which are and are not significant cost drivers are identified. And, interrelationships between variables are explored

Convex reformulation of biologically-based multi-criteria intensity-modulated radiation therapy optimization including fractionation effects

NASA Astrophysics Data System (ADS)

Hoffmann, Aswin L.; den Hertog, Dick; Siem, Alex Y. D.; Kaanders, Johannes H. A. M.; Huizenga, Henk

2008-11-01

Finding fluence maps for intensity-modulated radiation therapy (IMRT) can be formulated as a multi-criteria optimization problem for which Pareto optimal treatment plans exist. To account for the dose-per-fraction effect of fractionated IMRT, it is desirable to exploit radiobiological treatment plan evaluation criteria based on the linear-quadratic (LQ) cell survival model as a means to balance the radiation benefits and risks in terms of biologic response. Unfortunately, the LQ-model-based radiobiological criteria are nonconvex functions, which make the optimization problem hard to solve. We apply the framework proposed by Romeijn et al (2004 Phys. Med. Biol. 49 1991-2013) to find transformations of LQ-model-based radiobiological functions and establish conditions under which transformed functions result in equivalent convex criteria that do not change the set of Pareto optimal treatment plans. The functions analysed are: the LQ-Poisson-based model for tumour control probability (TCP) with and without inter-patient heterogeneity in radiation sensitivity, the LQ-Poisson-based relative seriality s-model for normal tissue complication probability (NTCP), the equivalent uniform dose (EUD) under the LQ-Poisson model and the fractionation-corrected Probit-based model for NTCP according to Lyman, Kutcher and Burman. These functions differ from those analysed before in that they cannot be decomposed into elementary EUD or generalized-EUD functions. In addition, we show that applying increasing and concave transformations to the convexified functions is beneficial for the piecewise approximation of the Pareto efficient frontier.

DUALITY IN MULTIVARIATE RECEPTOR MODEL. (R831078)

EPA Science Inventory

Multivariate receptor models are used for source apportionment of multiple observations of compositional data of air pollutants that obey mass conservation. Singular value decomposition of the data leads to two sets of eigenvectors. One set of eigenvectors spans a space in whi...

National Human Trafficking Initiatives: Dimensions of Policy Diffusion1

PubMed Central

Yoo, Eun-hye; Boyle, Elizabeth Heger

2014-01-01

The implementation of criminal law involves formal law enforcement, education and public outreach aimed at preventing criminal activity, and providing services for victims. Historically, quantitative research on global trends has tended to focus on a single policy dimension, potentially masking the unique factors that affect the diffusion of each policy dimension independently. Using an ordered-probit model to analyze new human trafficking policy data on national prosecution, prevention, and victim-protection efforts, we find that global ties and domestic interest groups matter more in areas where international law is less defined. While prosecution, officially mandated by the Trafficking Protocol, was relatively impervious to global ties and domestic interest groups, both trafficking prevention and victim protection were associated with these factors. Our findings also suggest that fear of repercussions is not a major driver of state actions to combat trafficking—neither ratification of the Trafficking Protocol nor levels of United States aid were associated with greater implementation of anti-trafficking measures. PMID:26538806

The Effect of Free Adult Preventive Care Services on Subsequent Utilization of Inpatient Services in Taiwan.

PubMed

Tian, Wei-Hua

2016-07-01

The objective of this article is to investigate the relationship between the utilization of free adult preventive care services and subsequent utilization of inpatient services among elderly people under the National Health Insurance program in Taiwan. The study used secondary data from the 2005 Taiwan National Health Interview Survey and claim data from the 2006 Taiwan National Health Insurance Research Database for the elderly aged 65 or over. A bivariate probit model was used to avoid the possible endogeneity in individuals' utilization of free adult preventive care and inpatient services. This study finds that, when individuals had utilized the preventive care services in 2005, the probability that they utilized inpatient services in 2006 was significantly reduced by 13.89%. The findings of this study may provide a good reference for policy makers to guide the efficient allocation of medical resources through the continuous promotion of free adult preventive care services under the National Health Insurance program. © Australian Council for Educational Research 2016.

When Do Older Adults Become “Disabled”? Social and Health Antecedents of Perceived Disability in a Panel Study of the Oldest Old*

PubMed Central

KELLEY-MOORE, JESSICA A.; SCHUMACHER, JOHN G.; KAHANA, EVA; KAHANA, BOAZ

2007-01-01

Disability carries negative social meaning, and little is known about when (or if), in the process of health decline, persons identify themselves as “disabled” We examine the social and health criteria that older adults use to subjectively rate their own disability status. Using a panel study of older adults (ages 72+), we estimate ordered probit and growth curve models of perceived disability over time. Total prevalent morbidity, functional limitations, and cognitive impairment are predictors of perceived disability. Cessation of driving and receipt of home health care also influence older adults’ perceptions of their own disability. A dense social network slowed the rate of labeling oneself disabled, while health anxiety accelerated the process over time, independent of health status. When considering perceived disability, the oldest old use multidimensional criteria capturing function, recent changes in health status and social networks, and anxiety about their health. PMID:16821507

Comparative acute toxicity of gallium(III), antimony(III), indium(III), cadmium(II), and copper(II) on freshwater swamp shrimp (Macrobrachium nipponense).

PubMed

Yang, Jen-Lee

2014-04-01

Acute toxicity testing were carried out the freshwater swamp shrimp, Macrobrachium nipponense, as the model animal for the semiconductor applied metals (gallium, antimony, indium, cadmium, and copper) to evaluate if the species is an suitable experimental animal of pollution in aquatic ecosystem. The static renewal test method of acute lethal concentrations determination was used, and water temperature was maintained at 24.0 ± 0.5°C. Data of individual metal obtained from acute toxicity tests were determined using probit analysis method. The median lethal concentration (96-h LC50) of gallium, antimony, indium, cadmium, and copper for M. nipponense were estimated as 2.7742, 1.9626, 6.8938, 0.0539, and 0.0313 mg/L, respectively. Comparing the toxicity tolerance of M. nipponense with other species which exposed to these metals, it is obviously that the M. nipponense is more sensitive than that of various other aquatic animals.

National Human Trafficking Initiatives: Dimensions of Policy Diffusion.

PubMed

Yoo, Eun-Hye; Boyle, Elizabeth Heger

2015-01-01

The implementation of criminal law involves formal law enforcement, education and public outreach aimed at preventing criminal activity, and providing services for victims. Historically, quantitative research on global trends has tended to focus on a single policy dimension, potentially masking the unique factors that affect the diffusion of each policy dimension independently. Using an ordered-probit model to analyze new human trafficking policy data on national prosecution, prevention, and victim-protection efforts, we find that global ties and domestic interest groups matter more in areas where international law is less defined. While prosecution, officially mandated by the Trafficking Protocol, was relatively impervious to global ties and domestic interest groups, both trafficking prevention and victim protection were associated with these factors. Our findings also suggest that fear of repercussions is not a major driver of state actions to combat trafficking-neither ratification of the Trafficking Protocol nor levels of United States aid were associated with greater implementation of anti-trafficking measures.

Determinants of ambulatory treatment mode for mental illness.

PubMed

Freiman, M P; Zuvekas, S H

2000-07-01

We estimate a reduced-form bivariate probit model to analyse jointly the choice of ambulatory treatment from the specialty mental health sector and/or the use of psychotropic drugs for a nationally representative sample of US household residents. We find significant differences in treatment choice by education, gender, race and ethnicity, while controlling for several aspects of self-reported mental health and treatment attitudes. For example, while women are more likely than men to use the specialty mental health sector and more likely to take psychotropic medications, this difference between men and women is much greater for psychotropic medications. The estimated differences may reflect patient preferences in a manner traditionally assumed when interpreting these coefficients in such equations, but we discuss how they may also reflect biases and misperceptions on the parts of patients and providers. We also discuss how our results relate to some findings and policies in the general health care sector. Copyright 2000 John Wiley & Sons, Ltd.

The 2016 Al-Mishraq sulphur plant fire: Source and health risk area estimation

NASA Astrophysics Data System (ADS)

Björnham, Oscar; Grahn, Håkan; von Schoenberg, Pontus; Liljedahl, Birgitta; Waleij, Annica; Brännström, Niklas

2017-11-01

On October 20, 2016, Daesh (Islamic State) set fire to the sulphur production site Al-Mishraq as the battle of Mosul in northern Iraq became more intense. An extensive plume of toxic sulphur dioxide and hydrogen sulphide caused comprehensive casualties. The intensity of the SO2 release was reaching levels of minor volcanic eruptions and the plume was observed by several satellites. By investigation of the measurement data from instruments on the MetOp-A, MetOp-B, Aura and Soumi satellites we have estimated the time-dependent source term to 161 kilotonnes sulphur dioxide released into the atmosphere during seven days. A long-range dispersion model was utilized to simulate the atmospheric transport over the Middle East. The ground level concentrations predicted by the simulation were compared with observation from the Turkey National Air Quality Monitoring Network. Finally, the simulation data provided, using a probit analysis of the simulated data, an estimate of the health risk area that was compared to reported urgent medical treatments.

Using Survey Data to Determine a Numeric Criterion for Nutrient Pollution

NASA Astrophysics Data System (ADS)

Jakus, Paul M.; Nelson, Nanette; Ostermiller, Jeffrey

2017-12-01

We present a scientific replication of a benthic algae nuisance threshold study originally conducted in Montana, but we do so using a different sampling methodology in a different state. Respondents are asked to rate eight photographs that depict varying algae conditions. Our initial results show that Utah resident preferences for benthic algae levels are quite similar to those of Montana residents, thus replicating the Montana study. For the full Utah sample, though, Cronbach's α indicated poor internal consistency in rating the photographs, so a "monotonicity rule" was used to identify respondents providing monotonic preferences with respect to chlorophyll a densities. Simple graphical analyses are combined with ordered probit analysis to determine the maximum desirable density of chlorophyll a (Chl a). Our analysis indicates that Chl a levels in excess of 150 mg Chl a/m2 are undesirable, but the regression model suggests that those with strictly monotonic preferences were far more likely favor a more stringent standard.

Private sector provision of oral rehydration therapy for child diarrhea in sub-Saharan Africa.

PubMed

Sood, Neeraj; Wagner, Zachary

2014-05-01

Although diarrheal mortality is cheaply preventable with oral rehydration therapy (ORT), over 700,000 children die of diarrhea annually and many health providers fail to treat diarrheal cases with ORT. Provision of ORT may differ between for-profit and public providers. This study used Demographic and Health Survey data from 19,059 children across 29 countries in sub-Saharan Africa from 2003 to 2011 to measure differences in child diarrhea treatment between private for-profit and public health providers. Differences in treatment provision were estimated using probit regression models controlling for key confounders. For-profit providers were 15% points less likely to provide ORT (95% confidence interval [CI] 13-17) than public providers and 12% points more likely to provide other treatments (95% CI 10-15). These disparities in ORT provision were more pronounced for poorer children in rural areas. As private healthcare in sub-Saharan Africa continues to expand, interventions to increase private sector provision of ORT should be explored.

Work Disability among Women: The Role of Divorce in a Retrospective Cohort Study.

PubMed

Tamborini, Christopher R; Reznik, Gayle L; Couch, Kenneth A

2016-03-01

We assess how divorce through midlife affects the subsequent probability of work-limiting health among U.S. women. Using retrospective marital and work disability histories from the Survey of Income and Program Participation matched to Social Security earnings records, we identify women whose first marriage dissolved between 1975 and 1984 (n = 1,214) and women who remain continuously married (n = 3,394). Probit and propensity score matching models examine the cumulative probability of a work disability over a 20-year follow-up period. We find that divorce is associated with a significantly higher cumulative probability of a work disability, controlling for a range of factors. This association is strongest among divorced women who do not remarry. No consistent relationships are observed among divorced women who remarry and remained married. We find that economic hardship, work history, and selection into divorce influence, but do not substantially alter, the lasting impact of divorce on work-limiting health. © American Sociological Association 2016.

Multivariate modelling of endophenotypes associated with the metabolic syndrome in Chinese twins.

PubMed

Pang, Z; Zhang, D; Li, S; Duan, H; Hjelmborg, J; Kruse, T A; Kyvik, K O; Christensen, K; Tan, Q

2010-12-01

The common genetic and environmental effects on endophenotypes related to the metabolic syndrome have been investigated using bivariate and multivariate twin models. This paper extends the pairwise analysis approach by introducing independent and common pathway models to Chinese twin data. The aim was to explore the common genetic architecture in the development of these phenotypes in the Chinese population. Three multivariate models including the full saturated Cholesky decomposition model, the common factor independent pathway model and the common factor common pathway model were fitted to 695 pairs of Chinese twins representing six phenotypes including BMI, total cholesterol, total triacylglycerol, fasting glucose, HDL and LDL. Performances of the nested models were compared with that of the full Cholesky model. Cross-phenotype correlation coefficients gave clear indication of common genetic or environmental backgrounds in the phenotypes. Decomposition of phenotypic correlation by the Cholesky model revealed that the observed phenotypic correlation among lipid phenotypes had genetic and unique environmental backgrounds. Both pathway models suggest a common genetic architecture for lipid phenotypes, which is distinct from that of the non-lipid phenotypes. The declining performance with model restriction indicates biological heterogeneity in development among some of these phenotypes. Our multivariate analyses revealed common genetic and environmental backgrounds for the studied lipid phenotypes in Chinese twins. Model performance showed that physiologically distinct endophenotypes may follow different genetic regulations.

Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data.

PubMed

Vial, Flavie; Wei, Wei; Held, Leonhard

2016-12-20

In an era of ubiquitous electronic collection of animal health data, multivariate surveillance systems (which concurrently monitor several data streams) should have a greater probability of detecting disease events than univariate systems. However, despite their limitations, univariate aberration detection algorithms are used in most active syndromic surveillance (SyS) systems because of their ease of application and interpretation. On the other hand, a stochastic modelling-based approach to multivariate surveillance offers more flexibility, allowing for the retention of historical outbreaks, for overdispersion and for non-stationarity. While such methods are not new, they are yet to be applied to animal health surveillance data. We applied an example of such stochastic model, Held and colleagues' two-component model, to two multivariate animal health datasets from Switzerland. In our first application, multivariate time series of the number of laboratories test requests were derived from Swiss animal diagnostic laboratories. We compare the performance of the two-component model to parallel monitoring using an improved Farrington algorithm and found both methods yield a satisfactorily low false alarm rate. However, the calibration test of the two-component model on the one-step ahead predictions proved satisfactory, making such an approach suitable for outbreak prediction. In our second application, the two-component model was applied to the multivariate time series of the number of cattle abortions and the number of test requests for bovine viral diarrhea (a disease that often results in abortions). We found that there is a two days lagged effect from the number of abortions to the number of test requests. We further compared the joint modelling and univariate modelling of the number of laboratory test requests time series. The joint modelling approach showed evidence of superiority in terms of forecasting abilities. Stochastic modelling approaches offer the potential to address more realistic surveillance scenarios through, for example, the inclusion of times series specific parameters, or of covariates known to have an impact on syndrome counts. Nevertheless, many methodological challenges to multivariate surveillance of animal SyS data still remain. Deciding on the amount of corroboration among data streams that is required to escalate into an alert is not a trivial task given the sparse data on the events under consideration (e.g. disease outbreaks).

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

NASA Astrophysics Data System (ADS)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

PubMed Central

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-01-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states. PMID:26996254

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy.

PubMed

Dankers, Frank; Wijsman, Robin; Troost, Esther G C; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L

2017-05-07

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

NASA Astrophysics Data System (ADS)

Dankers, Frank; Wijsman, Robin; Troost, Esther G. C.; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L.

2017-05-01

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Multivariate meta-analysis: potential and promise.

PubMed

Jackson, Dan; Riley, Richard; White, Ian R

2011-09-10

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Multivariate meta-analysis: Potential and promise

PubMed Central

Jackson, Dan; Riley, Richard; White, Ian R

2011-01-01

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

Stress and Personal Resource as Predictors of the Adjustment of Parents to Autistic Children: A Multivariate Model

ERIC Educational Resources Information Center

Siman-Tov, Ayelet; Kaniel, Shlomo

2011-01-01

The research validates a multivariate model that predicts parental adjustment to coping successfully with an autistic child. The model comprises four elements: parental stress, parental resources, parental adjustment and the child's autism symptoms. 176 parents of children aged between 6 to 16 diagnosed with PDD answered several questionnaires…

Multivariate mixed linear model analysis of longitudinal data: an information-rich statistical technique for analyzing disease resistance data

USDA-ARS?s Scientific Manuscript database

The mixed linear model (MLM) is currently among the most advanced and flexible statistical modeling techniques and its use in tackling problems in plant pathology has begun surfacing in the literature. The longitudinal MLM is a multivariate extension that handles repeatedly measured data, such as r...

Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method

Treesearch

Denis Valle; Benjamin Baiser; Christopher W. Woodall; Robin Chazdon; Jerome Chave

2014-01-01

We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates...

Visible lesion laser thresholds in Cynomolgus (Macaca fascicularis) retina with a 1064 nm 12-ns pulsed laser

NASA Astrophysics Data System (ADS)

Oliver, Jeffrey W.; Stolarski, David J.; Noojin, Gary D.; Hodnett, Harvey M.; Imholte, Michelle L.; Rockwell, Benjamin A.; Kumru, Semih S.

2007-02-01

A series of experiments in a new animal model for retinal damage, cynomolgus monkeys (Macaca fascicularis), have been conducted to determine the damage threshold for 12.5-nanosecond laser exposures at 1064 nm. These results provide a direct comparison to threshold values obtained in rhesus monkey (Macaca mulatta), which is the model historically used in establishing retinal maximum permissible exposure (MPE) limits. In this study, the irradiance level of a collimated Gaussian laser beam of 2.5 mm diameter at the cornea was randomly varied to produce a rectangular grid of exposures on the retina. Exposures sites were fundoscopically evaluated at post-irradiance intervals of 1 hour and 24 hours. Probit analysis was performed on dose-response data to obtain probability of response curves. The 50% probability of damage (ED50) values for 1 and 24 hours post-exposure are 28.5(22.7-38.4) μJ and 17.0(12.9-21.8) μJ, respectively. These values compare favorably to data obtained with the rhesus model, 28.7(22.3-39.3) μJ and 19.1(13.6-24.4) μJ, suggesting that the cynomolgus monkey may be a suitable replacement for rhesus monkey in photoacoustic minimum visible lesion threshold studies.

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Univariate and multivariate spatial models of health facility utilisation for childhood fevers in an area on the coast of Kenya.

PubMed

Ouma, Paul O; Agutu, Nathan O; Snow, Robert W; Noor, Abdisalan M

2017-09-18

Precise quantification of health service utilisation is important for the estimation of disease burden and allocation of health resources. Current approaches to mapping health facility utilisation rely on spatial accessibility alone as the predictor. However, other spatially varying social, demographic and economic factors may affect the use of health services. The exclusion of these factors can lead to the inaccurate estimation of health facility utilisation. Here, we compare the accuracy of a univariate spatial model, developed only from estimated travel time, to a multivariate model that also includes relevant social, demographic and economic factors. A theoretical surface of travel time to the nearest public health facility was developed. These were assigned to each child reported to have had fever in the Kenya demographic and health survey of 2014 (KDHS 2014). The relationship of child treatment seeking for fever with travel time, household and individual factors from the KDHS2014 were determined using multilevel mixed modelling. Bayesian information criterion (BIC) and likelihood ratio test (LRT) tests were carried out to measure how selected factors improve parsimony and goodness of fit of the time model. Using the mixed model, a univariate spatial model of health facility utilisation was fitted using travel time as the predictor. The mixed model was also used to compute a multivariate spatial model of utilisation, using travel time and modelled surfaces of selected household and individual factors as predictors. The univariate and multivariate spatial models were then compared using the receiver operating area under the curve (AUC) and a percent correct prediction (PCP) test. The best fitting multivariate model had travel time, household wealth index and number of children in household as the predictors. These factors reduced BIC of the time model from 4008 to 2959, a change which was confirmed by the LRT test. Although there was a high correlation of the two modelled probability surfaces (Adj R 2  = 88%), the multivariate model had better AUC compared to the univariate model; 0.83 versus 0.73 and PCP 0.61 versus 0.45 values. Our study shows that a model that uses travel time, as well as household and individual-level socio-demographic factors, results in a more accurate estimation of use of health facilities for the treatment of childhood fever, compared to one that relies on only travel time.

Adjustment of automatic control systems of production facilities at coal processing plants using multivariant physico- mathematical models

NASA Astrophysics Data System (ADS)

Evtushenko, V. F.; Myshlyaev, L. P.; Makarov, G. V.; Ivushkin, K. A.; Burkova, E. V.

2016-10-01

The structure of multi-variant physical and mathematical models of control system is offered as well as its application for adjustment of automatic control system (ACS) of production facilities on the example of coal processing plant.

A simplified parsimonious higher order multivariate Markov chain model with new convergence condition

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a simplified parsimonious higher-order multivariate Markov chain model with new convergence condition. (TPHOMMCM-NCC). Moreover, estimation method of the parameters in TPHOMMCM-NCC is give. Numerical experiments illustrate the effectiveness of TPHOMMCM-NCC.

Various forms of indexing HDMR for modelling multivariate classification problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aksu, Çağrı; Tunga, M. Alper

2014-12-10

The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled.more » In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.« less

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.

Insights on multivariate updates of physical and biogeochemical ocean variables using an Ensemble Kalman Filter and an idealized model of upwelling

NASA Astrophysics Data System (ADS)

Yu, Liuqian; Fennel, Katja; Bertino, Laurent; Gharamti, Mohamad El; Thompson, Keith R.

2018-06-01

Effective data assimilation methods for incorporating observations into marine biogeochemical models are required to improve hindcasts, nowcasts and forecasts of the ocean's biogeochemical state. Recent assimilation efforts have shown that updating model physics alone can degrade biogeochemical fields while only updating biogeochemical variables may not improve a model's predictive skill when the physical fields are inaccurate. Here we systematically investigate whether multivariate updates of physical and biogeochemical model states are superior to only updating either physical or biogeochemical variables. We conducted a series of twin experiments in an idealized ocean channel that experiences wind-driven upwelling. The forecast model was forced with biased wind stress and perturbed biogeochemical model parameters compared to the model run representing the "truth". Taking advantage of the multivariate nature of the deterministic Ensemble Kalman Filter (DEnKF), we assimilated different combinations of synthetic physical (sea surface height, sea surface temperature and temperature profiles) and biogeochemical (surface chlorophyll and nitrate profiles) observations. We show that when biogeochemical and physical properties are highly correlated (e.g., thermocline and nutricline), multivariate updates of both are essential for improving model skill and can be accomplished by assimilating either physical (e.g., temperature profiles) or biogeochemical (e.g., nutrient profiles) observations. In our idealized domain, the improvement is largely due to a better representation of nutrient upwelling, which results in a more accurate nutrient input into the euphotic zone. In contrast, assimilating surface chlorophyll improves the model state only slightly, because surface chlorophyll contains little information about the vertical density structure. We also show that a degradation of the correlation between observed subsurface temperature and nutrient fields, which has been an issue in several previous assimilation studies, can be reduced by multivariate updates of physical and biogeochemical fields.

Multivariate generalized multifactor dimensionality reduction to detect gene-gene interactions

PubMed Central

2013-01-01

Background Recently, one of the greatest challenges in genome-wide association studies is to detect gene-gene and/or gene-environment interactions for common complex human diseases. Ritchie et al. (2001) proposed multifactor dimensionality reduction (MDR) method for interaction analysis. MDR is a combinatorial approach to reduce multi-locus genotypes into high-risk and low-risk groups. Although MDR has been widely used for case-control studies with binary phenotypes, several extensions have been proposed. One of these methods, a generalized MDR (GMDR) proposed by Lou et al. (2007), allows adjusting for covariates and applying to both dichotomous and continuous phenotypes. GMDR uses the residual score of a generalized linear model of phenotypes to assign either high-risk or low-risk group, while MDR uses the ratio of cases to controls. Methods In this study, we propose multivariate GMDR, an extension of GMDR for multivariate phenotypes. Jointly analysing correlated multivariate phenotypes may have more power to detect susceptible genes and gene-gene interactions. We construct generalized estimating equations (GEE) with multivariate phenotypes to extend generalized linear models. Using the score vectors from GEE we discriminate high-risk from low-risk groups. We applied the multivariate GMDR method to the blood pressure data of the 7,546 subjects from the Korean Association Resource study: systolic blood pressure (SBP) and diastolic blood pressure (DBP). We compare the results of multivariate GMDR for SBP and DBP to the results from separate univariate GMDR for SBP and DBP, respectively. We also applied the multivariate GMDR method to the repeatedly measured hypertension status from 5,466 subjects and compared its result with those of univariate GMDR at each time point. Results Results from the univariate GMDR and multivariate GMDR in two-locus model with both blood pressures and hypertension phenotypes indicate best combinations of SNPs whose interaction has significant association with risk for high blood pressures or hypertension. Although the test balanced accuracy (BA) of multivariate analysis was not always greater than that of univariate analysis, the multivariate BAs were more stable with smaller standard deviations. Conclusions In this study, we have developed multivariate GMDR method using GEE approach. It is useful to use multivariate GMDR with correlated multiple phenotypes of interests. PMID:24565370

Factors affecting nurses' decision to get the flu vaccine.

PubMed

Shahrabani, Shosh; Benzion, Uri; Yom Din, Gregory

2009-05-01

The objective of this study was to identify factors that influence the decision whether or not to get the influenza (flu) vaccine among nurses in Israel by using the health belief model (HBM). A questionnaire distributed among 299 nurses in Israel in winter 2005/2006 included (1) socio-demographic information; (2) variables based on the HBM, including susceptibility, seriousness, benefits, barriers and cues to action; and (3) knowledge about influenza and the vaccine, and health motivation. A probit model was used to analyze the data. In Israel, the significant HBM categories affecting nurses' decision to get a flu shot are the perceived benefits from vaccination and cues to action. In addition, nurses who are vaccinated have higher levels of (1) knowledge regarding the vaccine and influenza, (2) perceived seriousness of the illness, (3) perceived susceptibility, and (4) health motivation than do those who do not get the vaccine. Immunization of healthcare workers may reduce the risk of flu outbreaks in all types of healthcare facilities and reduce morbidity and mortality among high-risk patients. In order to increase vaccination rates among nurses, efforts should be made to educate them regarding the benefits of vaccination and the potential health consequences of influenza for their patients, and themselves.

Changes in Quality of Health Care Delivery after Vertical Integration

PubMed Central

Carlin, Caroline S; Dowd, Bryan; Feldman, Roger

2015-01-01

Objectives To fill an empirical gap in the literature by examining changes in quality of care measures occurring when multispecialty clinic systems were acquired by hospital-owned, vertically integrated health care delivery systems in the Twin Cities area. Data Sources/Study Setting Administrative data for health plan enrollees attributed to treatment and control clinic systems, merged with U.S. Census data. Study Design We compared changes in quality measures for health plan enrollees in the acquired clinics to enrollees in nine control groups using a differences-in-differences model. Our dataset spans 2 years prior to and 4 years after the acquisitions. We estimated probit models with errors clustered within enrollees. Data Collection/Extraction Methods Data were assembled by the health plan’s informatics team. Principal Findings Vertical integration is associated with increased rates of colorectal and cervical cancer screening and more appropriate emergency department use. The probability of ambulatory care–sensitive admissions increased when the acquisition caused disruption in admitting patterns. Conclusions Moving a clinic system into a vertically integrated delivery system resulted in limited increases in quality of care indicators. Caution is warranted when the acquisition causes disruption in referral patterns. PMID:25529312

Model-based Bayesian inference for ROC data analysis

NASA Astrophysics Data System (ADS)

Lei, Tianhu; Bae, K. Ty

2013-03-01

This paper presents a study of model-based Bayesian inference to Receiver Operating Characteristics (ROC) data. The model is a simple version of general non-linear regression model. Different from Dorfman model, it uses a probit link function with a covariate variable having zero-one two values to express binormal distributions in a single formula. Model also includes a scale parameter. Bayesian inference is implemented by Markov Chain Monte Carlo (MCMC) method carried out by Bayesian analysis Using Gibbs Sampling (BUGS). Contrast to the classical statistical theory, Bayesian approach considers model parameters as random variables characterized by prior distributions. With substantial amount of simulated samples generated by sampling algorithm, posterior distributions of parameters as well as parameters themselves can be accurately estimated. MCMC-based BUGS adopts Adaptive Rejection Sampling (ARS) protocol which requires the probability density function (pdf) which samples are drawing from be log concave with respect to the targeted parameters. Our study corrects a common misconception and proves that pdf of this regression model is log concave with respect to its scale parameter. Therefore, ARS's requirement is satisfied and a Gaussian prior which is conjugate and possesses many analytic and computational advantages is assigned to the scale parameter. A cohort of 20 simulated data sets and 20 simulations from each data set are used in our study. Output analysis and convergence diagnostics for MCMC method are assessed by CODA package. Models and methods by using continuous Gaussian prior and discrete categorical prior are compared. Intensive simulations and performance measures are given to illustrate our practice in the framework of model-based Bayesian inference using MCMC method.

Estimation and model selection of semiparametric multivariate survival functions under general censorship.

PubMed

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2010-07-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root- n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided.

Estimation and model selection of semiparametric multivariate survival functions under general censorship

PubMed Central

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2013-01-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root-n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided. PMID:24790286

Usual Dietary Intakes: SAS Macros for Fitting Multivariate Measurement Error Models & Estimating Multivariate Usual Intake Distributions

Cancer.gov

The following SAS macros can be used to create a multivariate usual intake distribution for multiple dietary components that are consumed nearly every day or episodically. A SAS macro for performing balanced repeated replication (BRR) variance estimation is also included.

Comparative Robustness of Recent Methods for Analyzing Multivariate Repeated Measures Designs

ERIC Educational Resources Information Center

Seco, Guillermo Vallejo; Gras, Jaime Arnau; Garcia, Manuel Ato

2007-01-01

This study evaluated the robustness of two recent methods for analyzing multivariate repeated measures when the assumptions of covariance homogeneity and multivariate normality are violated. Specifically, the authors' work compares the performance of the modified Brown-Forsythe (MBF) procedure and the mixed-model procedure adjusted by the…

Family leave after childbirth and the mental health of new mothers.

PubMed

Chatterji, Pinka; Markowitz, Sara

2012-06-01

Recent studies indicate that short maternity leave, and, more generally, full-time maternal employment during the first year of life, detract from children's health, cognitive development, and behavioral outcomes. Much less is known, however, about how early parental employment affects the mental and physical health of the mothers themselves. The purpose of this paper is to examine the association between short family leave length (less than 12 weeks of total leave after childbirth, less than 8 weeks of paid leave) and mental and physical health outcomes among new mothers. Data come from the Early Childhood Longitudinal Study--Birth Cohort (ECLS-B), a nationally representative sample of 14,000 children born in 2001 and followed until kindergarten entry. We focus on a sample of ECLS-B mothers from the first wave of the survey who had worked during pregnancy and who had returned to work by the time of the first follow-up interview, which was conducted about 9 months after childbirth. When examining the effects of paternal leave, we further restrict this sample to mothers who were married at the time of the first follow-up interview. The maternal health outcomes of interest are measures of depression and overall health status. We use standard OLS and ordered probit models, as well as two-stage least squares and two-stage residual inclusion methods which address the potential endogeneity of family leave with respect to maternal health. Our findings from the OLS and ordered probit models indicate that, for mothers who worked prior to childbirth and who return to work in the first year, having less than 12 weeks of maternal leave and having less than 8 weeks of paid maternal leave are both associated with increases in depressive symptoms, and having less than 8 weeks of paid leave is associated with a reduction in overall health status. Findings from models that address the potential endogeneity of maternal leave generally support these results, and suggest that longer leave may improve the health of new mothers. Our findings suggest that longer leave after childbirth may benefit families. However, one potential drawback of using cross-sectional variation in state policies and community characteristics for identification is that these measures may be correlated with other unmeasured factors that directly influence family leave and maternal health. The mother's mental and physical health can be an important route through which infants are affected by parents' employment decisions. Our findings suggest that post-partum health services that target mothers' mental and physical health, and its effects on infants, may be useful. Our findings suggest that policies that support longer family leave may benefit maternal mental health. Future research should examine how workplace and public policies related to maternal employment can be used to improve families' health outcomes.

Critical elements on fitting the Bayesian multivariate Poisson Lognormal model

NASA Astrophysics Data System (ADS)

Zamzuri, Zamira Hasanah binti

2015-10-01

Motivated by a problem on fitting multivariate models to traffic accident data, a detailed discussion of the Multivariate Poisson Lognormal (MPL) model is presented. This paper reveals three critical elements on fitting the MPL model: the setting of initial estimates, hyperparameters and tuning parameters. These issues have not been highlighted in the literature. Based on simulation studies conducted, we have shown that to use the Univariate Poisson Model (UPM) estimates as starting values, at least 20,000 iterations are needed to obtain reliable final estimates. We also illustrated the sensitivity of the specific hyperparameter, which if it is not given extra attention, may affect the final estimates. The last issue is regarding the tuning parameters where they depend on the acceptance rate. Finally, a heuristic algorithm to fit the MPL model is presented. This acts as a guide to ensure that the model works satisfactorily given any data set.

Analysis/forecast experiments with a multivariate statistical analysis scheme using FGGE data

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1985-01-01

A three-dimensional, multivariate, statistical analysis method, optimal interpolation (OI) is described for modeling meteorological data from widely dispersed sites. The model was developed to analyze FGGE data at the NASA-Goddard Laboratory of Atmospherics. The model features a multivariate surface analysis over the oceans, including maintenance of the Ekman balance and a geographically dependent correlation function. Preliminary comparisons are made between the OI model and similar schemes employed at the European Center for Medium Range Weather Forecasts and the National Meteorological Center. The OI scheme is used to provide input to a GCM, and model error correlations are calculated for forecasts of 500 mb vertical water mixing ratios and the wind profiles. Comparisons are made between the predictions and measured data. The model is shown to be as accurate as a successive corrections model out to 4.5 days.

Multivariate Bayesian modeling of known and unknown causes of events--an application to biosurveillance.

PubMed

Shen, Yanna; Cooper, Gregory F

2012-09-01

This paper investigates Bayesian modeling of known and unknown causes of events in the context of disease-outbreak detection. We introduce a multivariate Bayesian approach that models multiple evidential features of every person in the population. This approach models and detects (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities and (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively non-informative prior probabilities. We report the results of simulation experiments which support that this modeling method can improve the detection of new disease outbreaks in a population. A contribution of this paper is that it introduces a multivariate Bayesian approach for jointly modeling both known and unknown causes of events. Such modeling has general applicability in domains where the space of known causes is incomplete. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

FACTOR ANALYTIC MODELS OF CLUSTERED MULTIVARIATE DATA WITH INFORMATIVE CENSORING

EPA Science Inventory

This paper describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster-level latent variables related to the primary outcomes and to the censorin...

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

1996-12-01

Consensus principles from radiation biology were used to describe a generic set of nonlinear, first-order differential equations for modeling 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 dose-response 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: 12,827more » mice, 2925 rats, 1676 sheep, 829 swine, 479 dogs, and 204 burros. 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 stemlike 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 LD50 and/or fractional group mortality in 38 protracted-dose experiments (rats and mice) that were not used in the fitting of model coefficients. 29 refs., 5 figs., 5 tabs.« less

An Examination of the Domain of Multivariable Functions Using the Pirie-Kieren Model

ERIC Educational Resources Information Center

Sengul, Sare; Yildiz, Sevda Goktepe

2016-01-01

The aim of this study is to employ the Pirie-Kieren model so as to examine the understandings relating to the domain of multivariable functions held by primary school mathematics preservice teachers. The data obtained was categorized according to Pirie-Kieren model and demonstrated visually in tables and bar charts. The study group consisted of…

Multivariate regression model for predicting yields of grade lumber from yellow birch sawlogs

Treesearch

Andrew F. Howard; Daniel A. Yaussy

1986-01-01

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and...

A Multivariate Model for the Meta-Analysis of Study Level Survival Data at Multiple Times

ERIC Educational Resources Information Center

Jackson, Dan; Rollins, Katie; Coughlin, Patrick

2014-01-01

Motivated by our meta-analytic dataset involving survival rates after treatment for critical leg ischemia, we develop and apply a new multivariate model for the meta-analysis of study level survival data at multiple times. Our data set involves 50 studies that provide mortality rates at up to seven time points, which we model simultaneously, and…

'In general, how do you feel today?'--self-rated health in the context of aging in India.

PubMed

Hirve, Siddhivinayak

2014-01-01

This thesis is centered on self-rated health (SRH) as an outcome measure, as a predictor, and as a marker. The thesis uses primary data from the WHO Study on global AGEing and adult health (SAGE) implemented in India in 2007. The structural equation modeling approach is employed to understand the pathways through which the social environment, disability, disease, and sociodemographic characteristics influence SRH among older adults aged 50 years and above. Cox proportional hazard model is used to explore the role of SRH as a predictor for mortality and the role of disability in modifying this effect. The hierarchical ordered probit modeling approach, which combines information from anchoring vignettes with SRH, was used to address the long overlooked methodological concern of interpersonal incomparability. Finally, multilevel model-based small area estimation techniques were used to demonstrate the use of large national surveys and census information to derive precise SRH prevalence estimates at the district and sub-district level. The thesis advocates the use of such a simple measure to identify vulnerable communities for targeted health interventions, to plan and prioritize resource allocation, and to evaluate health interventions in resource-scarce settings. The thesis provides the basis and impetus to generate and integrate similar and harmonized adult health and aging data platforms within demographic surveillance systems in different regions of India and elsewhere.

Analytical framework for reconstructing heterogeneous environmental variables from mammal community structure.

PubMed

Louys, Julien; Meloro, Carlo; Elton, Sarah; Ditchfield, Peter; Bishop, Laura C

2015-01-01

We test the performance of two models that use mammalian communities to reconstruct multivariate palaeoenvironments. While both models exploit the correlation between mammal communities (defined in terms of functional groups) and arboreal heterogeneity, the first uses a multiple multivariate regression of community structure and arboreal heterogeneity, while the second uses a linear regression of the principal components of each ecospace. The success of these methods means the palaeoenvironment of a particular locality can be reconstructed in terms of the proportions of heavy, moderate, light, and absent tree canopy cover. The linear regression is less biased, and more precisely and accurately reconstructs heavy tree canopy cover than the multiple multivariate model. However, the multiple multivariate model performs better than the linear regression for all other canopy cover categories. Both models consistently perform better than randomly generated reconstructions. We apply both models to the palaeocommunity of the Upper Laetolil Beds, Tanzania. Our reconstructions indicate that there was very little heavy tree cover at this site (likely less than 10%), with the palaeo-landscape instead comprising a mixture of light and absent tree cover. These reconstructions help resolve the previous conflicting palaeoecological reconstructions made for this site. Copyright © 2014 Elsevier Ltd. All rights reserved.

Does Eating Out Make Elderly People Depressed? Empirical Evidence from National Health and Nutrition Survey in Taiwan.

PubMed

Chang, Hung-Hao; Saeliw, Kannika

2017-06-01

This study investigates the association between eating out and depressive symptoms among elderly people. Potential mediators that may link to elderly eating out and depressive symptoms are also discussed. A unique dataset of 1,184 individuals aged 65 and older was drawn from the National Health and Nutrition Survey in 2008 in Taiwan. A bivariate probit model and an instrumental variable probit model were estimated to account for correlated, unmeasured factors that may be associated with both the decision and frequency of eating out and depressive symptoms in the elderly. An additional analysis is conducted to check whether the nutrient intakes and body weights can been seen as mediators that link the association between eating out and depressive symptoms of the elderly. Elderly people who eat out are 38 percent points more likely to have depressive symptoms than their counterparts who do not eat out, after controlling for socio-demographic characteristics and other factors. A positive association between the frequency of eating out and the likelihood of having depressive symptoms of the elderly is also found. It is evident that one addition meal away from home is associated with an increase of the likelihood of being depressed by 3.8 percentage points. With respect to the mediations, we find that nutrient intakes and body weight are likely to serve as mediators for the positive relationship between eating out and depressive symptoms in the elderly. Our results show that elderly who eat out have a higher chance of having depressive symptoms. To prevent depressive symptoms in the elderly, policy makers should be aware of the relationship among psychological status, physical health and nutritional health when assisting the elderly to better manage their food consumption away from home. Our study have some caveats. First, the interpretation of our results on the causality issue calls for caution in that our analysis relies on a cross-sectional survey. Second, other measures to define elderly depression, such as the Center for Epidemiological Studies-Depression (CES-D) score, can be used to check the robustness of our findings. Finally, the availability of food outlets in the local area and family characteristics are possibly associated with food away from home of the elderly. If data permit, the relationship between eating out and elderly depressive symptoms can be better identified after controlling for variables related to food facilities and family characteristics.

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China.

PubMed

Pei, Ling-Ling; Li, Qin; Wang, Zheng-Xin

2018-03-08

The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China's pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N )) model based on the nonlinear least square (NLS) method. The Gauss-Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N ) model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N ) and the NLS-based TNGM (1, N ) models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC), and per capita emissions of SO₂ and dust, alongside GDP per capita in China during the period 1996-2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N ) model presents greater precision when forecasting WDPC, SO₂ emissions and dust emissions per capita, compared to the traditional GM (1, N ) model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO₂ and dust reduce accordingly.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging.

PubMed

Naylor, Melissa G; Cardenas, Valerie A; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2014-03-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remain a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. Copyright © 2013 Wiley Periodicals, Inc.

Multivariate Analysis of Longitudinal Rates of Change

PubMed Central

Bryan, Matthew; Heagerty, Patrick J.

2016-01-01

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed by Roy and Lin [1]; Proust-Lima, Letenneur and Jacqmin-Gadda [2]; and Gray and Brookmeyer [3] among others. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, Gray and Brookmeyer [3] introduce an “accelerated time” method which assumes that covariates rescale time in longitudinal models for disease progression. In this manuscript we detail an alternative multivariate model formulation that directly structures longitudinal rates of change, and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. PMID:27417129

A Multivariate Descriptive Model of Motivation for Orthodontic Treatment.

ERIC Educational Resources Information Center

Hackett, Paul M. W.; And Others

1993-01-01

Motivation for receiving orthodontic treatment was studied among 109 young adults, and a multivariate model of the process is proposed. The combination of smallest scale analysis and Partial Order Scalogram Analysis by base Coordinates (POSAC) illustrates an interesting methodology for health treatment studies and explores motivation for dental…

Mathematical Formulation of Multivariate Euclidean Models for Discrimination Methods.

ERIC Educational Resources Information Center

Mullen, Kenneth; Ennis, Daniel M.

1987-01-01

Multivariate models for the triangular and duo-trio methods are described, and theoretical methods are compared to a Monte Carlo simulation. Implications are discussed for a new theory of multidimensional scaling which challenges the traditional assumption that proximity measures and perceptual distances are monotonically related. (Author/GDC)

A Multivariate Model of Parent-Adolescent Relationship Variables in Early Adolescence

ERIC Educational Resources Information Center

McKinney, Cliff; Renk, Kimberly

2011-01-01

Given the importance of predicting outcomes for early adolescents, this study examines a multivariate model of parent-adolescent relationship variables, including parenting, family environment, and conflict. Participants, who completed measures assessing these variables, included 710 culturally diverse 11-14-year-olds who were attending a middle…

Classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.

2002-01-01

An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

Multivariate missing data in hydrology - Review and applications

NASA Astrophysics Data System (ADS)

Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.

2017-12-01

Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.

A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores

PubMed Central

Neelon, Brian; Gelfand, Alan E.; Miranda, Marie Lynn

2013-01-01

Summary Researchers in the health and social sciences often wish to examine joint spatial patterns for two or more related outcomes. Examples include infant birth weight and gestational length, psychosocial and behavioral indices, and educational test scores from different cognitive domains. We propose a multivariate spatial mixture model for the joint analysis of continuous individual-level outcomes that are referenced to areal units. The responses are modeled as a finite mixture of multivariate normals, which accommodates a wide range of marginal response distributions and allows investigators to examine covariate effects within subpopulations of interest. The model has a hierarchical structure built at the individual level (i.e., individuals are nested within areal units), and thus incorporates both individual- and areal-level predictors as well as spatial random effects for each mixture component. Conditional autoregressive (CAR) priors on the random effects provide spatial smoothing and allow the shape of the multivariate distribution to vary flexibly across geographic regions. We adopt a Bayesian modeling approach and develop an efficient Markov chain Monte Carlo model fitting algorithm that relies primarily on closed-form full conditionals. We use the model to explore geographic patterns in end-of-grade math and reading test scores among school-age children in North Carolina. PMID:26401059

Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record

NASA Astrophysics Data System (ADS)

Relan, Rishi; Tiels, Koen; Marconato, Anna; Dreesen, Philippe; Schoukens, Johan

2018-05-01

Many real world systems exhibit a quasi linear or weakly nonlinear behavior during normal operation, and a hard saturation effect for high peaks of the input signal. In this paper, a methodology to identify a parsimonious discrete-time nonlinear state space model (NLSS) for the nonlinear dynamical system with relatively short data record is proposed. The capability of the NLSS model structure is demonstrated by introducing two different initialisation schemes, one of them using multivariate polynomials. In addition, a method using first-order information of the multivariate polynomials and tensor decomposition is employed to obtain the parsimonious decoupled representation of the set of multivariate real polynomials estimated during the identification of NLSS model. Finally, the experimental verification of the model structure is done on the cascaded water-benchmark identification problem.

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model.

PubMed

Snell, Kym I E; Hua, Harry; Debray, Thomas P A; Ensor, Joie; Look, Maxime P; Moons, Karel G M; Riley, Richard D

2016-01-01

Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Health insurance coverage among disabled Medicare enrollees

PubMed Central

Rubin, Jeffrey I.; Wilcox-Gök, Virginia

1991-01-01

In this article, we use the Survey of Income and Program Participation to identify patterns of non-Medicare insurance coverage among disabled Medicare enrollees. Compared with the aged, the disabled are less likely to have private insurance coverage and more likely to have Medicaid. Probit analysis of the determinants of private insurance for disabled Medicare enrollees shows that income, education, marital status, sex, and having an employed family member are positively related to the likelihood of having private health insurance, whereas age and the probability of Medicaid enrollment are negatively related to this likelihood. PMID:10170806

Multivariate Formation Pressure Prediction with Seismic-derived Petrophysical Properties from Prestack AVO inversion and Poststack Seismic Motion Inversion

NASA Astrophysics Data System (ADS)

Yu, H.; Gu, H.

2017-12-01

A novel multivariate seismic formation pressure prediction methodology is presented, which incorporates high-resolution seismic velocity data from prestack AVO inversion, and petrophysical data (porosity and shale volume) derived from poststack seismic motion inversion. In contrast to traditional seismic formation prediction methods, the proposed methodology is based on a multivariate pressure prediction model and utilizes a trace-by-trace multivariate regression analysis on seismic-derived petrophysical properties to calibrate model parameters in order to make accurate predictions with higher resolution in both vertical and lateral directions. With prestack time migration velocity as initial velocity model, an AVO inversion was first applied to prestack dataset to obtain high-resolution seismic velocity with higher frequency that is to be used as the velocity input for seismic pressure prediction, and the density dataset to calculate accurate Overburden Pressure (OBP). Seismic Motion Inversion (SMI) is an inversion technique based on Markov Chain Monte Carlo simulation. Both structural variability and similarity of seismic waveform are used to incorporate well log data to characterize the variability of the property to be obtained. In this research, porosity and shale volume are first interpreted on well logs, and then combined with poststack seismic data using SMI to build porosity and shale volume datasets for seismic pressure prediction. A multivariate effective stress model is used to convert velocity, porosity and shale volume datasets to effective stress. After a thorough study of the regional stratigraphic and sedimentary characteristics, a regional normally compacted interval model is built, and then the coefficients in the multivariate prediction model are determined in a trace-by-trace multivariate regression analysis on the petrophysical data. The coefficients are used to convert velocity, porosity and shale volume datasets to effective stress and then to calculate formation pressure with OBP. Application of the proposed methodology to a research area in East China Sea has proved that the method can bridge the gap between seismic and well log pressure prediction and give predicted pressure values close to pressure meassurements from well testing.

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

PubMed

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  < 0.08 and r < 0.13, for 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.

Time Series Model Identification by Estimating Information.

DTIC Science & Technology

1982-11-01

principle, Applications of Statistics, P. R. Krishnaiah , ed., North-Holland: Amsterdam, 27-41. Anderson, T. W. (1971). The Statistical Analysis of Time Series...E. (1969). Multiple Time Series Modeling, Multivariate Analysis II, edited by P. Krishnaiah , Academic Press: New York, 389-409. Parzen, E. (1981...Newton, H. J. (1980). Multiple Time Series Modeling, II Multivariate Analysis - V, edited by P. Krishnaiah , North Holland: Amsterdam, 181-197. Shibata, R

A General Multivariate Latent Growth Model with Applications to Student Achievement

ERIC Educational Resources Information Center

Bianconcini, Silvia; Cagnone, Silvia

2012-01-01

The evaluation of the formative process in the University system has been assuming an ever increasing importance in the European countries. Within this context, the analysis of student performance and capabilities plays a fundamental role. In this work, the authors propose a multivariate latent growth model for studying the performances of a…

Bayesian Estimation of Random Coefficient Dynamic Factor Models

ERIC Educational Resources Information Center

Song, Hairong; Ferrer, Emilio

2012-01-01

Dynamic factor models (DFMs) have typically been applied to multivariate time series data collected from a single unit of study, such as a single individual or dyad. The goal of DFMs application is to capture dynamics of multivariate systems. When multiple units are available, however, DFMs are not suited to capture variations in dynamics across…

Rotation in the Dynamic Factor Modeling of Multivariate Stationary Time Series.

ERIC Educational Resources Information Center

Molenaar, Peter C. M.; Nesselroade, John R.

2001-01-01

Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average.…

Modeling Associations among Multivariate Longitudinal Categorical Variables in Survey Data: A Semiparametric Bayesian Approach

ERIC Educational Resources Information Center

Tchumtchoua, Sylvie; Dey, Dipak K.

2012-01-01

This paper proposes a semiparametric Bayesian framework for the analysis of associations among multivariate longitudinal categorical variables in high-dimensional data settings. This type of data is frequent, especially in the social and behavioral sciences. A semiparametric hierarchical factor analysis model is developed in which the…

Parametric Cost Models for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2010-01-01

A study is in-process to develop a multivariable parametric cost model for space telescopes. Cost and engineering parametric data has been collected on 30 different space telescopes. Statistical correlations have been developed between 19 variables of 59 variables sampled. Single Variable and Multi-Variable Cost Estimating Relationships have been developed. Results are being published.

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments

PubMed Central

Avalappampatty Sivasamy, Aneetha; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. PMID:26357668

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments.

PubMed

Sivasamy, Aneetha Avalappampatty; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T(2) method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T(2) statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.

Predictive model for falling in Parkinson disease patients.

PubMed

Custodio, Nilton; Lira, David; Herrera-Perez, Eder; Montesinos, Rosa; Castro-Suarez, Sheila; Cuenca-Alfaro, Jose; Cortijo, Patricia

2016-12-01

Falls are a common complication of advancing Parkinson's disease (PD). Although numerous risk factors are known, reliable predictors of future falls are still lacking. The aim of this study was to develop a multivariate model to predict falling in PD patients. Prospective cohort with forty-nine PD patients. The area under the receiver-operating characteristic curve (AUC) was calculated to evaluate predictive performance of the purposed multivariate model. The median of PD duration and UPDRS-III score in the cohort was 6 years and 24 points, respectively. Falls occurred in 18 PD patients (30%). Predictive factors for falling identified by univariate analysis were age, PD duration, physical activity, and scores of UPDRS motor, FOG, ACE, IFS, PFAQ and GDS ( p -value < 0.001), as well as fear of falling score ( p -value = 0.04). The final multivariate model (PD duration, FOG, ACE, and physical activity) showed an AUC = 0.9282 (correctly classified = 89.83%; sensitivity = 92.68%; specificity = 83.33%). This study showed that our multivariate model have a high performance to predict falling in a sample of PD patients.

The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

PubMed

Hurtado Rúa, Sandra M; Mazumdar, Madhu; Strawderman, Robert L

2015-12-30

Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta-analysis example from the periodontal field and a medium meta-analysis from the study of stroke. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Studying Resist Stochastics with the Multivariate Poisson Propagation Model

DOE PAGES

Naulleau, Patrick; Anderson, Christopher; Chao, Weilun; ...

2014-01-01

Progress in the ultimate performance of extreme ultraviolet resist has arguably decelerated in recent years suggesting an approach to stochastic limits both in photon counts and material parameters. Here we report on the performance of a variety of leading extreme ultraviolet resist both with and without chemical amplification. The measured performance is compared to stochastic modeling results using the Multivariate Poisson Propagation Model. The results show that the best materials are indeed nearing modeled performance limits.

Multivariable Parametric Cost Model for Ground Optical Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2005-01-01

A parametric cost model for ground-based telescopes is developed using multivariable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction-limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature are examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e., multi-telescope phased-array systems). Additionally, single variable models Based on aperture diameter are derived.

Order-restricted inference for multivariate longitudinal data with applications to the natural history of hearing loss.

PubMed

Rosen, Sophia; Davidov, Ori

2012-07-20

Multivariate outcomes are often measured longitudinally. For example, in hearing loss studies, hearing thresholds for each subject are measured repeatedly over time at several frequencies. Thus, each patient is associated with a multivariate longitudinal outcome. The multivariate mixed-effects model is a useful tool for the analysis of such data. There are situations in which the parameters of the model are subject to some restrictions or constraints. For example, it is known that hearing thresholds, at every frequency, increase with age. Moreover, this age-related threshold elevation is monotone in frequency, that is, the higher the frequency, the higher, on average, is the rate of threshold elevation. This means that there is a natural ordering among the different frequencies in the rate of hearing loss. In practice, this amounts to imposing a set of constraints on the different frequencies' regression coefficients modeling the mean effect of time and age at entry to the study on hearing thresholds. The aforementioned constraints should be accounted for in the analysis. The result is a multivariate longitudinal model with restricted parameters. We propose estimation and testing procedures for such models. We show that ignoring the constraints may lead to misleading inferences regarding the direction and the magnitude of various effects. Moreover, simulations show that incorporating the constraints substantially improves the mean squared error of the estimates and the power of the tests. We used this methodology to analyze a real hearing loss study. Copyright © 2012 John Wiley & Sons, Ltd.

A comparison of direct and indirect methods for the estimation of health utilities from clinical outcomes.

PubMed

Hernández Alava, Mónica; Wailoo, Allan; Wolfe, Fred; Michaud, Kaleb

2014-10-01

Analysts frequently estimate health state utility values from other outcomes. Utility values like EQ-5D have characteristics that make standard statistical methods inappropriate. We have developed a bespoke, mixture model approach to directly estimate EQ-5D. An indirect method, "response mapping," first estimates the level on each of the 5 dimensions of the EQ-5D and then calculates the expected tariff score. These methods have never previously been compared. We use a large observational database from patients with rheumatoid arthritis (N = 100,398). Direct estimation of UK EQ-5D scores as a function of the Health Assessment Questionnaire (HAQ), pain, and age was performed with a limited dependent variable mixture model. Indirect modeling was undertaken with a set of generalized ordered probit models with expected tariff scores calculated mathematically. Linear regression was reported for comparison purposes. Impact on cost-effectiveness was demonstrated with an existing model. The linear model fits poorly, particularly at the extremes of the distribution. The bespoke mixture model and the indirect approaches improve fit over the entire range of EQ-5D. Mean average error is 10% and 5% lower compared with the linear model, respectively. Root mean squared error is 3% and 2% lower. The mixture model demonstrates superior performance to the indirect method across almost the entire range of pain and HAQ. These lead to differences in cost-effectiveness of up to 20%. There are limited data from patients in the most severe HAQ health states. Modeling of EQ-5D from clinical measures is best performed directly using the bespoke mixture model. This substantially outperforms the indirect method in this example. Linear models are inappropriate, suffer from systematic bias, and generate values outside the feasible range. © The Author(s) 2013.

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China

PubMed Central

Pei, Ling-Ling; Li, Qin

2018-01-01

The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China’s pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N)) model based on the nonlinear least square (NLS) method. The Gauss–Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N) model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N) and the NLS-based TNGM (1, N) models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC), and per capita emissions of SO2 and dust, alongside GDP per capita in China during the period 1996–2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N) model presents greater precision when forecasting WDPC, SO2 emissions and dust emissions per capita, compared to the traditional GM (1, N) model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO2 and dust reduce accordingly. PMID:29517985

Space-time variation of respiratory cancers in South Carolina: a flexible multivariate mixture modeling approach to risk estimation.

PubMed

Carroll, Rachel; Lawson, Andrew B; Kirby, Russell S; Faes, Christel; Aregay, Mehreteab; Watjou, Kevin

2017-01-01

Many types of cancer have an underlying spatiotemporal distribution. Spatiotemporal mixture modeling can offer a flexible approach to risk estimation via the inclusion of latent variables. In this article, we examine the application and benefits of using four different spatiotemporal mixture modeling methods in the modeling of cancer of the lung and bronchus as well as "other" respiratory cancer incidences in the state of South Carolina. Of the methods tested, no single method outperforms the other methods; which method is best depends on the cancer under consideration. The lung and bronchus cancer incidence outcome is best described by the univariate modeling formulation, whereas the "other" respiratory cancer incidence outcome is best described by the multivariate modeling formulation. Spatiotemporal multivariate mixture methods can aid in the modeling of cancers with small and sparse incidences when including information from a related, more common type of cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

Multivariate Time Series Decomposition into Oscillation Components.

PubMed

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

Multivariate meta-analysis using individual participant data

PubMed Central

Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

2016-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. PMID:26099484

Spatial occupancy models for large data sets

USGS Publications Warehouse

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.

Multivariable Parametric Cost Model for Ground Optical: Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2004-01-01

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature were examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter were derived.

A Multivariate Multilevel Approach to the Modeling of Accuracy and Speed of Test Takers

ERIC Educational Resources Information Center

Klein Entink, R. H.; Fox, J. P.; van der Linden, W. J.

2009-01-01

Response times on test items are easily collected in modern computerized testing. When collecting both (binary) responses and (continuous) response times on test items, it is possible to measure the accuracy and speed of test takers. To study the relationships between these two constructs, the model is extended with a multivariate multilevel…

Multivariate regression model for partitioning tree volume of white oak into round-product classes

Treesearch

Daniel A. Yaussy; David L. Sonderman

1984-01-01

Describes the development of multivariate equations that predict the expected cubic volume of four round-product classes from independent variables composed of individual tree-quality characteristics. Although the model has limited application at this time, it does demonstrate the feasibility of partitioning total tree cubic volume into round-product classes based on...

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

ERIC Educational Resources Information Center

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…

Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms

ERIC Educational Resources Information Center

Anderson, John R.

2012-01-01

Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…

Four Families of Multi-Variant Issues in Graduate-Level Asynchronous Online Courses

ERIC Educational Resources Information Center

Gisburne, Jaclyn M.; Fairchild, Patricia J.

2004-01-01

This is the first of several papers developed from a faculty and student perspective describing a new distance learning (DL) model. Integral to the model are four interrelated families of multi-variant issues, referred to here as (a) the academic divide, (b) student misalignment, (c) administrative influences, and (d) the use of student…

Assessing Reliability of Student Ratings of Advisor: A Comparison of Univariate and Multivariate Generalizability Approaches.

ERIC Educational Resources Information Center

Sun, Anji; Valiga, Michael J.

In this study, the reliability of the American College Testing (ACT) Program's "Survey of Academic Advising" (SAA) was examined using both univariate and multivariate generalizability theory approaches. The primary purpose of the study was to compare the results of three generalizability theory models (a random univariate model, a mixed…

Web-Based Tools for Modelling and Analysis of Multivariate Data: California Ozone Pollution Activity

ERIC Educational Resources Information Center

Dinov, Ivo D.; Christou, Nicolas

2011-01-01

This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting…

Multivariate Generalizations of Student's t-Distribution. ONR Technical Report. [Biometric Lab Report No. 90-3.

ERIC Educational Resources Information Center

Gibbons, Robert D.; And Others

In the process of developing a conditionally-dependent item response theory (IRT) model, the problem arose of modeling an underlying multivariate normal (MVN) response process with general correlation among the items. Without the assumption of conditional independence, for which the underlying MVN cdf takes on comparatively simple forms and can be…

Bias and Precision of Measures of Association for a Fixed-Effect Multivariate Analysis of Variance Model

ERIC Educational Resources Information Center

Kim, Soyoung; Olejnik, Stephen

2005-01-01

The sampling distributions of five popular measures of association with and without two bias adjusting methods were examined for the single factor fixed-effects multivariate analysis of variance model. The number of groups, sample sizes, number of outcomes, and the strength of association were manipulated. The results indicate that all five…

Effects of the 2008 Global Economic Crisis on National Health Indicators: Results from the Korean National Health and Nutrition Examination Survey

PubMed Central

Shin, Jung-Hyun; Lee, Gyeongsil; Kim, Jun-Suk; Oh, Hyung-Seok; Lee, Keun-Seung; Hur, Yong

2015-01-01

Background The relationship between economics and health has been of great interest throughout the years. The accumulated data is not sufficient enough to carry out long-term studies from the viewpoint of morbidity, although Korea National Health and Nutrition Examination Survey (KNHANES) was carried out yearly since 1998 in Korea. Thus, we investigated the effect of the 2008 global economic crisis on health indicators of Korea. Methods Health indicators were selected by paired t-test based on 2007 and 2009 KNHANES data. Age, gender, body mass index (BMI), smoking, drinking, exercise, education, income, working status, and stress were used as confounding factors, which were analyzed with logistic and probit analyses. Validation was done by comparing gross domestic product (GDP) growth rates and probit analyses results of 2007-2012 KNHANES data. Results Among several health indicators, the prevalence of hypertension and stress perception was higher after the economic crisis. Factors related with higher hypertension prevalence include older age, male gender, higher BMI, no current tobacco use, recent drinking, lower education levels, and stress perception. Factors related with more stress perception were younger age, female gender, current smoking, lower education levels, and lower income. GDP growth rates, a macroeconomic indicator, are inversely associated with hypertension prevalence with a one-year lag, and also inversely associated with stress perception without time lag. Conclusion The economic crisis increased the prevalence of hypertension and stress perception. In the case of GDP growth rate change, hypertension was an inversely lagging indicator and stress perception was an inversely-related coincident indicator. PMID:26217479

Retinal injury thresholds for 532, 578, and 630 nm lasers in connection to photodynamic therapy for choroidal neovascularization.

PubMed

Chen, Hongxia; Yang, Zaifu; Zou, Xianbiao; Wang, Jiarui; Zhu, Jianguo; Gu, Ying

2014-01-01

The purpose of this study was to explore the retinal injury thresholds in rabbits and evaluate the influence of retinal pigmentation on threshold irradiance at laser wavelengths of 532, 578, and 630 nm which might be involved in hypocrellin B (HB) and hematoporphyrin monomethyl ether (HMME) photodynamic therapy (PDT) for choroidal neovascularization (CNV). The eyes of pigmented and non-pigmented rabbits were exposed to 532, 578, and 630 nm lasers coupled to a slit lamp biological microscope. The exposure duration was 100 seconds and the retinal spot size was 2 mm throughout the experiment. The minimum visible lesions were detected by funduscopy at 1 and 24 hours post exposure. Bliss probit analysis was performed to determine the ED50 thresholds, fiducial limits and probit slope. In pigmented rabbits, the 24-hour retinal threshold irradiances at 532, 578, and 630 nm were 1,003, 1,475, and 1,720 mW/cm(2) , respectively. In non-pigmented rabbits, the 24-hour threshold irradiances were 1,657, 1,865, and 15,360 mW/cm(2) , respectively. The ED50 for 24-hour observation differed very little from the ED50 for 1-hour observation. The non-pigmented rabbits required a ninefold increase in threshold irradiance at 630 nm comparing to the pigmented rabbits. This study will contribute to the knowledge base for the limits of laser irradiance in application of HB or HMME PDT for CNV. © 2013 Wiley Periodicals, Inc.