Relevant bibliographies by topics / Regression of Proportion

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Regression of Proportion'

Author: Grafiati
Published: 8 September 2023
Last updated: 19 July 2025

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Journal articles on the topic "Regression of Proportion"

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Wang, Jin, Aizhi Sun, Qian Gao, Fuqiang Zhai, Shen Wu, and Alex A. Volinsky. "Slag material's proportion optimised by polynomial regression." Proceedings of the Institution of Civil Engineers - Construction Materials 167, no. 1 (2014): 8–13. http://dx.doi.org/10.1680/coma.12.00003.

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Milicevic, Mario, Vedran Batos, Adriana Lipovac, and Zeljka Car. "Deep Regression Neural Networks for Proportion Judgment." Future Internet 14, no. 4 (2022): 100. http://dx.doi.org/10.3390/fi14040100.

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Deep regression models are widely employed to solve computer vision tasks, such as human age or pose estimation, crowd counting, object detection, etc. Another possible area of application, which to our knowledge has not been systematically explored so far, is proportion judgment. As a prerequisite for successful decision making, individuals often have to use proportion judgment strategies, with which they estimate the magnitude of one stimulus relative to another (larger) stimulus. This makes this estimation problem interesting for the application of machine learning techniques. In regard to this, we proposed various deep regression architectures, which we tested on three original datasets of very different origin and composition. This is a novel approach, as the assumption is that the model can learn the concept of proportion without explicitly counting individual objects. With comprehensive experiments, we have demonstrated the effectiveness of the proposed models which can predict proportions on real-life datasets more reliably than human experts, considering the coefficient of determination (>0.95) and the amount of errors (MAE < 2, RMSE < 3). If there is no significant number of errors in determining the ground truth, with an appropriate size of the learning dataset, an additional reduction of MAE to 0.14 can be achieved. The used datasets will be publicly available to serve as reference data sources in similar projects.
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Han, Bing, and Nelson Lim. "Estimating conditional proportion curves by regression residuals." Statistics in Medicine 29, no. 13 (2010): 1443–54. http://dx.doi.org/10.1002/sim.3889.

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Lee, Dong-Hee. "Regression Models for Bivariate Semi-continuous Proportion Data." Korean Data Analysis Society 19, no. 2 (2017): 663–73. http://dx.doi.org/10.37727/jkdas.2017.19.2.663.

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Naik, V. D., and P. C. Gupta. "On regression method for estimating a population proportion." Statistical Papers 37, no. 1 (1996): 85–92. http://dx.doi.org/10.1007/bf02926162.

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Salsabila, Santi Wahyu, Achmad Efendi, and Nurjannah Nurjannah. "Simulation Study of Zero Inflated Negative Binomial Regression." CAUCHY: Jurnal Matematika Murni dan Aplikasi 10, no. 1 (2025): 457–68. https://doi.org/10.18860/cauchy.v10i1.32499.

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This study aims at evaluating the performance of Zero Inflated Negative Binomial (ZINB) regression analysis using the Maximum Likelihood Estimation (MLE) approach through simulation study. The research data used are secondary data and simulations. Secondary data was obtained from the Ministry of Health of the Republic of Indonesia in 2023 regarding cases of under-five deaths due to pneumonia with a total of 38 samples. The simulation study is conducted to analyze the performance of ZINB regression based on various sample sizes and proportions of zero values. The results show that the ZINB regression model with the MLE approach produces parameter estimates that tend to be more sensitive to sample size, with improved performance at large sample sizes. The ZINB regression model gives results for a large proportion of zeros and satisfies the excess zero condition. Data with a large proportion of zeros reflects high variability as well as the presence of excess zeros, so the ZINB regression model can provide more stable and precise parameter estimates than those with a lower proportion of zeros. Therefore, the ZINB regression model is effective for data with a high proportion of zeros as it fits the characteristics of the data distribution.
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Galvis, Diana M., Dipankar Bandyopadhyay, and Victor H. Lachos. "Augmented mixed beta regression models for periodontal proportion data." Statistics in Medicine 33, no. 21 (2014): 3759–71. http://dx.doi.org/10.1002/sim.6179.

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Sambhram, S. Patil. "Profitability of Indian Automobile Companies." Journal of Research and Review in Purchasing and Supply Management 2, no. 1 (2024): 40–49. https://doi.org/10.5281/zenodo.14504663.

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<em>Car industry is one of the vital areas in India and it is the advancing business in the country. The business helps the economy in numerous ways. The specialist chose 10 companies. For the investigation of benefit. The time duration of the review are from 2015 to 2024. The review utilized auxiliary information (bookkeeping information). The analytical tests involved proportional examination such as Pooled Regression, Fixed Effect Model, Random Effect Model, First - Differencing and Hausman test (Ph-test) were run and the following results were obtained., some are diminished during the review time frame, net benefit of Companies Expanded significantly. It was additionally confirmed that productivity regarding net benefit proportion, working benefit proportion, return on resources (sales revenue), Raw Material Cost and employees cost (pay) and Power and Fuel Consumption .</em> <em>All the companies mentioned in Intro. are superior during the review time frame. The consequences of Regression demonstrated that tremendous contrasts were tracked down in net benefit. proportion, return on resources and profit per divide between the example organizations and in the event that of working benefit proportion and profit from ventures, no massive contrasts were found among test organizations.</em>
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Nordmann, Tamara, Saskia Dede Davi, Michael Ramharter, and Johannes Mischlinger. "Quantification of the Proportion of Unfavorable Clinical Outcomes among Imported Malaria Patients According to the Degree of Semi-Immunity on Population Level: An Ecological Study." American Journal of Tropical Medicine and Hygiene 105, no. 2 (2021): 477–79. http://dx.doi.org/10.4269/ajtmh.21-0196.

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ABSTRACT. The protective effect of semi-immunity to alleviate clinical complications of malaria remains incompletely understood. This ecological study quantified the proportion of unfavorable clinical outcomes among patient populations with imported malaria as a function of the reported proportion of absent semi-immunity in a patient population. Group-level proportions were extracted from published studies on imported malaria. Linear regression analyses demonstrate a consistent positive trend between the average proportion of absent semi-immunity in patient populations of imported malaria and the proportion of unfavorable clinical outcomes therein. Regression equations provide a group-level estimate of attributable fractions of clinical complications resulting from absent semi-immunity to malaria.
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Shim, Jooyong, and Changha Hwang. "Geographically weighted kernel logistic regression for small area proportion estimation." Journal of the Korean Data and Information Science Society 27, no. 2 (2016): 531–38. http://dx.doi.org/10.7465/jkdi.2016.27.2.531.

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Dissertations / Theses on the topic "Regression of Proportion"

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Miyashiro, Eliane Shizue. "Modelos de regressão beta e simplex para análise de proporções." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-06112009-224039/.

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Diversos estudos compreendem a análise de variáveis definidas no intervalo (0, 1), como porcentagens ou proporções. Os modelos mais adequados são os de regressão baseados nas distribuições beta e simplex. Neste trabalho, apresentamos o modelo de regressão beta proposto por Ferrari & Cribari-Neto (2004) e desenvolvemos o modelo de regressão simplex. Definimos um resíduo para o modelo de regressão simplex, muito útil na análise de diagnóstico, a partir do trabalho de Espinheira, Ferrari & Cribari-Neto (2008). Apresentamos uma forma geral para algumas medidas de diagnóstico, que podem ser aplicadas para os dois modelos. Avaliamos os modelos de regressão beta e simplex por meio de duas aplicações a dados reais, utilizando essas medidas.<br>Many studies consider the analysis of variables restricted to the interval (0, 1), as percentages and proportions. The most recommended models are based upon the beta and simplex distributions. In this work, we present the beta regression model proposed by Ferrari and Cribari-Neto (2004) and develop the simplex regression model. We propose a residual for the simplex regression model, which is very useful for the diagnostic analysis, based upon the work of Espinheira et al. (2008). We generalize some diagnostic techniques that can be applied to both models. We evaluate the beta and simplex models by two applications to real data, using those techniques.
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Forslind, Fanni. "The Effect of Immigration on Income Distribution : A Comparative Study of Ordinary Least Squares and Beta Regression." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-433098.

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The purpose of this study is to estimate the relationship between income inequality and immigration in Sweden. To do so, data from the data base Kolada with observations from all 290 municipalities in Sweden is used. As a proxy for income distribution the Gini coefficient is used and as a proxy for immigration the share of foreign born of working age is used. The model also controls for income tax, education level and unemployment level. The dependent variable the Gini coefficient is bounded by a unit interval and it is therefore not possible to simply run a linear regression. Such a model could potentially predict outside the interval. To properly estimate the relationship two approaches are made. Firstly a model is estimated with Ordinary Least Squares (OLS) after the dependent variable is transformed on to the real line through log-odds. Then a model is estimated using beta regression. The study concludes that there is a statistically significant positive correlation between income inequality and immigration in Sweden. The OLS estimated model shows that a 1 unit increase in immigration, on average increases the log-odds of 0.28336 units, ceteris paribus. Beta regression provides perhaps more intuitive results. If immigration increases with 1% the income inequality increases with on average 0.1046%, ceteris paribus. Because of the easier interpretation, among other things, beta regression is determined to be a better estimation method in this study.
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Cribari-Neto, Francisco, and Achim Zeileis. "Beta Regression in R." Department of Statistics and Mathematics x, WU Vienna University of Economics and Business, 2009. http://epub.wu.ac.at/726/1/document.pdf.

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The class of beta regression models is commonly used by practitioners to model variables that assume values in the standard unit interval (0, 1). It is based on the assumption that the dependent variable is beta-distributed and that its mean is related to a set of regressors through a linear predictor with unknown coefficients and a link function. The model also includes a precision parameter which may be constant or depend on a (potentially different) set of regressors through a link function as well. This approach naturally incorporates features such as heteroskedasticity or skewness which are commonly observed in data taking values in the standard unit interval, such as rates or proportions. This paper describes the betareg package which provides the class of beta regressions in the R system for statistical computing. The underlying theory is briefly outlined, the implementation discussed and illustrated in various replication exercises.<br>Series: Research Report Series / Department of Statistics and Mathematics
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LEAL, ALTURO Olivia Lizeth. "Nonnested hypothesis testing inference in regression models for rates and proportions." Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/24573.

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Submitted by Alice Araujo (alice.caraujo@ufpe.br) on 2018-05-07T21:17:28Z No. of bitstreams: 1 DISSERTAÇÃO Olivia Lizeth Leal Alturo.pdf: 2450256 bytes, checksum: 8d29b676eaffcb3c5bc1b78a8611b9f8 (MD5)<br>Made available in DSpace on 2018-05-07T21:17:28Z (GMT). No. of bitstreams: 1 DISSERTAÇÃO Olivia Lizeth Leal Alturo.pdf: 2450256 bytes, checksum: 8d29b676eaffcb3c5bc1b78a8611b9f8 (MD5) Previous issue date: 2017-02-16<br>Existem diferentes modelos de regressão que podem ser usados para modelar taxas, proporções e outras variáveis respostas que assumem valores no intervalo unitário padrão, (0,1). Quando só uma classe de modelos de regressão é considerada, a seleção do modelos pode ser baseada nos testes de hipóteses usuais. O objetivo da presente dissertação é apresentar e avaliar numericamente os desempenhos em amostras imitas de testes que podem ser usados quando há dois ou mais modelos que são plausíveis, são não-encaixados e pertencem a classes de modelos de regressão distintas. Os modelos competidores podem diferir nos regressores que utilizam, nas funções de ligação e/ou na distribuição assumida para a variável resposta. Através de simulações de Monte Cario nós estimamos as taxas de rejeição nulas e não-nulas dos testes sob diversos cenários. Avaliamos também o desempenho de um procedimento de seleção de modelos. Os resultados mostram que os testes podem ser bastante úteis na escolha do melhor modelo de regressão quando a variável resposta assume valores no intervalo unitário padrão.<br>There are several different regression models that can be used with rates, proportions and other continuous responses that assume values in the standard unit interval, (0,1). When only one class of models is considered, model selection can be based on standard hypothesis testing inference. In this dissertation, we develop tests that can be used when the practitioner has at his/her disposal more than one plausible model, the competing models are nonnested and possibly belong to different classes of models. The competing models can differ in the regressors they use, in the link functions and even in the response distribution. The finite sample performances of the proposed tests are numerically eval-uated. We evaluate both the null and nonnull behavior of the tests using Monte Cario simulations. The results show that the tests can be quite useful for selecting the best regression model when the response assumes values in the standard unit interval.
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Persson, Inger. "Essays on the Assumption of Proportional Hazards in Cox Regression." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2002. http://publications.uu.se/theses/91-554-5208-6/.

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Crumer, Angela Maria. "Comparison between Weibull and Cox proportional hazards models." Kansas State University, 2011. http://hdl.handle.net/2097/8787.

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Master of Science<br>Department of Statistics<br>James J. Higgins<br>The time for an event to take place in an individual is called a survival time. Examples include the time that an individual survives after being diagnosed with a terminal illness or the time that an electronic component functions before failing. A popular parametric model for this type of data is the Weibull model, which is a flexible model that allows for the inclusion of covariates of the survival times. If distributional assumptions are not met or cannot be verified, researchers may turn to the semi-parametric Cox proportional hazards model. This model also allows for the inclusion of covariates of survival times but with less restrictive assumptions. This report compares estimates of the slope of the covariate in the proportional hazards model using the parametric Weibull model and the semi-parametric Cox proportional hazards model to estimate the slope. Properties of these models are discussed in Chapter 1. Numerical examples and a comparison of the mean square errors of the estimates of the slope of the covariate for various sample sizes and for uncensored and censored data are discussed in Chapter 2. When the shape parameter is known, the Weibull model far out performs the Cox proportional hazards model, but when the shape parameter is unknown, the Cox proportional hazards model and the Weibull model give comparable results.
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Maier, Marco J. "DirichletReg: Dirichlet Regression for Compositional Data in R." WU Vienna University of Economics and Business, 2014. http://epub.wu.ac.at/4077/1/Report125.pdf.

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Dirichlet regression models can be used to analyze a set of variables lying in a bounded interval that sum up to a constant (e.g., proportions, rates, compositions, etc.) exhibiting skewness and heteroscedasticity, without having to transform the data. There are two parametrization for the presented model, one using the common Dirichlet distribution's alpha parameters, and a reparametrization of the alpha's to set up a mean-and-dispersion-like model. By applying appropriate link-functions, a GLM-like framework is set up that allows for the analysis of such data in a straightforward and familiar way, because interpretation is similar to multinomial logistic regression. This paper gives a brief theoretical foundation and describes the implementation as well as application (including worked examples) of Dirichlet regression methods implemented in the package DirichletReg (Maier, 2013) in the R language (R Core Team, 2013). (author's abstract)<br>Series: Research Report Series / Department of Statistics and Mathematics
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Pereira, Gustavo Henrique de Araujo. "Modelos de regressão beta inflacionados truncados." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-14082012-123751/.

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Os modelos de regressão beta e beta inflacionados conseguem ajustar adequadamente grande parte das variáveis do tipo proporção. No entanto, esses modelos não são úteis quando a variável resposta não pode assumir valores no intervalo (0,c) e assume o valor c com probabilidade positiva. Variáveis relacionadas a algum tipo de pagamento limitado entre dois valores, quando estudadas em relação ao seu valor máximo, possuem essas características. Para ajustar essas variáveis, introduzimos a distribuição beta inflacionada truncada (BIZUT), que é uma mistura de uma distribuição beta com suporte no intervalo (c,1) e uma distribuição trinomial que assume os valores zero, um e c. Propomos ainda um modelo de regressão para as situações em que a variável resposta tem distribuição BIZUT. Admitimos que todos os parâmetros da distribuição podem variar em função de variáveis preditoras. Além disso, o modelo permite que o parâmetro conhecido c varie entre as unidades populacionais. Para esse modelo são desenvolvidos diversos aspectos inferenciais, são obtidos resultados para as situações em que c é variável e são conduzidos estudos de simulação de Monte Carlo. Além disso, discutimos análise de resíduos, desenvolvemos análise de influência local e realizamos uma aplicação a dados reais de cartão de crédito.<br>The beta regression model or the inflated beta regression model may be a reasonable choice to fit a proportion in most situations. However, they do not fit well variables that do not assume values in the open interval (0,c), 0 < c < 1 and assume the c value with positive probability. Variables related to a kind of double bounded payment amount when studied as a proportion of the maximum payment amount have this feature. For these variables, we introduce the truncated inflated beta distribution (TBEINF). This proposed distribution is a mixture of the beta distribution bounded in the open interval (c,1) and a trinomial distribution that assumes the values zero, one and c. This work also proposes a regression model where the response variable is TBEINF distributed. The model allows all the unknown parameters of the conditional distribution of the response variable to be modeled as functions of explanatory variables. Moreover, the model allows nonconstant known parameter c across population units. For this model, some inferential aspects are developed, some results when c is not constant are obtained and Monte Carlo simulation studies are performed. In addition, residual and local influence analysis are discussed and an application to credit card data is presented.
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Galvis, Soto Diana Milena 1978. "Bayesian analysis of regression models for proportional data in the presence of zeros and ones = Análise bayesiana de modelos de regressão para dados de proporções na presença de zeros e uns." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306682.

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Orientador: Víctor Hugo Lachos Dávila<br>Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica<br>Made available in DSpace on 2018-08-26T02:34:17Z (GMT). No. of bitstreams: 1 GalvisSoto_DianaMilena_D.pdf: 1208980 bytes, checksum: edbc193912a2a800da4936526ed79fa3 (MD5) Previous issue date: 2014<br>Resumo: Dados no intervalo (0,1) geralmente representam proporções, taxas ou índices. Porém, é possível observar situações práticas onde as proporções sejam zero e/ou um, representando ausência ou presença total da característica de interesse. Nesses casos, os modelos que analisam o efeito de covariáveis, tais como a regressão beta, beta retangular e simplex não são convenientes. Com o intuito de abordar este tipo de situações, considera-se como alternativa aumentar os valores zero e/ou um ao suporte das distribuições previamente mencionadas. Nesta tese, são propostos modelos de regressão de efeitos mistos para dados de proporções aumentados de zeros e uns, os quais permitem analisar o efeito de covariáveis sobre a probabilidade de observar ausência ou presença total da característica de interesse, assim como avaliar modelos com respostas correlacionadas. A estimação dos parâmetros de interesse pode ser via máxima verossimilhança ou métodos Monte Carlo via Cadeias de Markov (MCMC). Nesta tese, será adotado o enfoque Bayesiano, o qual apresenta algumas vantagens em relação à inferência clássica, pois não depende da teoria assintótica e os códigos são de fácil implementação, através de softwares como openBUGS e winBUGS. Baseados na distribuição marginal, é possível calcular critérios de seleção de modelos e medidas Bayesianas de divergência q, utilizadas para detectar observações discrepantes<br>Abstract: Continuous data in the unit interval (0,1) represent, generally, proportions, rates or indices. However, zeros and/or ones values can be observed, representing absence or total presence of a carachteristic of interest. In that case, regression models that analyze the effect of covariates such as beta, beta rectangular or simplex are not appropiate. In order to deal with this type of situations, an alternative is to add the zero and/or one values to the support of these models. In this thesis and based on these models, we propose the mixed regression models for proportional data augmented by zero and one, which allow analyze the effect of covariates into the probabilities of observing absence or total presence of the interest characteristic, besides of being possivel to deal with correlated responses. Estimation of parameters can follow via maximum likelihood or through MCMC algorithms. We follow the Bayesian approach, which presents some advantages when it is compared with classical inference because it allows to estimate the parameters even in small size sample. In addition, in this approach, the implementation is straightforward and can be done using software as openBUGS or winBUGS. Based on the marginal likelihood it is possible to calculate selection model criteria as well as q-divergence measures used to detect outlier observations<br>Doutorado<br>Estatistica<br>Doutora em Estatística
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Johnson, Edward P. "Applying Bayesian Ordinal Regression to ICAP Maladaptive Behavior Subscales." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2121.pdf.

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Books on the topic "Regression of Proportion"

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O'Quigley, John. Proportional Hazards Regression. Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-68639-4.

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Tapia-Aguilar, Alberto. Accurate confidence intervals for regression parameters in proportional hazards model. [s.n.], 1994.

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Spray, Judith A. Comparison of loglinear and logistic regression models for detecting changes in proportions. American College Testing Program, 1988.

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LeBlanc, Michael R. Step-function covariate effects in the proportional hazards model. University of Toronto, Department of Statistics, 1993.

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Consortium for Mathematics and Its Applications (U.S.), Chedd-Angier Production Company, American Statistical Association, and Annenberg Media, eds. Against all odds--inside statistics: Disc 3, programs 9-12. Annenberg Media, 2011.

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O'Quigley, John. Proportional Hazards Regression. Springer New York, 2010.

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O'Quigley, John. Survival Analysis: Proportional and Non-Proportional Hazards Regression. Springer, 2021.

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O'Quigley, John. Survival Analysis: Proportional and Non-Proportional Hazards Regression. Springer International Publishing AG, 2022.

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Proportional Hazards Regression (Statistics for Biology and Health). Springer, 2008.

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O'Quigley, John. Proportional Hazards Regression (Statistics for Biology and Health). Springer, 2008.

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Book chapters on the topic "Regression of Proportion"

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García Portilla, Jason. "Component 1 (Macro): Quantitative (Regression) Analysis." In “Ye Shall Know Them by Their Fruits”. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78498-0_15.

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AbstractThis chapter corresponds to the macro-quantitative component. It discusses how competitiveness and corruption were modelled, how the methods were applied in the correlation analysis and which empirical results were achieved.Regression analysis is used to test the relationship between variables of some prominent prosperity/corruption theories. This part does not seek to prove causation, but instead empirically explores whether competitiveness/transparency are related to indicators such as state religion or a population’s religious affiliation.Consistent results of the models on competitiveness (GCI) are: (1) a positive influence of EPI on GCI; (2) a positive influence of a German legal origin (or German language) on GCI; (3) a negative influence of an Orthodox population on GCI; and (4) a negative influence of a Roman Catholic population (or Roman Catholic State Religion) on GCI. These results are also consistent with the predictions in the theory chapters (Chaps. 10.1007/978-3-030-78498-0_6–10.1007/978-3-030-78498-0_11).The corruption model applied here tests the interrelations between GDP, political liberties (democracy proxy), and language and ethnic fractionalisation. The results of the models on corruption are entirely compatible with theory. The results confirm my hypothesis that transparency levels are directly (i.e. positively) related to the proportion of Protestants in countries in Europe and the Americas.
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Guo, Jiaxu, Shaowei Hu, Xuan Zhao, Xiu Tao, and Ying Nie. "Compressive Strength Performance of Additives for Cement-Based Grouting Material with Low Water-Binder Ratio by Response Surface Methodology." In Lecture Notes in Civil Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1260-3_34.

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AbstractIn order to research the influence and the function mechanism of calcium formate and defoaming agent on the compressive strength of cement-based grouting material with low water-binder ratio at different ages, quadratic polynomial regression models were established by RSM, and the mix proportion was optimized. The function mechanism of additives was analysed by macroscopic mechanical properties and microstructure. The results indicated that the response surface method is scientific in optimizing the mix proportion of cement-based grouting material. The optimal mix proportion was obtained as fallow: the calcium formate was 0.64%, the water-binder ratio was 0.21 and the defoaming agent was 0.26%, with taking 1d, 3d, 28d compressive strength as the optimization objective. Calcium formate is highly significant for the early compressive strength of cement-based grouting materials with low water-binder ratio, while the water-binder ratio and defoaming agent are highly significant for that of the middle and late period. Calcium formate promotes the formation of CSH gel and $$\mathrm{Ca}{\left(\mathrm{OH}\right)}_{2}$$ Ca OH 2 crystallization in the early period, and the defoaming agent can effectively reduce macropores. The results can provide an optimization method for the mix proportion design of cement-based grouting material and a theoretical reference for its mechanical properties.
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Harrell, Frank E. "Cox Proportional Hazards Regression Model." In Regression Modeling Strategies. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19425-7_20.

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Harrell, Frank E. "Cox Proportional Hazards Regression Model." In Regression Modeling Strategies. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3462-1_19.

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Wang, Wei, and Chengcheng Hu. "Proportional Hazards Regression Models." In Springer Handbook of Engineering Statistics. Springer London, 2006. http://dx.doi.org/10.1007/978-1-84628-288-1_21.

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Matthews, David Edward, and Vernon Todd Farewell. "13 Proportional Hazards Regression." In Using and Understanding Medical Statistics. KARGER, 2007. http://dx.doi.org/10.1159/000099428.

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Carter, Walter H., Galen L. Wampler, and Donald M. Stablein. "Proportional Hazards Analysis." In Regression Analysis of Survival Data in Cancer Chemotherapy. CRC Press, 2024. http://dx.doi.org/10.1201/9781003573531-3.

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Riyanti, Novi Dwi, Werner R. Murhadi, and Mudji Utami. "The Influence of Good Corporate Governance through the Gender Diversity on Firm Performance." In Proceedings of the 19th International Symposium on Management (INSYMA 2022). Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-008-4_2.

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AbstractThe objective of this study is to look at how board size, board gender diversity, independent commissioners, leverage, and firm size affect firm performance in manufacturing companies in Indonesia Stock Exchange and the Philippines Stock Exchange. This study used a quantitative approach with two least square regression analysis models and a sample of manufacturing sector companies listed on the Indonesia Stock Exchange and the Philippines Stock Exchange, with a total of 100 business entities and 480 observations. This study used the presence of female commissioners and the proportion of female commissioners to measure board gender diversity. According to the findings of this study, independent commissioners in Indonesia have a significantly positive effect on return on assets, female commissioners in Indonesia have a significant adverse effect on return on assets, and Tobin’s Q, both leverage in Indonesia and firm size in the Philippines have a significant adverse effect on return on assets. Meanwhile, leverage in Indonesia has a significant negative effect on Tobin’s Q, as does board size in Indonesia and firm size in Indonesia and the Philippines.
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Broström, Göran. "Proportional Hazards and Cox Regression." In Event History Analysis with R, 2nd ed. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429503764-3.

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Zang, Wanjun, and Jiang Wen. "Analysis of Slurry Ratio of Rotary Digging Pile in Deep Sand Layer." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1748-8_11.

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AbstractSlurry ratio is a crucial link in the construction of bored pile, which directly determines the quality of bored pile. In order to determine the key performance parameters of the slurry required to form piles in the deep sand layer, relying on Huizhou north station engineering, an orthogonal test of slurry proportioning was designed and carried out, and SPSS statistical analysis software was used to carry out bivariate correlation analysis and multivariate stepwise analysis of the test results, combined with the slurry performance index test regression equation and using MATLAB software optimization processing, slurry optimal mix ratio and verify, the research results show that: orthogonal test screening value, software calculation value, test value is not different. Conclusion: The results show that bentonite and CMC have significant influence on slurry indexes, while Na2CO3 and PHP can adjust slurry performance to meet the slurry use standard; the optimal mix ratio is 148 g bentonite, Na2CO3 5.2 g, CMC 3.5 g, PHP 0.05 g; the slurry ratio test analysis and treatment, and the optimization mix ratio is feasible and reasonable, class I pile proportion more than 98% to meet the actual engineering requirements.
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Conference papers on the topic "Regression of Proportion"

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Girard-Jollet, J., L. Shi, F. Boitier, and P. Layec. "Nonlinearity Estimation Leveraging PSD-based Monitoring and Machine Learning." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.m2e.2.

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We propose a regression model to estimate Kerr nonlinearity proportion in fiber optic transmissions. Trained on simulations and validated experimentally, the model achieves a 4% RMSE across varying power profiles, CPE parameters, and transmission reaches.
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Futas, Peter, Alena Pribulova, Jozef Petrik, Peter Peter Blasko, and Maria Mihalikova. "USE OF REGRESSION ANALYSIS AND SIMULATION TO OPTIMIZE THE COMPOSITION OF THE CAST IRON CHARGE." In 24th SGEM International Multidisciplinary Scientific GeoConference 24. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024/1.1/s04.58.

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Cast iron GG30 (EN-GJL-300C) has excellent mechanical properties and good machinability. It can be used in many industries such as hydraulics, engineering, automotive, oil and gas mining, and processing. It is suitable for the production of compressor and pump components. Return material, technological residue, iron scrap, fractional cast iron, liquid metal, and alloying components can be used as input raw materials in the production of cast iron, the first three were taken into account in the analysis. In practice, it is possible to choose their different ratio. At the same time, their chemical composition is somewhat variable. The aim of the paper is to compare two models, LINEST and DoE, which, with the help of regression analysis, determine the influence of these input raw materials (factors) on the mechanical properties: ultimate strength Rm, hardness HB of the final cast iron. The equations obtained will enable the calculation of the appropriate ratio of these three input raw materials to ensure the required mechanical properties of the final product. The equations obtained using DoE correspond more closely to the measured values than the equations obtained by LINEST, their calculation is more complex and requires the use of software. Based on these equations, taking into account the variability of the input data (for example, the fluctuation of the chemical composition of the iron scrap used), it is possible to estimate the proportion of castings with the required mechanical properties. For example, with a charge containing 40 % of technological residue, 22 % of returnable material, and 38 % of iron scrap with a variability of � 10 %, it will meet the requirements of the standard for tensile strength Rm of 99.85 % of castings and hardness HB of 81.88 % of castings.
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Cohen, Benjamin G., Burcu Beykal, and George M. Bollas. "Selection of Fitness Criteria for Learning Interpretable PDE Solutions via Symbolic Regression." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.199083.

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Physics-Informed Symbolic Regression (PISR) offers a pathway to discover human-interpretable solutions to partial differential equations (PDEs). This work investigates three fitness metrics within a PISR framework: PDE fitness, Bayesian Information Criterion (BIC), and a fitness metric proportional to the probability of a model given the data. Through experiments with Laplace�s equation, Burgers� equation, and a nonlinear wave equation, we demonstrate that incorporating information theoretic criteria like BIC can yield higher fidelity models while maintaining interpretability. Our results show that BIC-based PISR achieved the best performance, identifying an exact solution to Laplace�s equation and finding solutions with R2-values of 0.998 for Burgers� equation and 0.957 for the nonlinear wave equation. The inclusion of the Bayes D-optimality criterion in estimating model probability strongly constrained solution complexity, limiting models to 3-4 parameters and reducing accuracy. These findings suggest that a two-stage approach-using simpler complexity metrics during initial solution discovery followed by a post-hoc identifiability analysis may be optimal for discovering interpretable and mathematically identifiable PDE solutions.
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Li, Lianfang, and William Hartt. "The Role of Indium and Zinc in Activation of Aluminum in NaCl Solutions and in Seawater." In CORROSION 2002. NACE International, 2002. https://doi.org/10.5006/c2002-02019.

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Abstract The pitting and repassivation potentials (Ep and Er, respectively) of 1) Al (99.995% purity), 2) an Al-Zn and Al-In alloy, 3) a commercial Al-5.17 w/o Zn-0.017 w/o In anode, and 4) a built-up, arc sprayed 20 w/o Zn-0.2 w/o In alloy were determined using the cyclic potentiodynamic polarization technique. Also determined were current-time trends of Al in 0.5M NaCl solution with and without 0.005M In2(SO4)3 under potentiostatic polarization in the potential range from –0.70V to -1.20VSCE. A multiple linear regression model revealed that Ep and Er were linearly proportional to the individual contents of In or Zn in Al, with In having a larger weight factor than Zn, especially for Er in seawater. Potentiostatic polarization tests confirmed the effectiveness of In ions in 0.5M NaCl solution for activating Al. Corroded Al and Al-Zn-In specimens were examined with SEM and EDAX. The results are discussed with regard to 1) the mechanism of activation and 2) anode performance for marine cathodic protection service.
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Castro-Amoedo, Rafael, Alessio Santecchia, Henrique A. Matos, and Fran�ois Mar�chal. "Real-time carbon accounting and forecasting for reduced emissions in grid-connected processes." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.109753.

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Real-time carbon accounting is crucial for advancing policies that effectively meet sustainability objectives. This work introduces a carbon tracking tool specifically designed for the European electricity grid. The tool collects hourly data on electricity consumption and generation, cross-border power exchanges, and weather information to assess the real-time environmental effects of electricity use, employing locally-specific emission factors for the generation sources. It utilizes weather data from various stations across Europe to produce week-ahead forecasts of carbon intensity in the grid. Predictions are created using a random forest regressor, integrated within the optimal controller of an operational industrial batch process. This prediction-based optimizer seeks to reduce total emissions tied to the process schedule's electricity consumption by implementing a rolling horizon strategy. By leveraging enhanced energy flexibility, the controller provides significant opportunities for load shifting and emission reductions, though at a higher cost. An industrial batch process demonstration highlights the potential of this approach, showing environmental savings ranging from 4% in grids with low renewable energy to 32% in nations that incorporate a substantial proportion of renewable energy in their energy mix.
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Center, Julian L., Kevin H. Knuth, Ariel Caticha, Julian L. Center, Adom Giffin, and Carlos C. Rodríguez. "Regression for Proportion Data." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING. AIP, 2007. http://dx.doi.org/10.1063/1.2821266.

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Zhang, Bin, and Shoucheng Yuan. "Analysis of Affecting Factors of Proportion of Tertiary Industry Based on Ridge Regression." In 2018 2nd International Conference on Management, Education and Social Science (ICMESS 2018). Atlantis Press, 2018. http://dx.doi.org/10.2991/icmess-18.2018.66.

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Diz-Rosales, Naomi, María José Lombardía, and Domingo Morales. "Mapping the Poverty Proportion in Small Areas under Random Regression Coefficient Poisson Models." In Congreso XoveTIC: impulsando el talento científico (6º. 2023. A Coruña). Servizo de Publicacions. Universidade da Coruña, 2023. http://dx.doi.org/10.17979/spudc.000024.18.

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In a complex socio-economic context, policy makers need highly disaggregated poverty indicators. In this work, we develop a methodology in small area estimation to derive predictors of poverty proportions under a random regression coefficient Poisson model, introducing bootstrap estimators of mean squared errors. Maximum likelihood estimators of model parameters and random effects mode predictors are calculated using a Laplace approximation algorithm. Simulation experiments are conducted to investigate the behaviour of the fitting algorithm, the predictors and the mean squared error estimator. The new statistical methodology is applied to data from the Spanish survey of living conditions to map poverty proportions by province and sex, developing a tool to support policy decision making
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Ojha, Varun Kumar, Paramartha Dutta, Hiranmay Saha, and Sugato Ghosh. "Linear regression based statistical approach for detecting proportion of component gases in manhole gas mixture." In 2012 1st International Symposium on Physics and Technology of Sensors (ISPTS). IEEE, 2012. http://dx.doi.org/10.1109/ispts.2012.6260865.

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Rejivas, V. A., A. Praveen, and T. Ajitha. "Determination of Collinearity Developed in the CMB Model with the Concepts of Multi Linear Regression Analysis." In 2nd International Conference on Modern Trends in Engineering Technology and Management. AIJR Publisher, 2023. http://dx.doi.org/10.21467/proceedings.160.12.

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The Chemical Mass Balance model (CMB) gives an accurate source apportionment for the contribution of the sources with the input data of the source profile and receptor data collected. The source profiles for different sources should have a unique and specific species characterization for getting accurate source apportionment results. But due to the mixing of sources, the species characterization source profile may not have unique and specific species characterization due to the non-availability of the exact representation of particular sources and culminates collinearity of species during the CMB analysis. It leads to negative source apportionment results in the CMB analysis. Multi Linear Regression analysis that addresses in the study can effectively be used to identify the collinearity contributing sources. The Multi Linear Regression parameters such as tolerance, variance inflation factor (VIF), condition index, and variance decomposition proportions developed with the source profile variables (source profiles for soil, paved road dust, biomass, and traffic) are used for identifying the collinear sources. The tolerance value for the soil and paved road dust sources are obtained as 0.001 each and the variance inflation factor (VIF) for both are obtained as 204.2 and 208.8 respectively. It indicates the collinearity between soil and paved road dust. Collinearity diagnostics of the regression equations showed that the condition index and the variance decomposition proportion obtained for the soil and paved road dust were greater than 30 (104.09) and 90% (100%) respectively. Therefore, the presence of strong collinearity between soil and paved road dust can be understood.
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Reports on the topic "Regression of Proportion"

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Koenker, Roger, and Naveen Narisetty. Censored quantile regression survival models with a cure proportion. The IFS, 2019. http://dx.doi.org/10.1920/wp.cem.2019.5619.

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Puttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante, and Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, 2020. http://dx.doi.org/10.22617/wps200434-2.

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This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. It also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of population living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered, perhaps due to its capability to fit complex association structures even with small and medium-sized datasets.
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Tangka, Florence K. L., Sujha Subramanian, Madeleine Jones, et al. Young Breast Cancer Survivors: Employment Experience and Financial Well-Being. RTI Press, 2020. http://dx.doi.org/10.3768/rtipress.2020.rr.0041.2007.

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The economic burden of breast cancer for women under 50 in the United States remains largely unexplored, in part because young women make up a small proportion of breast cancer cases overall. To address this knowledge gap, we conducted a web-based survey to compare data from breast cancer survivors 18–39 years of age at first diagnosis and 40–49 years of age at first diagnosis. We administered a survey to a national convenience sample of 416 women who were 18–49 years of age at the time of their breast cancer diagnosis. We analyzed factors associated with financial decline using multivariate regression. Survivors 18–39 years of age at first diagnosis were more likely to report Stage II–IV breast cancer (P&lt;0.01). They also quit their jobs more often (14.6%) than older survivors (4.4%; P&lt;0.01) and faced more job performance issues (55.7% and 42.8%, respectively; P=0.02). For respondents in both groups, financial decline was more likely if the survivor had at least one comorbid condition (odds ratios: 2.36–3.21) or was diagnosed at Stage II–IV breast cancer (odds ratios: 2.04–3.51).
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O'Toole, Conor, and Rachel Slaymaker. Household size in Ireland: Stylised facts and cross country trends. ESRI, 2024. http://dx.doi.org/10.26504/qec2024sum_sa_otoole.

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This paper uses cross-country data to explore the trends in household size in Ireland and to place these trends in a European context. Using cross country data from the EU-SILC survey, the research presents a range of stylised facts regarding how Ireland differs from other countries and attempts to explore what might explain the variation. We find that Ireland has a high average household size on a cross-country basis. However, this appears to be strongly influenced by demographics, with high fertility rate, younger population and thus high share of households with children important factors in explaining the cross country trends. Indeed, a majority of the differences between Ireland and other countries disappear in a regression setting when socio-demographic and basic economic factors are controlled for. In terms of the change over time in household size, we find little association with time-varying economic factors but, again, a strong effect of aging and the proportion of households with children. We do find a negative relationship with housing supply; any change in household size would be, to a degree, affected by availability of housing. In terms of the long run trend in Irish household size, the level is likely to change in line with population aging i.e. demographic factors are likely to be the most important driver going forward, subject to availability. Projections for future household numbers or housing demand would likely benefit from the deployment of age specific household size trends which allow natural population dynamics to influence household size.
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Orbeta, Aniceto Jr, and Maropsil Potestad. On the Employability of the Senior High School Graduates: Evidence from the Labor Force Survey. Philippine Institute for Development Studies, 2020. https://doi.org/10.62986/dp2020.40.

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Continuing from the Institutes 2018 study on the SHS and the labor market which interviewed graduating SHS students and HR managers of firms about their perspectives on the prospects for employment, this study intends to gauge the actual performance of the SHS graduates in the labor market. Entry into the labor market is considered as one of the possible exits of SHS graduates. Given the first batch of SHS graduates in April 2018, this study utilizes eight rounds of Labor Force Survey (LFS) from July 2018 to April 2020. Labor market outcomes of SHS graduates (G12) are compared to their peers - G10 and second-year college completers. To improve comparability, the paper uses a multi-valued treatment effect estimation (i.e., Inverse Probability Weighting with Regression Adjustment (IPWRA)) with each of the education outcome as treatment and utilizing individual and household characteristics. The results confirm the results in the earlier study that only a small proportion (a little over 20%) enter the labor force and most of them (more than 70 percent) continue with their education. The estimations also find a mixed result with SHS graduates sometimes performing better and sometimes poorer relative to their peers in terms of labor market outcomes. These suggest that there is a need to re-examine employment and entrepreneurial objective SHS given the expressed and realized propensity of SHS graduates to continue with their education rather than entering the labor market. There is also a need to continue to examine and validate the effectiveness the work preparation component of the SHS curriculum. Furthermore, there is a need to continue working with employers informing and demonstrating to them what SHS graduates can do and eventually clearly defining the right niche for them in the labor market. Finally, this calls for continued close monitoring and generating empirical evidence to help understand the role of SHS graduates in the labor market.
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Sim, Sokcheng, Socheat Keo, and Molideth Sarom. Pesticide Use Practices in Cambodia’s Vegetable Farming. Cambodia Development Resource Institute, 2021. https://doi.org/10.64202/wp.128.202108.

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Pesticides are agricultural technologies that farmers use to control pests and weeds and remain an important modern input for crop production including vegetable farming. There are many types of pesticides, such as insecticides, fungicides, rodenticides and herbicides, that target different threats to crops. While the potential production benefits of chemical pesticides are undeniable, people are becoming more aware of their risks. There is an array of dangers associated with inappropriate pesticide use. As pesticides are a poison, they pose inherent health risks to the farmers exposed to them. Inappropriate pesticide use has been linked with pesticide residues in or on food above maximum residue levels (MRLs), the safe amount of residue allowed, which can cause a number of health effects in those who consume the products. Our survey data reveals that pests and diseases are the biggest challenge Cambodian vegetable farmers face. Pesticides/herbicides account for the largest share in vegetable production costs in our study areas, suggesting that chemical pesticides are commonly used in vegetable farming in Cambodia, particularly our study areas which are the main producers of vegetables in the country. Additionally, it is common that farmers mix various types of pesticides per spray which is not good practice. Applying ordinary least squares regression and probit model, we investigated the factors that facilitate or impede pesticide use practices. The results show that lower use of pesticide is associated with age of farmers in charge of pesticide spraying, educational attainment, female farmer, and varied by locations. At the same time, there is a significant link between the use of large quantities of pesticide and farmers’ misperception of pesticide use practices and the proportion of pesticide spending in total input costs. Apart from this, knowledge/advice about pest management/control farmers receive from their peers and pesticide stores, household participation in social groups such as agricultural cooperatives, and farm size are positively correlated with the probability that a farmer will comply with recommended pesticide doses. These results imply that modifying farmers’ attitudes towards pesticide use and promoting the role of women in vegetable pest management are among the important interventions to reduce pesticide dependence.
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McKee, Anna, and Ann Couch. Land use and dog park associations with Escherichia coli in the Chattahoochee River National Recreation Area watershed. National Park Service, 2024. http://dx.doi.org/10.36967/2302755.

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A recent study in the Chattahoochee River National Recreation Area (CHAT) indicated that dogs were a primary source of fecal contamination in the Chattahoochee River and that at least some of the contamination in the river was coming from locations outside of CHAT. The study herein sought to determine if dog parks in the CHAT watershed were sources of dog fecal contamination in streams within the watershed. Escherichia coli (E. coli) data were compiled from the Chattahoochee Riverkeeper Neighborhood Water Watch (NWW) program for sites within the CHAT watershed. Information about dog park locations within the Atlanta metropolitan area was compiled through online searches. Wilcoxon rank-sum tests, forward stepwise linear regression, and Spearman rank correlations were used to investigate the relations between seasonal E. coli levels (E. coli concentration and the proportion of samples that exceeded the U.S. Environmental Protection Agency beach action value [BAV]) and dog parks within the drainage basins. NWW sites with dog parks within the drainage basins had higher E. coli concentrations in the summer and winter, and samples exceeded the BAV more frequently in the winter than sites without dog parks within the drainage basins. Escherichia coli levels in the summer and winter were positively correlated with the number of dog parks within the drainage basins, indicating that E. coli concentrations and the frequency of BAV exceedances were seasonally higher at sites with more dog parks than at sites with fewer dog parks within the drainage basins. Escherichia coli concentrations in the summer were negatively correlated with distance to the nearest dog park in the drainage basin, indicating that sites with at least one dog park in close proximity had higher E. coli concentrations in the summer than sites for which the closest dog park was more distantly located. However, results of this study may have been influenced by the high degree of spatial autocorrelation in the data caused by overlapping drainage basins. Additionally, E. coli occurs in the gut systems of many species, so concentrations of E. coli may not represent levels of dog fecal contamination. Dog waste in residential yards and neighborhoods is a possible source of contamination in the watershed that could be investigated in future studies on sources of fecal contamination in the CHAT watershed. Utilizing dog-specific genetic markers in future studies would help reduce ambiguity in data interpretation.
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Yáñez-Pagans, Patricia. Do We Need More Women in Power? Gender, Public Policy, and Development in Bolivia. Inter-American Development Bank, 2014. http://dx.doi.org/10.18235/0011665.

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This paper evaluates the impacts of increasing female representation in Bolivian municipal councils on public policy choices and welfare outcomes. By combining detailed administrative panel data on municipal expenditures and revenues together with electoral data, an innovative regression discontinuity design (RDD) is applied. As opposed to previous studies, the RDD approach proposed is unique since it is implemented to systems of proportional representation. Findings indicate that municipalities with women councilors devote more resources to social investments. In particular, women politicians prioritize education, health, and environmental protection expenditures giving less attention to infrastructure investments. The impacts of higher female representation appear only some years after the elections, highlighting the importance of training and experience. Despite changes in public policy choices there is weak evidence on the links with final welfare outcomes.
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Shivakumar, Pranavkumar, Kanika Gupta, Antonio Bobet, Boonam Shin, and Peter J. Becker. Estimating Strength from Stiffness for Chemically Treated Soils. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317383.

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The central theme of this study is to identify strength-stiffness correlations for chemically treated subgrade soils in Indiana. This was done by conducting Unconfined Compression (UC) Tests and Resilient Modulus Tests for soils collected at three different sites—US-31, SR-37, and I-65. At each site, soil samples were obtained from 11 locations at 30 ft spacing. The soils were treated in the laboratory with cement, using the same proportions used for construction, and cured for 7 and 28 days before testing. Results from the UC tests were compared with the resilient modulus results that were available. No direct correlation was found between resilient modulus and UCS parameters for the soils investigated in this study. A brief statistical analysis of the results was conducted, and a simple linear regression model involving the soil characteristics (plasticity index, optimum moisture content and maximum dry density) along with UCS and resilient modulus parameters was proposed.
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Brosh, Arieh, David Robertshaw, Yoav Aharoni, Zvi Holzer, Mario Gutman, and Amichai Arieli. Estimation of Energy Expenditure of Free Living and Growing Domesticated Ruminants by Heart Rate Measurement. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7580685.bard.

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Research objectives were: 1) To study the effect of diet energy density, level of exercise, thermal conditions and reproductive state on cardiovascular function as it relates to oxygen (O2) mobilization. 2) To validate the use of heart rate (HR) to predict energy expenditure (EE) of ruminants, by measuring and calculating the energy balance components at different productive and reproductive states. 3) To validate the use of HR to identify changes in the metabolizable energy (ME) and ME intake (MEI) of grazing ruminants. Background: The development of an effective method for the measurement of EE is essential for understanding the management of both grazing and confined feedlot animals. The use of HR as a method of estimating EE in free-ranging large ruminants has been limited by the availability of suitable field monitoring equipment and by the absence of empirical understanding of the relationship between cardiac function and metabolic rate. Recent developments in microelectronics provide a good opportunity to use small HR devices to monitor free-range animals. The estimation of O2 uptake (VO2) of animals from their HR has to be based upon a consistent relationship between HR and VO2. The question as to whether, or to what extent, feeding level, environmental conditions and reproductive state affect such a relationship is still unanswered. Studies on the basic physiology of O2 mobilization (in USA) and field and feedlot-based investigations (in Israel) covered a , variety of conditions in order to investigate the possibilities of using HR to estimate EE. In USA the physiological studies conducted using animals with implanted flow probes, show that: I) although stroke volume decreases during intense exercise, VO2 per one heart beat per kgBW0.75 (O2 Pulse, O2P) actually increases and measurement of EE by HR and constant O2P may underestimate VO2unless the slope of the regression relating to heart rate and VO2 is also determined, 2) alterations in VO2 associated with the level of feeding and the effects of feeding itself have no effect on O2P, 3) both pregnancy and lactation may increase blood volume, especially lactation; but they have no effect on O2P, 4) ambient temperature in the range of 15 to 25°C in the resting animal has no effect on O2P, and 5) severe heat stress, induced by exercise, elevates body temperature to a sufficient extent that 14% of cardiac output may be required to dissipate the heat generated by exercise rather than for O2 transport. However, this is an unusual situation and its affect on EE estimation in a freely grazing animal, especially when heart rate is monitored over several days, is minor. In Israel three experiments were carried out in the hot summer to define changes in O2P attributable to changes in the time of day or In the heat load. The animals used were lambs and young calves in the growing phase and highly yielding dairy cows. In the growing animals the time of day, or the heat load, affected HR and VO2, but had no effect on O2P. On the other hand, the O2P measured in lactating cows was affected by the heat load; this is similar to the finding in the USA study of sheep. Energy balance trials were conducted to compare MEI recovery by the retained energy (RE) and by EE as measured by HR and O2P. The trial hypothesis was that if HR reliably estimated EE, the MEI proportion to (EE+RE) would not be significantly different from 1.0. Beef cows along a year of their reproductive cycle and growing lambs were used. The MEI recoveries of both trials were not significantly different from 1.0, 1.062+0.026 and 0.957+0.024 respectively. The cows' reproductive state did not affect the O2P, which is similar to the finding in the USA study. Pasture ME content and animal variables such as HR, VO2, O2P and EE of cows on grazing and in confinement were measured throughout three years under twenty-nine combinations of herbage quality and cows' reproductive state. In twelve grazing states, individual faecal output (FO) was measured and MEI was calculated. Regression analyses of the EE and RE dependent on MEI were highly significant (P&lt;0.001). The predicted values of EE at zero intake (78 kcal/kgBW0.75), were similar to those estimated by NRC (1984). The EE at maintenance condition of the grazing cows (EE=MEI, 125 kcal/kgBW0.75) which are in the range of 96.1 to 125.5 as presented by NRC (1996 pp 6-7) for beef cows. Average daily HR and EE were significantly increased by lactation, P&lt;0.001 and P&lt;0.02 respectively. Grazing ME significantly increased HR and EE, P&lt;0.001 and P&lt;0.00l respectively. In contradiction to the finding in confined ewes and cows, the O2P of the grazing cows was significantly affected by the combined treatments (P&lt;0.00l ); this effect was significantly related to the diet ME (P&lt;0.00l ) and consequently to the MEI (P&lt;0.03). Grazing significantly increased O2P compared to confinement. So, when EE of grazing animals during a certain season of the year is estimated using the HR method, the O2P must be re measured whenever grazing ME changes. A high correlation (R2&gt;0.96) of group average EE and of HR dependency on MEI was also found in confined cows, which were fed six different diets and in growing lambs on three diets. In conclusion, the studies conducted in USA and in Israel investigated in depth the physiological mechanisms of cardiovascular and O2 mobilization, and went on to investigate a wide variety of ruminant species, ages, reproductive states, diets ME, time of intake and time of day, and compared these variables under grazing and confinement conditions. From these combined studies we can conclude that EE can be determined from HR measurements during several days, multiplied by O2P measured over a short period of time (10-15 min). The study showed that RE could be determined during the growing phase without slaughtering. In the near future the development microelectronic devices will enable wide use of the HR method to determine EE and energy balance. It will open new scopes of physiological and agricultural research with minimizes strain on animals. The method also has a high potential as a tool for herd management.
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