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An Evaluation of the Indian Child Nutrition and Development Program
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copies of this document for noncommercial purposes by any means, provided that this copyright notice appears on all such copies.
BETAREGRESSION MODEL FOR PERIODIC DATA WITH A TREND
, 2007
"... Abstract. In this paper, we prove that there exists exactly one maximum likelihood estimator for the betaregression model, where beta distributed dependent variable is periodic with a trend. This is an important generalization of the result obtained by Dawidowicz ([3]). The model is useful when th ..."
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Abstract. In this paper, we prove that there exists exactly one maximum likelihood estimator for the betaregression model, where beta distributed dependent variable is periodic with a trend. This is an important generalization of the result obtained by Dawidowicz ([3]). The model is useful when the dependent variable is continuous and restricted to a bounded interval. In such a model the classical regression should not be applied. The parameters are obtained by maximum likelihood estimation. We test a hypothesis of periodicity against the trend. An AIC is used to decide whether the hypothesis should be rejected or not. We analyze the goodnessoffit sensitivity. We consider diagnostic techniques that can be used to identify departures from the postulated model and to identify influential observations. 1. Introduction. The
Modelling student performance in a tertiary preparatory course
"... In this dissertation a review of the literature as it applies to the modelling of educational performance data is undertaken. Statistical linear models, including the novel Beta, Tweedie and Tobit regression models, are then applied to the performance data of students who have undertaken a preparato ..."
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In this dissertation a review of the literature as it applies to the modelling of educational performance data is undertaken. Statistical linear models, including the novel Beta, Tweedie and Tobit regression models, are then applied to the performance data of students who have undertaken a preparatory mathematics course. These models are then critically reviewed and compared with the commonly used standard linear regression model. Issues that arise from the application of statistical linear models to educational performance data are then explored. For example, the effects of nonNormality, which characterizes educational performance data, and the presence of large numbers of students who fail to complete the course (a characteristic of this particular context), are examined and reported. Both of these effects can violate the underlying assumptions of the standard linear regression model. Simulation studies are then used to assess the appropriateness of the linear model when it is applied under the condition of nonNormality and the presence of large numbers of missing observations. Findings from this study indicate that issues relating to model effectiveness are clouded in the educational context by typically large values of the error variance (high noise) and the difficulty in finding suitable performance predictors. Educational models of performance typically lack statistical power, so that in many instances it doesn’t matter what model is applied to the data. Nevertheless, the study highlights many reasons why models alternative to the standard linear regression model should be applied to such i ii data. For example, in situations where the effect is not constant over the entire domain of the explanatory variable, a linear model based upon the beta distribution will be much more appropriate. Similarly, in situations where the performance data contains exact zeros (for example the performance of students who withdraw from the course without providing any measure of achievement) it is more appropriate to use a Tweedie linear model than the standard linear regression model. Certification of dissertation
Beta regression modeling: recent advances in theory and applications
, 2013
"... Data measured in a continuous scale and restricted to the unit interval, i.e. 0 < y < 1: ◮ percentages, ◮ proportions, ..."
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Data measured in a continuous scale and restricted to the unit interval, i.e. 0 < y < 1: ◮ percentages, ◮ proportions,
Generalized Beta Regression Models for Random LossGivenDefault
, 2008
"... We propose a new framework for modeling systematic risk in LossGivenDefault (LGD) in the context of credit portfolio losses. The class of models is very flexible and accommodates well skewness and heteroscedastic errors. The quantities in the models have simple economic interpretation. Inference o ..."
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We propose a new framework for modeling systematic risk in LossGivenDefault (LGD) in the context of credit portfolio losses. The class of models is very flexible and accommodates well skewness and heteroscedastic errors. The quantities in the models have simple economic interpretation. Inference of models in this framework can be unified. Moreover, it allows efficient numerical procedures, such as the normal approximation and the saddlepoint approximation, to calculate the portfolio loss distribution, Value at Risk (VaR) and Expected Shortfall (ES).
Article TwoPart Models for Fractional Responses Defined as Ratios of Integers
, 2014
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Generalized ARMA Models with Martingale Difference Errors∗
, 2013
"... The analysis of nonGaussian time series has been studied extensively and has many applications. Many successful models can be viewed as special cases or variations of the generalized autoregressive moving average (GARMA) models of Benjamin et al. (2003), where a link function similar to that used i ..."
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The analysis of nonGaussian time series has been studied extensively and has many applications. Many successful models can be viewed as special cases or variations of the generalized autoregressive moving average (GARMA) models of Benjamin et al. (2003), where a link function similar to that used in generalized linear models is introduced and the conditional mean, under the link function, assumes an ARMA structure. Under such a model, the ’transformed ’ time series, under the same link function, assumes an ARMA form as well. Unfortunately, unless the link function is an identity function, the error sequence defined in the transformed ARMA model is usually not a martingale difference sequence. In this paper we extend the GARMA model in such a way that the resulting ARMA model in the transformed space has a martingale difference sequence as its error sequence. The benefit of such an extension are fourfolds. It has easily verifiable conditions for stationarity and ergodicity; its Gaussian pseudolikelihood estimator is consistent; standard time series model building tools are ready to use; and its MLE’s asymptotic distribution can be established. We also proposes two new classes of nonGaussian time series models under the new framework. The performance of the proposed models is demonstrated with simulated and real examples.
Visual artist price heterogeneity
, 2012
"... This paper proposes an empirical analysis to establish the determinants of Artist Price Heterogeneity (APH), using a unique dataset, which comprises all artwork sales occurred in Italy between 2006 and 2010. APH is measured by Gini indices calculated on artist price distributions. A Beta Regression ..."
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This paper proposes an empirical analysis to establish the determinants of Artist Price Heterogeneity (APH), using a unique dataset, which comprises all artwork sales occurred in Italy between 2006 and 2010. APH is measured by Gini indices calculated on artist price distributions. A Beta Regression Model (BRM) is estimated to account for the characteristic of the dependent variable, which can only assume values in the standard unit interval. Our analysis shows that APH is influenced by number of trades, average price, artist specialization, descent, fame, production, market structure and nationality, and artistic period.