New prediction method for the mixed logistic model applied in a marketing problem

Tamura, Karin Ayumi; Giampaoli, Viviana

doi:10.1016/j.csda.2013.04.006

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…The observations, grouped by severity level and analyzed by using a mixed model [17], allow us to include the correlation and variability due to factors that were not observed in the study. Because the interest is to analyze or predict the asthma state through the binary response variable FAO, a mixed-effect logistic regression model can be proposed [31] to describe this response.…”

Section: Introduction and Context Of The Empirical Applicationmentioning

confidence: 99%

Data-Influence Analytics in Predictive Models Applied to Asthma Disease

et al. 2020

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Asthma is one of the most common chronic diseases around the world and represents a serious problem in human health. Predictive models have become important in medical sciences because they provide valuable information for data-driven decision-making. In this work, a methodology of data-influence analytics based on mixed-effects logistic regression models is proposed for detecting potentially influential observations which can affect the quality of these models. Global and local influence diagnostic techniques are used simultaneously in this detection, which are often used separately. In addition, predictive performance measures are considered for this analytics. A study with children and adolescent asthma real data, collected from a public hospital of São Paulo, Brazil, is conducted to illustrate the proposed methodology. The results show that the influence diagnostic methodology is helpful for obtaining an accurate predictive model that provides scientific evidence when data-driven medical decision-making.

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Section: Introduction and Context Of The Empirical Applicationmentioning

confidence: 99%

Data-Influence Analytics in Predictive Models Applied to Asthma Disease

et al. 2020

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“…Some recent authors like [14], [11], [9], [8] and [10] provide extensive studies on the characteristics of generalized logistic distributions, somehow answering the ever increasing interest in the logistic family of distributions. Indeed, applications abound that make use of both the standard logistic regression model and the so-called generalized logistic regression model, as can be seen in [13] and [12]. The probit link, for which is the second most commonly used of all the link functions, with Bayesian researchers seemingly topping the charts in its use.…”

Section: Necla Gündüz and Ernest Fokouémentioning

confidence: 99%

On the predictive properties of binary link functions

FOKOUÉ

2017

Communications Faculty of Science University of Ankara Series A1Mathematics and Statistics

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ON THE PREDICTIVE PROPERTIES OF BINARY LINK FUNCTIONS NECLA GÜNDÜZ AND ERNEST FOKOUÉAbstract. This paper provides a theoretical and computational justi…cation of the long held claim of the similarity of the probit and logit link functions often used in binary classi…cation. Despite this widespread recognition of the strong similarities between these two link functions, very few (if any) researchers have dedicated time to carry out a formal study aimed at establishing and characterizing …rmly all the aspects of the similarities and di¤erences. This paper proposes a de…nition of both structural and predictive equivalence of link functions-based binary regression models, and explores the various ways in which they are either similar or dissimilar. From a predictive analytics perspective, it turns out that not only are probit and logit perfectly predictively concordant, but the other link functions like cauchit and complementary log log enjoy very high percentage of predictive equivalence. Throughout this paper, simulated and real life examples demonstrate all the equivalence results that we prove theoretically.

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Inventive Investment Using Bigdata: Tools, Applicability and Challenges Associated

Wani

2021

Computational Management

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New prediction method for the mixed logistic model applied in a marketing problem

Cited by 4 publications

References 19 publications

Data-Influence Analytics in Predictive Models Applied to Asthma Disease

Data-Influence Analytics in Predictive Models Applied to Asthma Disease

On the predictive properties of binary link functions

Inventive Investment Using Bigdata: Tools, Applicability and Challenges Associated

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