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Data Analysis with Regression Models, Advanced Regression Models, and Machine Learning through Optimization
Published in William P. Fox, Nonlinear Optimization, 2020
We begin with some background on logistic regression. In data analysis, logistic regression (sometimes called the logistic model or logitmodel) is a type of regression analysis used for predicting the outcome of a binary dependent variable (a variable that can take only two possible outcomes, e.g. “yes” vs. “no” or “success” vs. “failure”) based on one or morepredictor variables. Logistic regression attempts to model the probability of a “yes/success” outcome using a linear function of the predictors. Specifically, the log-odds of success (the logit of the probability) is fit to the predictors using linear regression. Logistic regression is one type of discrete choice model, which in general predict categorical dependent variables—either binary or multi-way.
A statistical investigation of structurally unsound sewers
Published in Mark Knight, Neil Thomson, Underground Infrastructure Research, 2020
J.P. Davies, B.A. Clarke, J.T. Whiter, R.J. Cunningham
Prior to modelling, the probability of G5 scale is transformed from the range (0,1) to (-∞,∞). The most common transformation is the logistic transformation, due to the direct manner in which the transformed variable may be interpreted. The logistic transformation of a G5 sewer (i.e. y=1) probability p is ln{p/(l-p)}, which is written as logit(p). It can be seen that any value p in the range (0,1) corresponds to a value of logit(p) in (-∞,∞). A linear model is then adopted for the transformed response, a procedure which ensures that the fitted probabilities will lie between zero and one when back-transformed. Thus, the logistic transform of the failure probability is modelled as a linear combination of k explanatory variables as in Equation 1 below: logit(pi)=β0+β1x1i+β2x2i+…+βkxki
Machine Learning Classifiers
Published in Rashmi Agrawal, Marcin Paprzycki, Neha Gupta, Big Data, IoT, and Machine Learning, 2020
Logistic regression, or logistic model or logit model, examines the relationship between a set of predictor variables and a categorical response variable, and determines the probability of occurrence of an event by modeling the response in terms of predictors using a logistic or sigmoid curve (DeGregory, Kuiper et al. 2018). Logistic regression models are binary logistic regression and multinomial logistic regression depending on whether the dependent variable is binary or not. If the dependent variable is binary, having two values, true or false, and independent variables are either continuous or categorical, binary logistic regression is applied. Multinomial logistic regression is applied when the response variable has more than two categorical values. The relationship between independent and dependent variables is represented as: Y=b0+b1X1+b2X2+………bnXn
The motives for shipping asset securitisation: sale-leaseback transactions in the shipping industry
Published in Maritime Policy & Management, 2023
Sangho Yoon, Chi Yeol Kim, Young-Joon Seo
Using a dataset consisting of 25 shipping companies that applied for SLB transactions during 2019, we examine the motives of Korean shipping companies in pursuing asset securitisations by analysing their financial characteristics using key accounting metrics over the period from 2016 to 2018. For empirical analysis, we first compare key accounting metrics of SLB firms (shipping companies that participated in SLBs) with those of non-SLB firms (shipping companies that did not participated in SLBs), and investigate statistical significance of the differences in the mean and median values between the two groups using t-test and Wilcoxon test, respectively. Then, multivariate analysis is performed using the logit model. Logit regression estimates the probability of a binary dependent variable with two possible values such as 0/1, success/fail and yes/no. In our analysis, the dependent variable is going for SLB or not. The binary logit model has gained popularity in shipping finance research estimating the default risk in bank loans (Kavussanos and Tsouknidis 2016; Mitroussi et al. 2016), high-yield bonds (Grammenos, Nomikos, and Papapostolou 2008) and shipping companies (Lozinskaia et al. 2017).
Exploring the social acceptance of transforming urban arterials to multimodal corridors. The case of Panepistimiou Avenue in Athens
Published in International Journal of Sustainable Transportation, 2023
Eleni Tzamourani, Panagiotis G. Tzouras, Stefanos Tsigdinos, Ioannis Kosmidis, Konstantinos Kepaptsoglou
By definition, the Likert scale is an ordinal scale. Ordinal scales use numbers to indicate a rank of a single attribute; yet, the ordinal data do not provide metric information (Liddell & Kruschke, 2018; Scott Long, 2015; Tzouras et al., 2020). Usually, the real numerical distances among different levels of an ordinal scale are not the same. Τhe ordinal logistic regression (ordered logit) considers the thresholds, which form the intervals per level, as model unknown parameters (see Equation (4)). Ordered logit model (or ordinal logistic regression model) is based on the proportional odds assumption. According to this assumption, the odds ratio remains constant for all the different intervals; therefore, there is only one set of betas (McCullagh, 1980). The value of odds ratio can be interpreted as: for unit increase in x the odds of being in a level less than or equal to k change by factor exp(-β) holding other parameter constant in an ordered logit model. For a given dataset, the validity of the proportional odds assumption is tested beforehand by performing a X2 test, comparing a model using the proportional odds assumption (null hypothesis) with one not using it. acceptance of scenario i from individual j measured in a Likert Scale from 1 to 5
Model justification and stratification for confounding of Chlamydia trachomatis disease
Published in Letters in Biomathematics, 2019
The logistic model (or logit model) is a statistical model which is usually taken to apply on a binary dependent variable. The logistic regression is the most important model for ordered categorical response data (Anderson, 1984; McCullagh, 1980). It is used increasingly in a wide variety of applications. It is not only used in biomedical studies but also rapidly used in social science research and marketing in the past 20 years. Apart from this, another area of increasing application is genetics. In statistics, binomial regression is a technique in which the response (often referred to as Y) is the result of a series of Bernoulli trials or a series of one of two possible disjoint outcomes (traditionally denoted as ‘success’ or 1, and ‘failure’ or 0). The log-binomial model is simply a binomial generalized linear model (GLM) with a log link function. It is particularly useful (or, popular) in biostatistical and epidemiological applications as an alternative to logistic regression.