The history of the logistic function since its introduction in 1838 is reviewed, and the logistic model for a polychotomous response variable is presented with a discussion of the assumptions involved in its derivation and use. Following this, the maximum likelihood estimators for the model parameters are derived along with a Newton-Raphson iterative procedure for evaluation. A rigorous mathematical derivation of the limiting distribution of the maximum likelihood estimators is then presented using a characteristic function approach. An appendix with theorems on the asymptotic normality of sample sums when the observations are not identically distributed, with proofs, supports the presentation on asymptotic properties of the maximum likelih...
The logistic function is widely used in many disciplines to study the asymptotic growth behaviour of...
grantor: University of TorontoThe maximum likelihood method is traditionally used in estim...
textabstractIn his recent textbook "Primer of Biostatistics", S, A, Glantz refers to the nowadays gr...
The history of the logistic function since its introduction in 1838 is reviewed, and the logistic mo...
The primary objective of this paper is a focused introduction to the logistic regression model and i...
The logistic regression originally is intended to explain the relationship between the probability o...
In this paper, we introduce a new continuous log-logistic extension. Several of its properties are e...
In the context of polytomous regression, as with any generalized linear model, robustness issues are...
International audiencePredicting individual risk is needed to target preventive interventions toward...
International audiencePredicting individual risk is needed to target preventive interventions toward...
International audiencePredicting individual risk is needed to target preventive interventions toward...
Abstract. This paper focuses on regression with binomial response data. In these cases logit regress...
This article presents an overview of the logistic regression model for dependent variables having tw...
Analysis through logistic regression explored to investigate the relationship between binary or mult...
Many studies over the last 20 years have used logistic regression to model the relationship between ...
The logistic function is widely used in many disciplines to study the asymptotic growth behaviour of...
grantor: University of TorontoThe maximum likelihood method is traditionally used in estim...
textabstractIn his recent textbook "Primer of Biostatistics", S, A, Glantz refers to the nowadays gr...
The history of the logistic function since its introduction in 1838 is reviewed, and the logistic mo...
The primary objective of this paper is a focused introduction to the logistic regression model and i...
The logistic regression originally is intended to explain the relationship between the probability o...
In this paper, we introduce a new continuous log-logistic extension. Several of its properties are e...
In the context of polytomous regression, as with any generalized linear model, robustness issues are...
International audiencePredicting individual risk is needed to target preventive interventions toward...
International audiencePredicting individual risk is needed to target preventive interventions toward...
International audiencePredicting individual risk is needed to target preventive interventions toward...
Abstract. This paper focuses on regression with binomial response data. In these cases logit regress...
This article presents an overview of the logistic regression model for dependent variables having tw...
Analysis through logistic regression explored to investigate the relationship between binary or mult...
Many studies over the last 20 years have used logistic regression to model the relationship between ...
The logistic function is widely used in many disciplines to study the asymptotic growth behaviour of...
grantor: University of TorontoThe maximum likelihood method is traditionally used in estim...
textabstractIn his recent textbook "Primer of Biostatistics", S, A, Glantz refers to the nowadays gr...