Lecture

Specification of the deterministic part

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Description

This lecture covers the specification of the deterministic part in nonlinear transformations of variables, focusing on the constant and decreasing marginal impact of travel time assumptions. It discusses data preprocessing to account for nonlinearities and interactions between attributes and socio-economic characteristics.

In MOOC

Introduction to Discrete Choice Models

Human behavior is complex, and unpredictable. Or is it? Predicting the behavior using mathematical models is the topic of this course. More specifically, we focus on choice modeling, in order to obtain disaggregate demand models. Focusing on the logit model, the course covers in details the specification of the model, the estimation of its parameters, the validation and the application. Exercises using the software Biogeme are also proposed. Concrete case studies with real data sets are provided.

Official source

Related concepts (5)

Dependent and independent variables

Dependent and independent variables are variables in mathematical modeling, statistical modeling and experimental sciences. Dependent variables are studied under the supposition or demand that they depend, by some law or rule (e.g., by a mathematical function), on the values of other variables. Independent variables, in turn, are not seen as depending on any other variable in the scope of the experiment in question. In this sense, some common independent variables are time, space, density, mass, fluid flow rate, and previous values of some observed value of interest (e.

Nonlinear regression

In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations. In nonlinear regression, a statistical model of the form, relates a vector of independent variables, , and its associated observed dependent variables, . The function is nonlinear in the components of the vector of parameters , but otherwise arbitrary.

Errors-in-variables models

In statistics, errors-in-variables models or measurement error models are regression models that account for measurement errors in the independent variables. In contrast, standard regression models assume that those regressors have been measured exactly, or observed without error; as such, those models account only for errors in the dependent variables, or responses. In the case when some regressors have been measured with errors, estimation based on the standard assumption leads to inconsistent estimates, meaning that the parameter estimates do not tend to the true values even in very large samples.

Motivation

Motivation is the reason for which humans and other animals initiate, continue, or terminate a behavior at a given time. Motivational states are commonly understood as forces acting within the agent that create a disposition to engage in goal-directed behavior. It is often held that different mental states compete with each other and that only the strongest state determines behavior. This means that we can be motivated to do something without actually doing it. The paradigmatic mental state providing motivation is desire.

Self-determination theory

Self-determination theory (SDT) is a macro theory of human motivation and personality that concerns people's innate growth tendencies and innate psychological needs. It pertains to the motivation behind people's choices in the absence of external influences and distractions. SDT focuses on the degree to which human behavior is self-motivated and self-determined. In the 1970s, research on SDT evolved from studies comparing intrinsic and extrinsic motives, and from growing understanding of the dominant role that intrinsic motivation played in individual behavior.

Related concepts (5)

Dependent and independent variables

Nonlinear regression

Errors-in-variables models

Motivation

Self-determination theory