Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling

Conventional methods of estimating latent behaviour generally use attitudinal questions which are subjective and these survey questions may not always be available. We hypothesize that an alternative approach can be used for latent variable estimation through an undirected graphical models. For instance, non-parametric artificial neural networks. In this study, we explore the use of generative non-parametric modelling methods to estimate latent variables from prior choice distribution without the conventional use of measurement indicators. A restricted Boltzmann machine is used to represent latent behaviour factors by analyzing the relationship information between the observed choices and explanatory variables. The algorithm is adapted for latent behaviour analysis in discrete choice scenario and we use a graphical approach to evaluate and understand the semantic meaning from estimated parameter vector values. We illustrate our methodology on a financial instrument choice dataset and perform statistical analysis on parameter sensitivity and stability. Our findings show that through non-parametric statistical tests, we can extract useful latent information on the behaviour of latent constructs through machine learning methods and present strong and significant influence on the choice process. Furthermore, our modelling framework shows robustness in input variability through sampling and validation

An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service

In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the methodology, a single decision tree, bootstrap-aggregated (bagged) decision trees, random forest, boosted decision trees, and artificial neural network for regression have been adapted and systematically compared using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and slope. To better assess the quality of the models, they have been tested on a real case study using the data of DiDi Chuxing, the main on-demand ride hailing service provider in China. In the current study, 199,584 time-slots describing the spatio-temporal ride-hailing demand has been extracted with an aggregated-time interval of 10 mins. All the methods are trained and validated on the basis of two independent samples from this dataset. The results revealed that boosted decision trees provide the best prediction accuracy (RMSE=16.41), while avoiding the risk of over-fitting, followed by artificial neural network (20.09), random forest (23.50), bagged decision trees (24.29) and single decision tree (33.55).Comment: Currently under review for journal publicatio

Spatial dimensions of social unrest and armed violence

Social unrest and armed violence rewind development achievements in the fight against poverty. This thesis examines various factors contributing to social unrest and armed violence in different parts of a country. A common method of this thesis is the empirical regression analysis of geo-spatial data to explain social unrest and armed violence. I find four results. First, social unrest occurs more likely in areas where droughts coincide with existing ethnic grievances. Second, to identify these grievances, we develop a novel spatial inequality measure between and within ethnic groups. Validation of the inequality measure against perceived differences in identity groups’ economic conditions shows that individuals feel ethnic grievances. Third, competition between armed groups causally increases the level of violence. Finally, there is no evidence that development aid increases civil wars, but Chinese aid seems to increase state repressions and a higher tolerance for autocratic rule. This thesis shows that spatial characteristics can help understand and explain social unrest and armed violence within countries

Identification and Estimation of Structural-Change Models with Misclassification

Consider a simple change-point model with a binary regressor. We examine the consistency of the change-point estimator when the regressor is subject to misclassification. It is found that the time of change can always be identified. Further, special cases where the structural parameters can also be identified are discussed. Simulation evidence is provided.

Green Plants for Hawai'i's Tropical Landscapes

Plants recommended for Hawaii landscapes to suggest a tropical environment, even when grown in subtropical areas (such as Hawaii) or warm-temperate zones, are listed and illustrated