Advances in Data Science and Adaptive Analysis

Inference methods are proposed for the bivariate mean function of a continuous stochastic process with a two-dimensional domain. Nonparametric bivariate estimation is facilitated by thresholded projection estimators. Estimators adapt to the sparsity of the bivariate function. Oracle inequality results are developed to describe the adaptive inference methods. The construction of nonparametric bivariate confidence bands is presented. Implementation results show the applicability of the methods in practice.

This paper develops a web database application to make space-time 4D visual delivery (4DVD) of big climate data. The delivery system shows climate data in a 4D space-time box and allows users to visualize the data. Users can zoom in or out to help identify desired information for particular locations. Data can then be downloaded for the spatial maps and historical climate time series of a given location after the maps and time series are identified to be useful. These functions enable a user to quickly reach the core interested features without downloading the entire dataset in advance, which saves both time and storage space. The 4DVD system has many graphical display options such as displaying data on a round globe or on a 2D map with detailed background topographic images. It can animate maps and show time series. The combination of these features makes the system a convenient and attractive multimedia tool for classrooms, museums, and households, in addition to climate research scientists, industrial applicants, and policy makers. To demonstrate the 4DVD’s usage, several application examples are included in this paper, such as the La Nina’s influence on land temperature.

The information-based prediction models using machine learning techniques have gained massive popularity during the last few decades. Such models have been applied in a number of domains such as medical diagnosis, crime prediction, movies rating, etc. Similar is the trend in telecom industry where prediction models have been applied to predict the dissatisfied customers who are likely to change the service provider. Due to immense financial cost of customer churn in telecom, the companies from all over the world have analyzed various factors (such as call cost, call quality, customer service response time, etc.) using several learners such as decision trees, support vector machines, neural networks, probabilistic models such as Bayes, etc. This paper presents a detailed survey of models from 2000 to 2015 describing the datasets used in churn prediction, impacting features in those datasets and classifiers that are used to implement prediction model. A total of 48 studies related to churn prediction in telecom industry are discussed using 23 datasets (3 public and 20 private). Our survey aims to highlight the evolution of techniques from simple features/learners to more complex learners and feature engineering or sampling techniques. We also give an overview of the current challenges in churn prediction and suggest solutions to resolve them. This paper will allow researchers such as data analysts in general and telecom operators in particular to choose best suited techniques and features to prepare their churn prediction models.