Deep Recurrent Survival Analysis

Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship bias. Many works have been proposed for survival analysis ranging from traditional statistic methods to machine learning models. However, the existing methodologies either utilize counting-based statistics on the segmented data, or have a pre-assumption on the event probability distribution w.r.t. time. Moreover, few works consider sequential patterns within the feature space. In this paper, we propose a Deep Recurrent Survival Analysis model which combines deep learning for conditional probability prediction at fine-grained level of the data, and survival analysis for tackling the censorship. By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i.e., the probability of the non-occurrence of the event, for the censored data. Meanwhile, without assuming any specific form of the event probability distribution, our model shows great advantages over the previous works on fitting various sophisticated data distributions. In the experiments on the three real-world tasks from different fields, our model significantly outperforms the state-of-the-art solutions under various metrics.Comment: AAAI 2019. Supplemental material, slides, code: https://github.com/rk2900/drs

An example of aquifer heterogeneity simulation to modeling well-head protection areas

Groundwater management requires the definition of Well-Head Protection Areas (WHPA) for water supply wells. Italian law uses geometrical, chronological and hydrogeological criteria for WHPA identification, providing a groundwater travel time of 60 days for the definition of the Zone of Travel (ZOT). An exhaustive ZOT delineation must involve numerical modeling of groundwater flow together with simulation of the advective component of the transport process. In this context, the spatial variability of hydrogeological and transport parameters has to be critically estimated during numerical modeling implementation. In the present article, geostatistical simulation using a transition probability approach and groundwater numerical modeling were performed to delineate WHPAs for several supply wells in the middle Venetian Plain, taking into account the lithologic heterogeneity of the aquifer. The transition probability approach for the lithologic data was developed by T-PROGS software, while MODDLOW-2005 and PEST-ASP were used, respectively, to reproduce and calibrate site-specific hydraulic head data. Finally, a backward particle tracking analysis was performed with MODPATH to outline the 60-day ZOT

ASSESSING THE FINANCIAL RISKS OF DIVERSIFIED COFFEE PRODUCTION SYSTEMS: AN ALTERNATIVE NONNORMAL CDF ESTIMATION APPROACH

Recently developed techniques are adapted and combined for the modeling and simulation of crop yields and prices that can be mutually correlated, exhibit heteroskedasticity or autocorrelation, and follow nonnormal probability density functions. The techniques are applied to the modeling and simulation of probability distribution functions for the returns of three tropical agroforestry systems for coffee production. The importance of using distribution functions that can more closely reflect the statistical behavior of yields and prices for risk analysis is discussed and illustrated.Risk and Uncertainty,

Probability: modeling and applications to random processes

This is a review of the book "Probability: Modeling and Applications to Random Processes" by Gregory K. Miller