Model-Based Distance Sampling

Rapid Distance-Based Outlier Detection via Sampling

Distance-based approaches to outlier detection are popular in data mining, as they do not require to model the underlying probability distribution, which is particularly challenging for high-dimensional data. We present an empirical comparison of various approaches to distance-based outlier detection across a large number of datasets. We report the surprising observation that a simple, sampling...

Point-Based Mark-Recapture Distance Sampling

Avian surveys using point sampling for abundance estimation have either focused on distance sampling or more commonly mark-recapture to correct for detection bias. Combining mark-recapture and distance sampling (MRDS) has become an effective tool for line transects, but it has been largely ignored in point sampling literature. We describe MRDS and show that the previously published methods for ...

mortality forecasting based on lee-carter model

over the past decades a number of approaches have been applied for forecasting mortality. in 1992, a new method for long-run forecast of the level and age pattern of mortality was published by lee and carter. this method was welcomed by many authors so it was extended through a wider class of generalized, parametric and nonlinear model. this model represents one of the most influential recent d...

Distance Sampling

Model Confidence Set Based on Kullback-Leibler Divergence Distance

Consider the problem of estimating true density, h(.) based upon a random sample X1,…, Xn. In general, h(.)is approximated using an appropriate in some sense, see below) model fƟ(x). This article using Vuong's (1989) test along with a collection of k(> 2) non-nested models constructs a set of appropriate models, say model confidence set, for unknown model h(.).Application of such confide...