In this paper, we investigate the estimation of the tail index and extreme quantiles of a heavy-tailed distribution when some covariate information is available and the data are randomly right-censored. We construct several estimators by combining a moving-window technique (for tackling the covariate information) and the inverse probability-of-censoring weighting method, and we establish their ...

When a spatial pattern is observed through a bounded window, inference about the pattern is hampered by sampling eeects known as \edge eeects". This chapter identiies two main types of edge eeects: size-dependent sampling bias and censoring eeects. Sampling bias can be eliminated by changing the sampling technique, or`corrected' by weighting the observations. Censoring eeects can be tackled usi...

We generalize the Allais common consequence effect by describing three common consequence effect conditions and characterizing their implications for the probability weighting function in rank-dependent expected utility. The three conditions—horizontal, vertical, and diagonal shifts within the probability triangle—are necessary and sufficient for different curvature properties of the probabilit...

Optimizing weighting factors for a linear combination of terms in a scoring function is a crucial step for success in developing a threading algorithm. Usually weighting factors are optimized to yield the highest success rate on a training dataset, and the determined constant values for the weighting factors are used for any target sequence. Here we explore completely different approaches to ha...

This paper explores a relationship between parameters for the context tree weighting and weights for a general model weighting technique. In particular, an algorithm is proposed that approximately computes the parameters from the weights, and a condition under which no error for the approximation occurs is derived. key words: model weighting technique, tree source, context tree weighting.