We propose identi…cation robust inference methods for multivariate reduced rank (MRR) regressions. Such models involve nonlinear restrictions on the coe¢ cients of a multivariate linear regression (MLR), whose identi…cation may raise serious non-regularities leading to the failure of standard asymptotics. To circumvent such problems, we propose con…dence set estimates for parameters of interest...

Seasonal adjustment methods transform observed time series data into estimated data, where these estimated data are constructed such that they show no or almost no seasonal variation. An advantage of model-based methods is that these can provide con dence intervals around the seasonally adjusted data. One particularly useful time series model for seasonal adjustment is the basic structural time...

We consider the problem of resampling or bootstrapping a point process to get con dence intervals for the reduced second moment function We propose a resampling scheme for spatial data which we call the marked point method This is a variant of the block of blocks bootstrap rst introduced by K unsch A simulation study with a Poisson a clustered and a regular point process on the unit square in R...

This paper studies set estimation and inference of models characterized by conditional moment inequalities. A consistent set estimator and a con…dence set are provided in this paper. Previous studies on set estimation and inference with conditional moment inequalities typically use only a …nite set of moment inequalities implied by the conditional moment inequalities. We consider an alternative...

Association rule algorithms can produce a very large number of output patterns. This has raised questions of whether the set of discovered rules \over t" the data because all the patterns that satisfy some constraints are generated (the Bonferroni e ect). In other words, the question is whether some of the rules are \false discoveries" that are not statistically signi cant. We present a novel a...

This paper describes a technique for the integration of con"dence information using Markov random "elds'models to improve the segmentation and reconstruction of three-dimensional acoustical images. A range image in which each point is associated with a reliability measure, namely, its `con"dencea, is directly provided by acoustic image formation process. In this paper, range and con"dence image...

The standard Basket Analysis derives all frequent itemsets and all association rules having support and con dence levels greater than their thresholds, and lters out trivial rules in statistical sense (1994, 1991). This framework gives comprehensive "descriptions" of regularities contained in the data. Another major purpose of data mining is to derive important knowledge for "estimation and pre...

This article proposes an estimate of the odds ratio in a (2 £ 2) table obtained from studies in which the row totals are Ž xed by design, such as a phase II clinical trial. Our estimate,basedon themedianunbiasedestimateof the probabilitiesof success in the (2£2) table, will always be in the interval (0;1): Another estimate of the odds ratio which has such properties is obtained when adding .5 t...

Beckerman ( 1993) defined causal indepen­ dence in terms of a set of temporal condi­ tional independence statements. These state­ ments formalized certain types of causal in­ teraction where (1) the effect is independent of the order that causes are introduced and (2) the impact of a single cause on the ef­ fect does not depend on what other causes have previously been applied. In this paper, w...

We illustrate the corrosive e¤ect of even small amounts of adverse selection in an asset market and how it can lead to the total breakdown of trade. The problem is the failure of “market con…dence”, de…ned as approximate common knowledge of an upper bound on expected losses. Small probability events can unravel market con…dence. We discuss the role of contagious adverse selection and the proble...