Three methods of synthesizing correlations for meta-analytic structural equation modeling (SEM) under different degrees and mechanisms of missingness were compared for the estimation of correlation and SEM parameters and goodness-of-fit indices by using Monte Carlo simulation techniques. A revised generalized least squares (GLS) method for synthesizing correlations, weighted-covariance GLS (W-C...

Non-response is a problem which pervades almost all survey research. This paper reviews ways in which adjustments for the biasing effects of non-response can be made at the analysis stage. The emphasis is practical rather than theoretical. Practical constraints on the choice of weighting method (or whether to weight at all) are discussed, as well as statistical implications and how to assess th...

Alignment of time series is an important problem to solve in many scientific disciplines. In particular, temporal alignment of two or more subjects performing similar activities is a challenging problem due to the large temporal scale difference between human actions as well as the inter/intra subject variability. In this paper we present canonical time warping (CTW), an extension of canonical ...

Context tree weighting method is a universal compression algorithm for FSMX sources. Though we expect that it will have good compression ratio in practice, it is difficult to implement it and in many cases the implementation is only for estimating compression ratio. Though Willems and Tjalkens showed practical implementation using not block probabilities but conditional probabilities, it is use...

In this paper, we describe our retrieval system used for the primary task of genomics track at this year. Our primary goal in this task is to find a proper method for the domain-specific retrieval environment. To achieve the goal, we have tested several techniques such as a phrase indexing strategy, two query weighting methods, and two post-processing methods such as a document filtering method...

This paper presents a new feature weighting method for distance-based classifiers. It is based on a generalized least squares minimization of a criterion function to estimate a feature relevance metric. Experiments over both artificial and real data sets illustrate the behaviour of this algorithm when irrelevant attributes and/or features with varying relevance are present. Effectiveness of the...

Model-based optoacoustic inversion methods are capable of eliminating image artefacts associated with the widely adopted back-projection reconstruction algorithms. Yet, significant image artefacts might also occur due to reflections and scattering of optoacoustically-induced waves from strongly acoustically-mismatched areas in tissues. Herein, we modify the model-based reconstruction methodolog...

We propose to directly measure the importance of queries in the source domain to the target domain where no rank labels of documents are available, which is referred to as query weighting. Query weighting is a key step in ranking model adaptation. As the learning object of ranking algorithms is divided by query instances, we argue that it’s more reasonable to conduct importance weighting at que...