This paper reports results from social learning experiments where subjects choose between two options and each subject has a small chance of being perfectly informed about which option is correct. In treatment “sequence,” subjects observe the entire sequence of predecessors’ choices while in treatment “no-sequence” they only observe the number of times each option has been chosen. The theoretic...

Benjamini and Hochberg (1995) proposed the false discovery rate (FDR) as an alternative to the familywise error rate (FWER) in multiple testing problems. Since then, researchers have been increasingly interested in developing methodologies for controlling the FDR under different model assumptions. In a later paper, Benjamini and Yekutieli (2001) developed a conservative step-up procedure contro...

In the context of correlated multiple tests, we aim to nonasymptotically control the family-wise error rate (FWER) using resampling-type procedures. We observe repeated realizations of a Gaussian random vector in possibly high dimension and with an unknown covariance matrix, and consider the oneand two-sided multiple testing problem for the mean values of its coordinates. We address this proble...

In museums, it is critical to properly conserve the existing artwork. For this purpose, it is fundamental to continuously monitor its environment, either in storage or exhibition rooms. Contrarily to traditional measuring equipments and procedures used in museums, the deployment of a Wireless Sensor Network (WSN) can help to implement these measurements continuously, in a real-time basis, and i...

The approach of controlling the false discovery rate (FDR) which is less conservative than controlling the family wise error rate (FWER) has become a new and popular multiple testing procedure in microarray studies. In addition to multiple testing problems the limitation of sample size is another big challenge in this area. This thesis addresses the problem of multiple testing in the context of...

Genetic association tests involving copy-number variants (CNVs) are complicated by the fact that CNVs span multiple markers at which measurements are taken. The power of an association test at a single marker is typically low, and it is desirable to pool information across the markers spanned by the CNV. However, CNV boundaries are not known in advance, and the best way to proceed with this poo...