tag:crantastic.org,2005:/authors/7374Latest activity for Koushiki Bose2018-05-29T13:41:06Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/756992018-05-29T13:41:06Z2018-05-29T13:41:06ZFarmTest was upgraded to version 1.0.3<a href="/packages/FarmTest">FarmTest</a> was <span class="action">upgraded</span> to version <a href="/packages/FarmTest/versions/72257">1.0.3</a><br /><h3>Package description:</h3><p>Performs robust multiple testing for means in the presence of known and unknown latent factors. It implements a robust procedure to estimate distribution parameters using the Huber's loss function and accounts for strong dependence among coordinates via an approximate factor model. This method is particularly suitable for high dimensional data when there are many variables but only a small number of observations available. Moreover, the method is tailored to cases when the underlying distribution deviates from Gaussian, which is commonly assumed in the literature. Besides the results of hypotheses testing, the estimated underlying factors and diagnostic plots are also output. Multiple comparison correction is done after estimating the proportion of true null hypotheses using the method in Storey (2015) <https://github.com/jdstorey/qvalue>. For detailed description of methods and further references, see the papers on the 'FarmTest' method: Fan et al. (2017) <arXiv:1711.05386> and Zhou et al. (2017) <arXiv:1711.05381>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/739722018-04-19T22:01:10Z2018-04-19T22:01:10ZFarmSelect was upgraded to version 1.0.2<a href="/packages/FarmSelect">FarmSelect</a> was <span class="action">upgraded</span> to version <a href="/packages/FarmSelect/versions/70624">1.0.2</a><br /><h3>Package description:</h3><p>Implements a consistent model selection strategy for high dimensional sparse regression when the covariate dependence can be reduced through factor models. By separating the latent factors from idiosyncratic components, the problem is transformed from model selection with highly correlated covariates to that with weakly correlated variables. It is appropriate for cases where we have many variables compared to the number of samples. Moreover, it implements a robust procedure to estimate distribution parameters wherever possible, hence being suitable for cases when the underlying distribution deviates from Gaussianity. See the paper on the 'FarmSelect' method, Fan et al.(2017) <arXiv:1612.08490>, for detailed description of methods and further references.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/725942018-03-14T16:21:06Z2018-03-14T16:21:06ZFarmTest was upgraded to version 1.0.2<a href="/packages/FarmTest">FarmTest</a> was <span class="action">upgraded</span> to version <a href="/packages/FarmTest/versions/69288">1.0.2</a><br /><h3>Package description:</h3><p>Performs robust multiple testing for means in the presence of known and unknown latent factors. It implements a robust procedure to estimate distribution parameters using the Huber's loss function and accounts for strong dependence among coordinates via an approximate factor model. This method is particularly suitable for high dimensional data when there are many variables but only a small number of observations available. Moreover, the method is tailored to cases when the underlying distribution deviates from Gaussian, which is commonly assumed in the literature. Besides the results of hypotheses testing, the estimated underlying factors and diagnostic plots are also output. Multiple comparison correction is done after estimating the proportion of true null hypotheses using the method in Storey (2015) <https://github.com/jdstorey/qvalue>. See the paper on the 'FarmTest' method, Zhou et al.(2017) <https://goo.gl/68SJpd>, for detailed description of methods and further references.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/710502018-01-30T18:21:05Z2018-01-30T18:21:05ZFarmSelect was upgraded to version 1.0.1<a href="/packages/FarmSelect">FarmSelect</a> was <span class="action">upgraded</span> to version <a href="/packages/FarmSelect/versions/67852">1.0.1</a><br /><h3>Package description:</h3><p>Implements a consistent model selection strategy for high dimensional sparse regression when the covariate dependence can be reduced through factor models. By separating the latent factors from idiosyncratic components, the problem is transformed from model selection with highly correlated covariates to that with weakly correlated variables. It is appropriate for cases where we have many variables compared to the number of samples. Moreover, it implements a robust procedure to estimate distribution parameters wherever possible, hence being suitable for cases when the underlying distribution deviates from Gaussianity. See the paper on the 'FarmSelect' method, Fan et al.(2017) <arXiv:1612.08490>, for detailed description of methods and further references.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/704912018-01-17T13:00:52Z2018-01-17T13:00:52ZFarmSelect was released<a href="/packages/FarmSelect">FarmSelect</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Implements a consistent model selection strategy for high dimensional sparse regression when the covariate dependence can be reduced through factor models. By separating the latent factors from idiosyncratic components, the problem is transformed from model selection with highly correlated covariates to that with weakly correlated variables. It is appropriate for cases where we have many variables compared to the number of samples. Moreover, it implements a robust procedure to estimate distribution parameters wherever possible, hence being suitable for cases when the underlying distribution deviates from Gaussianity. See the paper on the 'FarmSelect' method, Fan et al.(2017) <arXiv:1612.08490>, for detailed description of methods and further references.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/688802017-11-26T19:40:45Z2017-11-26T19:40:45ZFarmTest was upgraded to version 1.0.1<a href="/packages/FarmTest">FarmTest</a> was <span class="action">upgraded</span> to version <a href="/packages/FarmTest/versions/65860">1.0.1</a><br /><h3>Package description:</h3><p>Performs robust multiple testing for means in the presence of known and unknown latent factors. It implements a robust procedure to estimate distribution parameters using the Huber's loss function and accounts for strong dependence among coordinates via an approximate factor model. This method is particularly suitable for high dimensional data when there are many variables but only a small number of observations available. Moreover, the method is tailored to cases when the underlying distribution deviates from Gaussian, which is commonly assumed in the literature. Besides the results of hypotheses testing, the estimated underlying factors and diagnostic plots are also output. Multiple comparison correction is done after estimating the proportion of true null hypotheses using the method in Storey (2015) <https://github.com/jdstorey/qvalue>. See the paper on the 'FarmTest' method, Zhou et al.(2017) <https://goo.gl/68SJpd>, for detailed description of methods and further references.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/676892017-10-24T15:20:49Z2017-10-24T15:20:49ZFarmTest was released<a href="/packages/FarmTest">FarmTest</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Performs robust multiple testing for means in the presence of known and unknown latent factors. It implements a robust procedure to estimate distribution parameters using the Huber's loss function and accounts for strong dependence among coordinates via an approximate factor model. This method is particularly suitable for high dimensional data when there are many variables but only a small number of observations available. Moreover, the method is tailored to cases when the underlying distribution deviates from Gaussian, which is commonly assumed in the literature. Besides the results of hypotheses testing, the estimated underlying factors and diagnostic plots are also output. Multiple comparison correction is done after estimating the proportion of true null hypotheses using the method in Storey (2015) <https://github.com/jdstorey/qvalue>. For detailed description of methods and further references, see the papers on the 'FarmTest' method: Fan et al. (2017) <arXiv:1711.05386> and Zhou et al. (2017) <arXiv:1711.05381>.</p>crantastic.org