This article introduces clusteff, a new Stata command for checking the severity of cluster heterogeneity in cluster robust analyses. Cluster heterogeneity can cause a size distortion leading to underrejection of the null hypothesis. Carter, Schnepel, and Steigerwald (2015) develop the effective number of clusters to reflect a reduction in the degrees of freedom, thereby mirroring the distortion...

In many problems of machine learning, the data are distributed nonlinearly. One way to address this kind of data is training a nonlinear classifier such as kernel support vector machine (kernel SVM). However, the computational burden of kernel SVM limits its application to large scale datasets. In this paper, we propose a Clustered Support Vector Machine (CSVM), which tackles the data in a divi...

The brain is a complex system composed of enormous numbers of differentiated neurons, and brain structure and function differs among vertebrates. To examine the molecular mechanisms underlying brain structure and function, it is important to identify the molecules involved in generating neural diversity and organization. The clustered protocadherin (Pcdh) family is the largest subgroup of the d...

Clustered linear regression (CLR) is a new machine learning algorithm that improves the accuracy of classical linear regression by partitioning training space into subspaces. CLR makes some assumptions about the domain and the data set. Firstly, target value is assumed to be a function of feature values. Second assumption is that there are some linear approximations for this function in each su...

Many data layout optimizations cluster data accesses and memory into high-locality groups in order to optimize for the memory hierarchy. In this paper, we demonstrate that similar clustering program transformations enable efficient vectorization. We call this approach clustered data parallelism (CDP). CDP enables fast and power-efficient parallelism by partitioning a data structure into cluster...

Humans effortlessly use experience from related tasks to improve their performance at novel tasks. In machine learning, we are often confronted with data from "related" tasks and asked to make predictions for a new task. How can we use the related data to make the best prediction possible? In this thesis, I present the Clustered Naive Bayes classifier, a hierarchical extension of the classic Na...

The social networks that infectious diseases spread along are typically clustered. Because of the close relation between percolation and epidemic spread, the behavior of percolation in such networks gives insight into infectious disease dynamics. A number of authors have studied clustered networks, but the networks often contain preferential mixing between high degree nodes. We introduce a clas...

The Hanani–Tutte theorem is a classical result proved for the first time in the 1930s that characterizes planar graphs as graphs that admit a drawing in the plane in which every pair of edges not sharing a vertex cross an even number of times. We generalize this result to clustered graphs with two disjoint clusters, and show that a straightforward extension to flat clustered graphs with three o...

M. Fellenberg, S. Stocker, M. Münsterkötter, H.W. Mewes http://www.mips.biochem.mpg.de/proj/expression/ 1 Experiment Portal – Features of each Clustering. The entry point of the web pages presents a list of the analyzed experiments. For a selected experiment the Experiment Portal provides the parameters that are used for the clustering and a histogram of cluster sizes. A valid clustering shows ...

Flooding is one of the most devastating natural disasters causing significant economic losses. One dominant drivers flood losses heavy precipitation, with other contributing factors such as built environments and socio-economic conditions superimposed to it. To better understand risk profile associated this hazard, we develop probabilistic models quantify future likelihood fluvial flood-related...