The RODEO Approach for Nonparametric Density Estimation

Der von Lafferty und Wasserman (2008) entwickelte RODEO-Ansatz (Regularization of Derivative Expectation Operator) ist eine Regularisierungstechnik, die auf eine Vielzahl nichtparametrischer Kernel-Smoother angewendet werden kann. Die Idee des Ansatzes ist, die Reduktion der Verzerrung des Kernel-Smoothers, die mit einer Verringerung der Bandweiten einhergeht, entlang eines glatten Weges von abnehmenden Bandweite-Parameterwerten zu bestrafen. Der Einfluss von Dimensionen mit geringer lokaler Variation wird so effektiv ``herausgeglättet'', wodurch eine Art implizite Variablenauswahl stattfindet. Unter bestimmten Annahmen können so schnellere Konvergenzraten für den mittleren integrierten quadratischen Fehler des Kernel-Smoothers erreicht werden. Dies macht den RODEO-Ansatz vor allem für höhere Dimensionen attraktiv. In der vorliegenden Arbeit wird eine Implementierung präsentiert, die den RODEO-Ansatz mit lokal polynomialer Dichteschätzung kombiniert. Die Implementierung wurde durch das R-Paket lpderodeo realisiert. Ziel der vorliegenden Arbeit ist es, die Performance der Implementierung anhand einiger Beispiele zu evaluieren und mit einer Auswahl von acht weiteren nichtparametrischen Dichteschätzverfahren zu vergleichen. Die Ergebnisse legen nahe, dass RODEO-Ansatz im Vergleich zu den anderen Ansätzen schlechter ist. Darüber hinaus leidet die Implementierung aufgrund einer naiven Auswertungsabfolge unter relativ langen Rechenzeiten. Das wohl wichtigste Ergebnis dieser Arbeit ist jedoch die Tatsache, dass die von Liu, Lafferty und Wasserman (2007) entwickelte Theorie fehlerhaft ist. So führt bereits eine simple Rotation der Daten dazu, dass der Algorithmus nicht mehr richtig funktioniert.The regularization of derivative expectation operator (RODEO) approach developed by Lafferty and Wasserman (2008) is a regularization technique designed for a wide range of nonparametric kernel smoothers. The approach applies regularization by penalizing the bias reduction associated with a bandwidth reduction along a smooth path of decreasing bandwidth parameter values in order to avoid overfitting. Dimensions with small local variation are effectively smoothed out, thus implicitly carrying out variable selection. Under certain conditions, faster rates of converges of convergence for the mean integrated square error can be achieved, which makes the approach attractive for applications in high dimensions. In this paper we apply the RODEO approach to local polynomial density estimation. We implemented the approach in the R package lpderodeo. We apply our implementation to a few examples, and evaluate its performance in a comparative study using a sample of eight other approaches for nonparametric density estimation. Our findings suggest that the approach does not work well in comparison to the other considered approaches with regard to the applied performance metrics. Furthermore, our implementation suffers from long computation time due to a naive query. Our main finding, however, concerns the fact that the theoretical framework proposed by Liu, Lafferty, and Wasserman (2007) has severe shortcomings. In fact, we demonstrate that a simple rotation of the data makes the algorithm fail in practice

The intermicrovillar adhesion complex in gut barrier function and inflammation

The surface of intestinal epithelial cells is covered by the brush border, which consists of densely packed cellular extrusions called microvilli. Until recently, microvilli have not been known to be interconnected. In 2014, a protein complex, called the intermicrovillar adhesion complex (IMAC) which is located at the tips of the microvilli and responsible for the regular spatial organization of the brush border, was identified. Deletion of IMAC components such as cadherin-related family member-2 (CDHR2) in mice resulted in microvillus disorganization and fanning, a structural aberration that is also found in the brush border of patients with inflammatory bowel disease. The etiology of inflammatory bowel disease has been primarily associated with dysfunctional mucosal immunity, but the discovery of the IMAC may encourage theories of an epithelial origin. Here, possible effects of the brush border on the gut barrier function and intestinal inflammation are discussed proposing that the IMAC protects against inflammation through its microvillus cross-linking function

OpenLB User Guide: Associated with Release 1.6 of the Code

OpenLB is an object-oriented implementation of LBM. It is the first implementation of a generic platform for LBM programming, which is shared with the open source community (GPLv2). Since the first release in 2007, the code has been continuously improved and extended which is documented by thirteen releases as well as the corresponding release notes which are available on the OpenLB website (https://www.openlb.net). The OpenLB code is written in C++ and is used by application programmers as well as developers, with the ability to implement custom models OpenLB supports complex data structures that allow simulations in complex geometries and parallel execution using MPI, OpenMP and CUDA on high-performance computers. The source code uses the concepts of interfaces and templates, so that efficient, direct and intuitive implementations of the LBM become possible. The efficiency and scalability has been checked and proved by code reviews. This user manual and a source code documentation by DoxyGen are available on the OpenLB project website

Is Wisdom of Crowds a Positive Signal? Effects of Crowdfinancing on Subsequent Venture Capital Selection

The seed funding gap is still a major obstacle for the initiation of new ventures and diffusion of innovation. Crowdfunding – an innovation in the market for startup finance – could offer a new market-based means of partly closing this gap. However, crowdfinancing cannot be regarded as a complete substitute for venture capital funding, e.g. since it is not likely to fully finance a technology-based venture over time and because professional venture investors provide crucial resources besides capital. It therefore appears important to study the interaction between crowdfunding and more traditional forms of start-up finance. We examine the impact and signaling effects that crowd-based funding has on subsequent venture capital financing rounds. Drawing on a choice experimental research design and data on 5,280 decisions of 120 venture investors, our results indicate that “the crowd” generally is a negative signal for professional venture investors, but that they do not ignore positive signals sent by the crowd. We find causal evidence that start-ups with a prior crowd-investing (securities-based crowdfunding) tend to be not selected by venture investors, while high sums of (reward-based) crowdfunding, collected fast by startups with a B2C business model, can have a positive effect on VC managers’ funding decisions. Our results also suggest that traditional forms of pre-funding, i.e., prior business angel investments, by contrast significantly increase the likelihood of subsequent financing rounds. Theoretical and managerial implications are discussed

Crowdfinancing as a Determinant of Venture Capitalists' Selection Decisions

Crowdfinancing is emerging as a novel way for entrepreneurs to secure early-stage financing. With venture capital still being the most important source of funding for growth-oriented ventures in later stages, this raises the question of potential interactions between crowdfinancing and traditional forms of start-up funding. In this study, we examine the impact and signaling effects that crowdfinancing has on subsequent venture capital funding rounds. Drawing on a choice experimental research design we find causal evidence that while the crowd itself is generally seen as a negative signal, it can generate certain positive signals to which professional venture investors react. Theoretical and managerial implications are discussed