The Art of Case Study Research
Introduction An Intensive Study of Case Study Research Methods The Unique Case Research Questions The Nature of Qualitative Research Data Gathering Analysis and Interpretation C...
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6,837 publications
Regularization Paths for Generalized Linear Models via Coordinate Descent
We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nom...
A Survey of Corporate Governance
ABSTRACT This article surveys research on corporate governance, with special attention to the importance of legal protection of investors and of ownership concentration in corpo...
Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
Functionality-specific vulnerabilities, which mainly occur in Application Programming Interfaces (APIs) with specific functionalities, are crucial for software developers to det...
Embryonic Stem Cell Lines Derived from Human Blastocysts
Human blastocyst-derived, pluripotent cell lines are described that have normal karyotypes, express high levels of telomerase activity, and express cell surface markers that cha...
Density Estimation for Statistics and Data Analysis
Introduction. Survey of Existing Methods. The Kernel Method for Univariate Data. The Kernel Method for Multivariate Data. Three Important Methods. Density Estimation in Action.
Cancer statistics, 2023
Abstract Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population‐based c...
RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models
Abstract Summary: RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies ...
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. T...