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Many applied problems require a covariance matrix estimator that is not only invertible, but also well-conditioned (that is, inverting it does not amplify estimation error). For largedimensional covariance matrices, the usual estimator—the sample covariance matrix—is typically not well-conditioned and may not even be invertible. This paper introduces an(More)
This paper proposes to estimate the covariance matrix of stock returns by an optimally weighted average of two existing estimators: the sample covariance matrix and single-index covariance matrix. This method is generally known as shrinkage, and it is standard in decision theory and in empirical Bayesian statistics. Our shrinkage estimator can be seen as a(More)
PURPOSE Panitumumab, a fully human antibody against the epidermal growth factor receptor (EGFR), has activity in a subset of patients with metastatic colorectal cancer (mCRC). Although activating mutations in KRAS, a small G-protein downstream of EGFR, correlate with poor response to anti-EGFR antibodies in mCRC, their role as a selection marker has not(More)
PURPOSE Panitumumab is a fully human monoclonal antibody directed against the epidermal growth factor receptor (EGFR). We compared the activity of panitumumab plus best supportive care (BSC) to that of BSC alone in patients with metastatic colorectal cancer who had progressed after standard chemotherapy. PATIENTS AND METHODS We randomly assigned 463(More)
PURPOSE Panitumumab, a fully human anti-epidermal growth factor receptor (EGFR) monoclonal antibody that improves progression-free survival (PFS), is approved as monotherapy for patients with chemotherapy-refractory metastatic colorectal cancer (mCRC). The Panitumumab Randomized Trial in Combination With Chemotherapy for Metastatic Colorectal Cancer to(More)
This paper analyzes whether standard covariance matrix tests work when dimensionality is large, and in particular larger than sample size. In the latter case, the singularity of the sample covariance matrix makes likelihood ratio tests degenerate, but other tests based on quadratic forms of sample covariance matrix eigenvalues remain well-defined. We study(More)
CONTEXT More persons in the United States die from non-small cell lung cancer (NSCLC) than from breast, colorectal, and prostate cancer combined. In preclinical testing, oral gefitinib inhibited the growth of NSCLC tumors that express the epidermal growth factor receptor (EGFR), a mediator of cell signaling, and phase 1 trials have demonstrated that a(More)
The central message of this paper is that nobody should be using the sample covariance matrix for the purpose of portfolio optimization. It contains estimation error of the kind most likely to perturb a mean-variance optimizer. In its place, we suggest using the matrix obtained from the sample covariance matrix through a transformation called shrinkage.(More)
PURPOSE The purpose of this study was to determine whether the addition of the epidermal growth factor receptor tyrosine kinase inhibitor gefitinib (Iressa, ZD1839; AstraZeneca, Wilmington, DE) to standard first-line gemcitabine and cisplatin provides clinical benefit over gemcitabine and cisplatin alone in patients with advanced or metastatic(More)
PURPOSE Preclinical studies indicate that gefitinib (Iressa, ZD1839; AstraZeneca, Wilmington, DE), an orally active epidermal growth factor receptor tyrosine kinase inhibitor, may enhance antitumor efficacy of cytotoxics, and combination with paclitaxel and carboplatin had acceptable tolerability in a phase I trial. Gefitinib monotherapy demonstrated(More)