35,521 research outputs found
Senator James L. Buckley statement to the 1972 Republican Platform Committee
Get PDFStatement by Senator James L. Buckley to Platform Committee at the 1972 Republican National Convention
BUCKLEY: Stata module to implement Buckley-James method for analysing censored data
No full textbuckley uses the Buckley-James method (Buckley and James 1979) to estimate the regression coefficients and generate the expected value of the censored outcome. depvar is the dependent variable whose value is rightly censored when the censoring variable censorvar=1. Otherwise it is observed exactly when censorvar=0. varlist is a list of explanatory variable names. At least one explanatory variable must be specified. This is a revision of material published in SJ 5(4), 2005.censoring, Buckley-James, regression
Author\u27s Response to James J. Buckley
Get PDFA response to James J. Buckley\u27s review of A Future for Truth: Evangelical Theology in a Postmodern World
Buckley-James method for analyzing censored data, with an application to a cardiovascular disease and an HIV/AIDS study
No full textBuckley-James method for analyzing censored data, with an application to a cardiovascular disease and an HIV/AIDS stud
Correspondence between Dr. Aziz Atiya and John S. Badeau, James M. Buckley
No full textCorrespondence between Dr. Aziz Atiya, John S. Badeau, and James M. Buckley regarding various Sinai trips, books on the Crusades, the Library of Congress, and other matters of research. Record contains 13 pagesCorrespondence between Dr. Aziz Atiya and John S. Badeau, James M. Buckley. 1. Typed letter dated 9 December 1952 to Dr. Atiya, 14 Sharia Wadi El Nil, Maadi, from John S. Badeau, President of the American University at Cairo. Badeau thanks Dr. Atiya for a trip to monasteries. 2. Typed letter dated 23 October, 1950, to John S. Badeau from Dr. Atiya. Dr. Atiya describes his agenda for visiting the United States and assisting the Library of Congress. 3. Typed letter dated 15 July 1959 from James M. Buckley, Press Officer, Office of Education, to Dr. Atiya at Princeton University. Mr. Buckley asks Dr. Atiya for a copy of Dr. Atiya\u27s book "The Crusade in the Later Middle Ages." 4. Typed letter dated 19 November 1949 from John S. Badeau, The American University at Cairo, to Dr. Atiya. Mr. Badeau thanks Dr. Atiya for gifting him with Dr. Atiya\u27s two books on the Crusades. 5. Typed letter dated 20 November 1950 from John S. Badeau, The American University at Cairo, to Dr. Atiya, 100 Ru
David N. Buckley : James Fintan Lalor : Radical
No full textJoannon Pierre. David N. Buckley : James Fintan Lalor : Radical. In: Études irlandaises, n°16-2, 1991. pp. 244-245
A note on Buckley-James estimators for censored data
No full textWe consider Buckley-James estimation in simple linear regression applied to the Stanford heart transplant data. We describe an experiment in which the ages of patients are perturbed. The number of limiting values of Buckley-James estimates exhibits chaotic behaviour. By considering the ordering of the residuals we obtain formulae for the limiting values of the regression coefficients.</p
WHRC Interviews Buckley, Kennan, Krutch
No full textAccession No. 5706 No. of Items: 2 From: Bruce Reeves '55 Through: Gift or Purchase: Reel-to-mel tapes of Reeves' interview for WHRC of William Buckley, George Kennan, James Wood Krutch, (G. Bromley?) Oxnam and (?) Weeks. Date Received: May, 1993 Date Acknowledged: June, 1993 by DF Peterson Remarks: To HCA, Range 10Side 1: 15:30 - Interview with William Buckley - 4-12-55; 14:30Interview with George Kennan - 4-18-55; Side 2:Interview Joseph Wood Krutch5-3-55; 896-1161; SCAP 13, 13N
Empirical likelihood analysis of the Buckley-James estimator
No full textThe censored linear regression model, also referred to as the accelerated failure time (AFT) model when the logarithm of the survival time is used as the response variable, is widely seen as an alternative to the popular Cox model when the assumption of proportional hazards is questionable. Buckley and James [Linear regression with censored data, Biometrika 66 (1979) 429-436] extended the least squares estimator to the semiparametric censored linear regression model in which the error distribution is completely unspecified. The Buckley-James estimator performs well in many simulation studies and examples. The direct interpretation of the AFT model is also more attractive than the Cox model, as Cox has pointed out, in practical situations. However, the application of the Buckley-James estimation was limited in practice mainly due to its illusive variance. In this paper, we use the empirical likelihood method to derive a new test and confidence interval based on the Buckley-James estimator of the regression coefficient. A standard chi-square distribution is used to calculate the P-value and the confidence interval. The proposed empirical likelihood method does not involve variance estimation. It also shows much better small sample performance than some existing methods in our simulation studies.Censored data Wilks theorem Accelerated failure time model Linear regression model
The Buckley-James Estimator and Induced Smoothing
No full textThe Buckley-James (BJ) estimator is known to be consistent and efficient for a linear regression model with censored data. However, its application in practice is handicapped by the lack of a reliable numerical algorithm for finding the solution. For a given data set, the iterative approach may yield multiple solutions, or no solution at all. To alleviate this problem, we modify the induced smoothing approach originally proposed in 2005 by Brown & Wang. The resulting estimating functions become smooth, thus eliminating the tendency of the iterative procedure to oscillate between different parameter values. In addition to facilitating point estimation the smoothing approach enables easy evaluation of the projection matrix, thus providing a means of calculating standard errors. Extensive simulation studies were carried out to evaluate the performance of different estimators. In general, smoothing greatly alleviates numerical issues that arise in the estimation process. In particular, the one-step smoothing estimator eliminates non-convergence problems and performs similarly to full iteration until convergence. The proposed estimation procedure is illustrated using a dataset from a multiple myeloma study
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