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Articles 31 - 40 of 40
Full-Text Articles in Physical Sciences and Mathematics
Eigenfunction And Harmonic Function Estimates In Domains With Horns And Cusps, Michael Cranston, Yi Li
Eigenfunction And Harmonic Function Estimates In Domains With Horns And Cusps, Michael Cranston, Yi Li
Yi Li
No abstract provided.
Eigenfunction And Harmonic Function Estimates In Domains With Horns And Cusps, Michael Cranston, Yi Li
Eigenfunction And Harmonic Function Estimates In Domains With Horns And Cusps, Michael Cranston, Yi Li
Mathematics and Statistics Faculty Publications
No abstract provided.
Travelling Fronts In Cylinders And Their Stability, Jerrold W. Bebernes, Comgming Li, Yi Li
Travelling Fronts In Cylinders And Their Stability, Jerrold W. Bebernes, Comgming Li, Yi Li
Mathematics and Statistics Faculty Publications
No abstract provided.
The Litigious Plaintiff Hypothesis: Case Selection And Resolution, Theodore Eisenberg, Henry S. Farber
The Litigious Plaintiff Hypothesis: Case Selection And Resolution, Theodore Eisenberg, Henry S. Farber
Cornell Law Faculty Publications
The process through which cases are selected for litigation cannot be ignored because it yields a set of lawsuits and plaintiffs that is far from a random selection either of potential claims or of potential claimants. We present a theoretical framework for understanding the operation of this suit-selection process and its relationship to the underlying distribution of potential claims and claimants. The model has implications for the trial rate and the plaintiff win rate at trial. Our empirical analysis, using data on over 200,000 federal civil litigations, yields results that are strongly consistent with the theory.
Ua56/1 Fact Book, Wku Institutional Research
Ua56/1 Fact Book, Wku Institutional Research
WKU Archives Records
Statistical and demographic profile of WKU.
Analysis Of Repeated Measures Data Under Circular Covariance, Andrew Montgomery Hartley
Analysis Of Repeated Measures Data Under Circular Covariance, Andrew Montgomery Hartley
Mathematics & Statistics Theses & Dissertations
Circular covariance is important in modelling phenomena in epidemiological, communications and numerous physical contexts. We introduce and develop a variety of methods which make it a more versatile tool. First, we present two classes of estimators for use in the presence of missing observations. Using simulations, we show that the mean squared errors of the estimators of one of these classes are smaller than those of the Maximum Likelihood (ML) estimators under certain conditions. Next, we propose and discuss a parsimonious, autoregressive type of circular covariance structure which involves only two parameters. We specify ML and other types of estimators …
Uniformly Adaptive Estimation For Models With Arma Errors, Douglas Steigerwald
Uniformly Adaptive Estimation For Models With Arma Errors, Douglas Steigerwald
Douglas G. Steigerwald
A semiparametric estimator based on an unknown density is uniformly adaptive if the expected loss of the estimator converges to the asymptotic expected loss of the maximum likelihood estimator based on the true density (MLE), and if convergence does not depend on either the parameter values or the form of the unknown density. Without uniform adaptivity, the asymptotic expected loss of the MLE need not approximate the expected loss of a semiparamteric estimator for any finite sample. I show that a two-step semiparametric estimator is uniformly adaptive for the parameters of nonlinear regression models with autoregressive moving average errors.
A Bayesian Approach To Bivariate Nonparametric Regression, Michael Smith, Robert Kohn
A Bayesian Approach To Bivariate Nonparametric Regression, Michael Smith, Robert Kohn
Michael Stanley Smith
No abstract provided.
Econometric Estimation Of Foresight: Tax Policy And Investment In The U.S., Douglas G. Steigerwald, Charles Stuart
Econometric Estimation Of Foresight: Tax Policy And Investment In The U.S., Douglas G. Steigerwald, Charles Stuart
Douglas G. Steigerwald
We develop a method for measuring the foresight agents have. We first dichotomize an agent's information at current date t into knowledge up to date t+f and expectations after t+f. We then form a residual-based test statistic that allows us to compare prediction errors for econometric models based on different values of f. We illustrate the method, examining investment around tax reforms to measure the foresight firms have about tax policy. In this illustration, current investment appears to reflect currently available information but little foresight other than foresight of enacted policy changes.
Asymptotic Bias For Quasi-Maximum Likelihood Estimators In Models With Conditional Heteroskedasticity, Douglas G. Steigerwald, Whitney Newey
Asymptotic Bias For Quasi-Maximum Likelihood Estimators In Models With Conditional Heteroskedasticity, Douglas G. Steigerwald, Whitney Newey
Douglas G. Steigerwald
Virtually all applications of time-varying conditional variance models use a quasi-maximum likelihood estimator (QMLE). Consistency of a QMLE requires an identification condition that the quasi-log-likelihood have a unique maximum at the true conditional mean and relative scale parameters. We show that the identification condition holds for a non-Gaussian QMLE if the conditional mean is identically zero or if a symmetry condition is satisfied. Without symmetry an additional parameter, for the location of the innovation density, must be added for consistency. We calculate the efficiency loss from adding such a parameter under symmetry, when the parameter is not needed. We also …