What are the statistical and computational problems associated with robust nonlinear regression? This paper presents a … number of possible approaches to these problems and develops a particular algorithm based on the work of Powell and Dennis …

) Improved Stochastic Ranking Evolution Strategy (ISRES), (iii) Multi-Level Single-Linkage (MLSL) algorithm, (iv) Stochastic … versions of TikTak, which is a multistart global optimization algorithm used in some recent economic applications. For … completeness, we add three popular local algorithms to the comparison--the Nelder-Mead downhill simplex algorithm, the Derivative …

We examine the higher order properties of the wild bootstrap (Wu, 1986) in a linear regression model with stochastic …

regression of Y onto X in a subpopulation homogenous in W = w (formally a conditional linear predictor). Let b₀ (w) be … the coefficient vector on X in this regression. We introduce a semiparametrically efficient estimate of the average β … methods of semiparametric regression (e.g., the partially linear regression model) …

constraints can be worth the costs. Constrained regression adds a tool for developing risk equalization models that can improve …

It is common in regression discontinuity analysis to control for high order (third, fourth, or higher) polynomials of …

When a researcher estimates the parameters of a regression function using information on all 50 states in the United … associated with regression estimates. As a leading example we focus on the case where some parameters of the regression function …

characterize a broad class of models in which a "Regression Kink Design" (RKD, or RK Design) provides valid inferences for the … covariates around the kink point. We introduce a generalization of the RKD - the "fuzzy regression kink design" - that allows for … assignment variable and the policy variable. We also show how standard local polynomial regression techniques can be adapted to …