Nonlinear Hypotheses, Inequality Restrictions, and Non-Nested Hypotheses: Exact Simultaneous Tests in Linear Regressions
在经典线性模型框架下，研究如何同时检验回归系数上的非线性假设、不等式约束或非嵌套假设，推导了似然比统计量在原假设下精确的界，并提出了类似德宾-沃森检验的界检验，适用于多重检验问题。
In the context of the classical linear model, the problem of comparing two arbitrary hypotheses on the regression coefficients is considered. Problems involving nonlinear hypotheses, inequality restrictions, or non-nested hypotheses are included as special cases. Exact bounds on the null distribution of likelihood ratio statistics are derived. In an important special case, a bounds test similar to the Durbin-Watson test is proposed. Multiple testing problems are also studied