  We consider hypotheses testing problems for three parameters in
high-dimensional linear models with minimal sparsity assumptions of their type
but without any compatibility conditions. Under this framework, we construct
the first $\sqrt{n}$-consistent estimators for low-dimensional coefficients,
the signal strength, and the noise level. We support our results using
numerical simulations and provide comparisons with other estimators.
