SUMMARYIn many regression applications the independent variable is measured with error. When this happens, conventional parametric and nonparametric regression techniques are no longer valid. We consider two different approaches to nonparametric regression. The first uses the SIMEX method and makes no assumption about the distribution of the unobserved error-prone predictor. For this approach we derive an asymptotic theory for kernel regression which has some surprising implications. Penalised regression splines are also considered for fixed number of known knots. The second approach assumes that the errorprone predictor has a distribution of a mixture of normals with an unknown number of mixtures, and uses regression splines. Simulations illustrate the results.
Cooked, vacuum-packaged beef top rounds containing 3% sodium lactate (NaL) or 3 or 4% NaL in combination with 0.1 or 0.2% sodium propionate (NaP) were stored for up to 84 days at 4ЊC. Addition of any of the treatments greatly reduced total Aerobic Plate Counts (APC). Positive flavor notes associated with fresh beef were enhanced by ingredient addition and tended to be highest in roasts with 3% NaL ϩ 0.2% NaP. Cooked roast beef color was enhanced by addition of NaL alone or in combination with 0.1% NaP. Lipid oxidation and water activity were decreased by addition of NaL with NaP and pH and cooked yield were increased with any level or combination of ingredients.
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