Avoiding bias in mixed model inference for fixed effects

PARAMETRIC BOOTSTRAP INTERVAL APPROACH TO INFERENCE FOR FIXED EFFECTS IN THE MIXED LINEAR MODEL BY Copyright 2009

Improved inference on a scalar fixed effect of interest in nonlinear mixed-effects models

Likelihood-based inference on a scalar fixed effect of interest in nonlinear mixed-effects models usually relies on first-order approximations. If the sample size is small, tests and confidence intervals derived from first-order solutions can be inaccurate. An improved test statistic based on a modification of the signed likelihood ratio statistic is presented which was recently suggested by Sk...

Avoiding model selection bias in small-sample genomic datasets

MOTIVATION Genomic datasets generated by high-throughput technologies are typically characterized by a moderate number of samples and a large number of measurements per sample. As a consequence, classification models are commonly compared based on resampling techniques. This investigation discusses the conceptual difficulties involved in comparative classification studies. Conclusions derived f...

Avoiding Bias in Observational Studies

T he randomized clinical trial is a well established and frequently used study design and is generally accepted as the gold standard (1). Nevertheless, many questions can only be answered with epidemiological observational studies, such as the influence of cigarette consumption on the development of lung cancer (2), the effects of sport, nutrition and overweight on cardiovascular diseases (3), ...

Avoiding bias in cards cryptography

Public key cryptography bases its security on mathematical problems that are computationally hard to solve. There are also cryptographic protocols wherein discovering the secret is not too complex given the current state of technology, but logically impossible. One approach involves the use of a random deck of cards for such protocols. The messages in the protocol consist of announcements made ...