Sentence examples for measurement error problems from inspiring English sources

One possibility would be to consider the stochastic imputation approach developed for missing data, which has been extended to tackle measurement error problems (e.g. Brownstone and Valletta 1996;Freedman et al. 2008).

Measurement error problems are common in a variety of fields.

The EM method can be applied to measurement error problems by treating unobservable true x gi as missing data values [ 2].

In countless number of clinical trials, measurements of outcomes rely on instrument questionnaire items which however often suffer measurement error problems which in turn affect statistical power of study designs.

Not only a larger number of individuals studied, but the correlation structure from adjacent probes on the microarray is more complex, peaks are expected to be more variable in size and shape and in certain applications more numerous (requiring different approaches for multiple comparisons) and there can be more serious measurement error problems.

Overall, they are roughly comparable in magnitude to my three-step GMM estimates when using the zero-stage prediction to measure the expected exchange rate change (recall the values of the last and second to the last column in Table 7) and thus support this solution to the measurement error problem.

In this paper, to derive closed-from power functions, we formulate a statistical model for parallel items that relates the item scores to a measurement error problem.

In theory, one way to overcome the measurement error problem is to use an external data set with more accurate data to quantify the error and thereafter correct for it in the primary data set [ 10].

In statistical literature, exposure misclassification and miss-measurement are known as a measurement error problem and a plethora of approaches exist to correct for biases that arise from it under certain assumptions [ 4, 5].

Applications include a number of useful generalisations of the basic case-control design and a broad class of missing data and measurement-error problems.

Longitudinal continuous monitoring however, is often incomplete due to measurement errors, hardware problems or insufficient sampling frequency.