Interval estimation of the mean of a normal distribution based on quantized observations

Year, pages: 2009, 627 - 645

We consider the problem of making statistical inference about the mean of a normal distribution based on a random sample of quantized (digitized) observations. This problem arises, for example, in a measurement process with errors drawn from a normal distribution and with a measurement device or process with a known resolution, such as the resolution of an analog-to-digital converter or another digital instrument. In this paper we investigate the effect of quantization on subsequent statistical inference about the true mean. If the standard deviation of the measurement error is large with respect to the resolution of the indicating measurement device, the effect of quantization (digitization) diminishes and standard statistical inference is still valid. Hence, in this paper we consider situations where the standard deviation of the measurement error is relatively small. By Monte Carlo simulations we compare small sample properties of the interval estimators of the mean based on standard approach (i.e. by ignoring the fact that the measurements have been quantized) with some recently suggested methods, including the interval estimators based on maximum likelihood approach and the fiducial approach. The paper extends the original study by Hannig et al. (2007).

Witkovský, V., Wimmer, G. 2009. Interval estimation of the mean of a normal distribution based on quantized observations. In Mathematica Slovaca, vol. 59, no.5, pp. 627-645. 0139-9918. DOI: https://doi.org/10.2478/s12175-009-0152-1

Witkovský, V., Wimmer, G. (2009). Interval estimation of the mean of a normal distribution based on quantized observations. Mathematica Slovaca, 59(5), 627-645. 0139-9918. DOI: https://doi.org/10.2478/s12175-009-0152-1