High-density oligonucleotide arrays (GeneChip, Affymetrix, Santa Clara, CA) have become a standard research tool in many areas of biomedical research. They quantitatively monitor the expression of thousands of genes simultaneously by measuring fluorescence from gene-specific targets or probes. The relationship between signal intensities and transcript abundance as well as normalization issues have been the focus of much recent attention (Hill et al., 2001, Chudin et al., 2002, Naef et al., 2002a). It is desirable that a researcher has the best possible analytical tools to make the most of the information that this powerful technology has to offer. At present there are three analytical methods available: the newly released Affymetrix Microarray Suite 5.0 (AMS) software that accompanies the GeneChip product, the method of Li and Wong (LW, Li and Wong, 2001), and the method of Naef et al. (FN, Naef et al., 2001). The AMS method is tailored for analysis of a single microarray, and can therefore be used with any experimental design. The LW method on the other hand depends on a large number of microarrays in an experiment and cannot be used for an isolated microarray, and the FN method is particular to paired microarrays, such as resulting from an experiment in which each treatment sample has a corresponding control sample. Our focus is on analysis of experiments in which there is a series of samples. In this case only the AMS, LW, and the method described in this paper can be used. The present method is model-based, like the LW method, but assumes multiplicative not additive noise, and employs elimination of statistically significant outliers for improved results. Unlike LW and AMS, we do not assume probe-specific background (measured by the so-called mismatch probes). Rather, we assume uniform background, whose level is estimated using both the mismatch and perfect match probe intensities.