BMCBI
2006
13 years 4 months ago
2006
Background: Serial Analysis of Gene Expressions (SAGE) produces gene expression measurements on a discrete scale, due to the finite number of molecules in the sample. This means t...
BMCBI
2006
13 years 4 months ago
2006
Background: Determining whether a gene is differentially expressed in two different samples remains an important statistical problem. Prior work in this area has featured the use ...
CSDA
2008
13 years 4 months ago
2008
Sociologists, demographers, and economists often use the index of dissimilarity, D, to describe the extent of racial, ethnic, spatial, or areal dissimilarity (or segregation) of d...
BMCBI
2008
13 years 4 months ago
2008
Background: Proteomic profiling using mass spectrometry (MS) is one of the most promising methods for the analysis of complex biological samples such as urine, serum and tissue fo...
BMCBI
2007
13 years 4 months ago
2007
Background: In many laboratory-based high throughput microarray experiments, there are very few replicates of gene expression levels. Thus, estimates of gene variances are inaccur...
BMCBI
2007
13 years 4 months ago
2007
Background: Classifying nuclear magnetic resonance (NMR) spectra is a crucial step in many metabolomics experiments. Since several multivariate classification techniques depend up...
WSC
1994
13 years 5 months ago
1994
Overlapping batch statistics estimate the variance of point estimators using overlapping batches (Schmeiser, Avramidis and Hashem 1990). In this paper we study sufficient conditio...
WSC
1998
13 years 5 months ago
1998
When pricing options via Monte Carlo simulations, precision can be improved either by performing longer simulations, or by reducing the variance of the estimators. In this paper, ...
AIPS
1998
13 years 5 months ago
1998
Westudytheruntimeprofilesofcompletebacktrackstylesearchmethodsappliedtohardschedulingproblems.Suchsearchmethodsoftenexhibita largevariabilityinperformanceduetothenon-standardnatur...
UAI
2001
13 years 5 months ago
2001
There exist a number of reinforcement learning algorithms which learn by climbing the gradient of expected reward. Their long-run convergence has been proved, even in partially ob...