A-7 Statistical Report On FIP Applications And Cases Discontinued, April 2004

Monthly statistical report on FIP by the Iowa Department of Human Services

A-7 Statistical Report On FIP Applications And Cases Discontinued, December 2004

Monthly statistical report on FIP by the Iowa Department of Human Services

A-1 Monthly Public Assistance Statistical Report Family Investment Program, November 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - Nov 2007

Further evidence on the statistical properties of real GNP

The well-known lack of power of unit root tests has often been attributed to the shortlength of macroeconomic variables and also to DGP s that depart from the I(1)-I(0)alternatives. This paper shows that by using long spans of annual real GNP and GNPper capita (133 years) high power can be achieved, leading to the rejection of both theunit root and the trend-stationary hypothesis. This suggests that possibly neither modelprovides a good characterization of these data. Next, more flexible representations areconsidered, namely, processes containing structural breaks (SB) and fractional ordersof integration (FI). Economic justification for the presence of these features in GNP isprovided. It is shown that the latter models (FI and SB) are in general preferred to theARIMA (I(1) or I(0)) ones. As a novelty in this literature, new techniques are appliedto discriminate between FI and SB models. It turns out that the FI specification ispreferred, implying that GNP and GNP per capita are non-stationary, highly persistentbut mean-reverting series. Finally, it is shown that the results are robust when breaksin the deterministic component are allowed for in the FI model. Some macroeconomicimplications of these findings are also discussed.

A-1 Monthly Public Assistance Statistical Report Family Investment Program, January 2008

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 Monthly Public Assistance Statistical Report Family Investment Program, Februray 2008

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 Monthly Public Assistance Statistical Report Family Investment Program, March 2008

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 Monthly Public Assistance Statistical Report Family Investment Program, April 2008

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 Monthly Public Assistance Statistical Report Family Investment Program, May 2008

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 Monthly Public Assistance Statistical Report Family Investment Program, December 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 Monthly Public Assistance Statistical Report Family Investment Program, October 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

Iowa Individual Income Tax Statistical Report 2006

Iowa Individual Income Tax Statistical Report 2006

Database construction and Peptide identification strategies for proteogenomic studies on sequenced genomes.

Since the advent of high-throughput DNA sequencing technologies, the ever-increasing rate at which genomes have been published has generated new challenges notably at the level of genome annotation. Even if gene predictors and annotation softwares are more and more efficient, the ultimate validation is still in the observation of predicted gene product( s). Mass-spectrometry based proteomics provides the necessary high throughput technology to show evidences of protein presence and, from the identified sequences, confirmation or invalidation of predicted annotations. We review here different strategies used to perform a MS-based proteogenomics experiment with a bottom-up approach. We start from the strengths and weaknesses of the different database construction strategies, based on different genomic information (whole genome, ORF, cDNA, EST or RNA-Seq data), which are then used for matching mass spectra to peptides and proteins. We also review the important points to be considered for a correct statistical assessment of the peptide identifications. Finally, we provide references for tools used to map and visualize the peptide identifications back to the original genomic information.