Utvärdering av kvalité och dess behov inom förprovtryck

It always has been a need for the abiltiy to create color proofs. When an error occurs late in the production process, itis allways complicated and difficult to correct the error. In this project, digital proofs been made and discussions havebeen held with several people in the printing industry, in order to examine how well excisting digital proofs, meet thedemand of the market. And how close the digital proofs can come to the actual printsheat from the press. The study hasbeen shown that the one thing that has had the most influence on the outcome for the quality of a digital proof, is theprintshop operator’s knowledge about color management and proofing systems. Many advertising agencies in the graphicindustry think rasterised proofs are not necessesary and expensive. Therefor they prefer a cheaper alternative, whichdoesn’t show colors as well as the rasterised proof, but well enough to be content with it. There are a good awarenessconcerning lack of communication between printshop, reproduction and advertising agency. Advertising agencies thinkthat printshop rarely listen to what they have to say, while the printshop think that the advertising agency doesn’t understandwhat they are trying to tell them. The outcome of the printed proofs in this study can’t be representive for howgood digital proofs are conducted in regular basis in the industry. The divergence between the print press sheat and thedigital proof that was made was bigger than expected. This shows that implementation of ICC profiles in a color managementflow, not alone is the answer to making perfect digital proofs. There are so many other issues that has to be examined,like color management software, measure tools and correct color management module. In order to make a perfectproof, you have to look at the whole picture. In the end, the human eye finally has the last word on wheather theproof is good or not.

Likelihood prediction for generalized linear mixed models under covariate uncertainty

This paper presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction is explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. The paper outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson error-in-variable generalized linear model, it has been shown that in complicated cases LP produces better results than already know methods.