Although I theoretically agree with this, I think assigning the reads in the right transcripts is not trivial (whereas gene-level quantification are very consistent among different tools).

As read length increases and coverage becomes more even, I might change my mind, but I think RPKM is currently the most practical strategy and tools using RPKM measurements (such as limma) are known to provide accurate results. For example, you can check out these differential expression comparisons:

http://bib.oxfordjournals.org/content/early/2013/12/02/bib.bbt086.long

"In general, limma performed well under many circumstances in the present comparisons, being also computationally fastest to run."

http://genomebiology.com/2013/14/9/R95

"We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq."

http://cdwscience.blogspot.com/2013/11/rna-seq-differential-expression.html

*Recommend Partek or DESeq over edgeR or cuffdiff

Lior Pacther's slides and presentation are helpful. Here is another another helpful video from one of his students, with even more explicit discussion about FKPM and TPM:

I'm not a statistician, but I remember that z scores assume normal distribution of data. Right? I think RNA-seq doesn't follow normal distribution and hence you will have a significant error in your estimation.