Authorship attribution is one of the important problem, with many applications of practical use in the real-world. Authorship identification determines the likelihood of a piece of writing produced by a particular author by examining the other writings of that author. Most of the research in this field is carried out by using instance based model. One of the disadvantages of this model is that it treats the different documents of each author differently. It produces a matrix per each document of the author, thus creating a huge number of matrices per author, i.e. the dimensionality is very high. This paper presents authorship identification using Author based Rank Vector Coordinates (ARVC) model. The advantage of the proposed ARVC model is that it integrates all the author's profile documents into a single integrated profile document (IPD) and thus overcomes the above disadvantage. To overcome the ambiguity created by common words of authors ARVC model removes the common words based on a threshold. Singular value decomposition (SVD) is used on IPD after removing the common words. To reduce the overall dimension of the matrix, without affecting its semantic meaning a rank-based vector coordinates are derived. The eigenvector features are derived on ARVC model. The present paper used cosine similarity measure for author attribution and carries out authorship attribution on English poems and editorial documents