The Taishō edition of the Chinese Buddhist canon (1924-1932) collects ca. 1000 Indian texts that were translated into Chinese between the 2nd and the 11th century CE. 153 of these texts are marked as 失譯 indicating that the name(s) of the translator(s) are unknown. For the texts translated between the 2nd and the late 6th century, however, we have to confront the dilemma that many attributions are uncertain, problematic or simply wrong. Over the years Buddhist scholars have leveraged traditional text-critical methods to corroborate or dispute traditional attributions. Although these methods method can produce high quality results, they often rely heavily on the intuition of a single scholar honed over many years of research. Information technology offers an alternative vector of inquiry that aims to complement rather than supersede more traditional approaches. For this we will adopt statistical, quantitative methods and artificial intelligence algorithms to analyze ancient Buddhist texts translated into Chinese in order to discover new evidence to address the translator attribution. The major advantage of stylometrics and quantitative authorship attribution is being able to discover hidden patterns, which cannot be discerned by traditional approaches. In the past four decades, considerable attention has been paid to quantitative authorship attribution of literature in western languages; however, there have been only few attempts focusing on texts written in classical Chinese, much less in the particular form of ‘Indian Buddhist Chinese’ of early translated texts. In this paper, our main focus will be on grammatical particles (xuci 虛詞) that are widely used in classical Chinese to express grammatical relations. After measuring their occurrence in Indian Buddhist Chinese we use Principle Component Analysis (PCA) to discuss how their use reflects on the authorship of some selected sutras. Our analysis explores different scenarios that have to be accounted for such as a translator changing his style in the course of his career, how to understand commonalities between of contemporaneous translations, and how to quantify the difference between different translations of the same sutra. Also, in latter part of this thesis, we apply our analysis model with the three different translations of Gandhavyūha from different dynasties. We have conduct a series of experiments with different values of arguments. Through the experiments, we demonstrates that the T.278 was translated three to four hundred years earlier than T.279 and T.293 and shows which of its features can identify it as an earlier text.