Scientific Research Publishing

Read full paper at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53145#.VLXdcMnQrzE Author(s)    Bilal Khan 1 , Kirk Dombrowski 2 , Ric Curtis 2 , Travis Wendel 3   Affiliation(s) 1 Department of Math and Computer Science, John Jay College (CUNY), New York, USA . 2 Department of Sociology, University of Nebraska-Lincoln, Lincoln, USA . 3 St. Ann’s Corner of Harm Reduction, Bronx, USA . ABSTRACT This paper presents a new method for obtaining network properties from incomplete data sets. Problems associated with missing data represent well-known stumbling blocks in Social Network Analysis. The method of “estimating connectivity from spanning tree completions” (ECSTC) is specifically designed to address situations where only spanning tree(s) of a network are known, such as those obtained through respondent driven sampling (RDS). Using repeated random completions derived from degree information, this method forgoes the usual step of...

Read full paper at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=51324#.VGRwj2fHRK0 Author(s) Mahmoud Afshari 1* , Anoshirvan Ghaffaripour 2 Affiliation(s) 1 Department of Statistics, College of Science, Persian Gulf University, Bushehr, Iran . 2 Department of Statistics, College of Science, Yasouj University, Yasuj, Iran . ABSTRACT In this paper we consider sequences of observations that irregularly space at infrequent time in-tervals. We will discuss about one of the most important issues of stochastic processes, named Markov chains. We would reconstruct the collected imperfect data as a Markov chain and obtain an algorithm for finding maximum likelihood estimate of transition matrix. This a...

Read full paper at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=50304#.VDXaWlfHRK0 Author(s)   Jing Gao 1 , Wei Wang 2 Affiliation(s) 1 College of Sciences, Shanghai Institute of Technology, Shanghai, China . 2 Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China . ABSTRACT We consider LM tests for spatial correlations in the spatial error model (SEM) and spatial autoregressive model (SAM) with randomly missing data in the dependent variable. We derive the formulas of the LM test statistics and provide finite sample performance of the LM tests through Monte Carlo experiments. KEYWORDS LM Test , Spatial Correlations , Missing Dat...