We study the structural variation of networks formed by connecting Standard & Poor’s 500 (S&P500) stocks that were traded from January 1, 2000 to December 31, 2004. The construction of the network is based on cross correlation between the time series of the closing prices (or price returns) over a fixed trading period and takes a simple winner-take-all approach for establishing connections betw...

In this paper we study the structural variation of the network formed by connecting Standard & Poor’s 500 (S&P500) stocks whose closing prices (or price returns) are highly correlated. Specifically we consider S&P500 stocks that were traded from January 1, 2000 to December 31, 2004, and construct complex networks based on cross correlation between the time series of the closing prices (or price...

Employing data on the assessed value of land in 1974–2007 Japan, we exhibit a quasistatically varying log-normal distribution in the middle scale region. In the derivation, a Non-Gibrat’s law under the detailed quasi-balance is adopted together with two approximations. The resultant distribution is power-law with the varying exponent in the large scale region and the quasistatic log-normal dist...

In this paper we consider the problem of estimating a parameter of a probability distribution when we have some prior information on a nuisance parameter. We start by the very simple case where we know perfectly the value of the nuisance parameter. The complete likelihood is the classical tool in this case. Then, progressively, we consider the case where we are given a prior probability distrib...