The special properties of the chaos make the chaotic encryption technology to be an important research field of information science and technology. However, because digital chaos is affected by finite precision of computer, chaos system properties present degradation—short periodicity. This paper firstly analyses reason that causes strong autocorrelation of digital chaotic sequence, then uses s...

In a first part is recalled a simulation method for a strictly stationary non-Gaussian process given its one-dimensional marginal distribution (or its -first statistical moments) and its autocorrelation function. This method was already widely treated in the articles [14] and [13]. The objective of the present paper is twofold : firstly simplify this method when by Mehler formula it is possible...

The purpose of this research is to verify the impact corporate social responsibility and managerial ownership towards firm value with profitability as an intervening variable. independent variables used are ownership. dependent variable value. profitability. population in mining companies listed on Indonesian Stock Exchange 2016-2018 period. sampling method purposive a sample 22 for 3 years so ...

Survival is a key fitness component and the evolution of age- and stage-specific patterns in survival is a central question in evolutionary biology. In variable environments, favouring chances of survival at the expense of other fitness components could increase fitness by spreading risk across uncertain conditions, especially if environmental conditions improve in the future. Both the magnitud...

Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful, patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more diicult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial...

Spatial autocorrelation analysis tests whether the observed value of a variable at one locality is significantly dependent on values of the variable at neighbouring localities. The method was extended by us in an earlier paper to include the computation of correlograms for spatial autocorrelation. These show the autocorrelation coefficient as a function of distance between pairs of localities, ...

Virtually all remotely sensed data contain spatial autocorrelation, which impacts upon their statistical features of uncertainty through variance inflation, and the compounding of duplicate information. Estimating the nature and degree of this spatial autocorrelation, which is usually positive and very strong, has been hindered by computational intensity associated with the massive number of pi...

Abstract High-frequency seismic data on Mars are dominated by wind-generated lander vibrations, which radiated partially to the subsurface. Autocorrelation functions (ACFs) of filtered between 1 and 5 Hz show clear phases at ∼1.3, ∼2.6, ∼3.9 s. Daily temporal changes their arrival times (dt/t) correlate well with daily ground temperature, ∼5% variation ∼50 min apparent phase delay. The followin...

where x̄ and ȳ are the sample means of both variables. ρ measures whether, on average, xi and yi are associated. For a single variable, say x, I will measure whether xi and xj , with i 6= j, are associated. Note that with ρ, xi and xj are not associated since the pairs (xi, yi) are assumed to be independent of each other. In the study of spatial patterns and processes, we may logically expect th...