Identification of continuous-time autoregressive processes from discrete-time data by replacing the differentiation operator by an approximation is considered. A linear regression model can then be formulated.
It has also aided the search for functional materials (e.g. superhard and electride materials)27,28,29,30,31,32,33,34,35,36,37,38. However, unlike structure prediction involving continuous variables, the search for the MEP is a discrete global optimization problem for solid-to-solid phase ...
Continuous variables are presented as means and standard deviation and categorical variables as numbers and percentages. Differences in lesion characteristics between groups with an FFR >0.80 and an FFR ≤0.80 were tested using an unpaired Student t test or chi-square test, as appropriate. Receiver-...
original feature kernels and weight\(w_r\)for the rationale feature kernels, where\(w_r\ge w_o\), since intuitively the rationale features are the ones that have the highest power to separate the OS flights from the NOS ones. However, to satisfy Mercer’s condition, we need to ensure ...
A solution to the MSSM problem is represented by two vectors of boolean decision variables R = (R1, . . . , Rm) for the rows and C = (C1, . . . , Cn) for the columns, with Ri ∈ {0, 1} and Cj ∈ {0, 1}. When a decision variable is equal to 1, its corresponding ...
{θ , α}best = argmaxθ ,α median(intra_MIs) median(inter_MIs) (1) MI values between variables X, RPKM expression vector of gene1/lncRNA1 across 24 tissues and Y, RPKM expres- sion vector of gene2/lncRNA2 across 24 tissues, is defined in terms of their marginal Shannon entropie...
a set of finite discrete variables as component behavioral modes, T a set of transitions among these modes, X the set of continuous vari- ables partitionned in state variables XX, output (observed) variables XY and input variables (commands) XU. E is a set of continuous ...
Interactions between the periodic terms and locality were also included in the model, together with other clinically meaningful two-way interactions. Continuous explanatory variables were centred for model fitting and variables were removed stepwise if the estimated p-value was >0.05, excepting the ...
As the multiplicative type perturbation reduces to the case-weights perturbation and individual influential observations can be identified when ni(xj)=1, it is a more appropriate scheme to apply when the variables in x are continuous in scale. However, when the variables in x are discrete and ...
V′=0.157486+0.1249074×Dwhere V′ denotes the VIIRS-like DMSP data and D denotes the original DMSP data. 3.1.3. Topography data Our topographical variables include elevation, slope, and hydrological networks. We used NASA's digital elevation model (DEM), which is the 2000 reprocessing of the...