"exponential shrinkage" is a correct and usable phrase in written English.
It can be used to describe a rapid and significant decrease or reduction in something. It is often used in technical or scientific contexts, such as in the field of mathematics, statistics, or economics. Example: "The company's profits experienced exponential shrinkage in the last quarter, leading to a major restructuring of their business model."
We compared several penalties, including the elastic net, ridge, Lasso, MCP and the normal-exponential-γ shrinkage prior implemented in the hyperlasso software, to standard single locus analysis and simple forward stepwise regression.
We also performed separate LAMARC analyses to test for signatures of exponential growth or shrinkage using the same searching strategy.
Wang et al. [ 19] suggested a model with exponential drug-dependent shrinkage and linear growth with time (equation 1) to describe tumour SLD data from four clinical trials of patients with NSCLC treated with various chemotherapies or placebo.
We used the normal exponential gamma distribution (NEG) shrinkage prior with shape parameter 1.0, as recommended by Vignal et al. (2011).
Data were fitted to an exponential rise equation, and the initial shrinkage rate (k) was determined by the average of at least three fitted equations.
This value τ followed an exponential distribution Exp, where λ is a regularization parameter for the shrinkage of marker effect estimates.
In BL, marker effects were assumed to follow a double-exponential distribution, with a parameter τ regulating the amount of shrinkage.
In Part II of this investigation, a sintering model describing the evolution of shrinkage is developed using a Weibull power law time-dependent exponential equation, consistent with the Kolmogorov Johnson Mehl Avrami (KJMA) kinetic theory of nucleation and growth.
Nonetheless, the normalized overall shrinkage response was approximately represented by the KWW stretched-exponential growth curve [ 10, 16].
In the LASSO, the distribution of SNP effects are assigned a double exponential (DE) prior as which states that the marker-specific prior is Gaussian, with marker-specific shrinkage of effects depending on τ, which is in turn controlled by the prior distribution of the regularization parameter (p [ 37].
