'less stringent assumptions' is correct and usable in written English.
You can use it when you need to describe assumptions that are not overly strict or exacting. For example: "We decided to use less stringent assumptions when estimating the cost of the project, so that our budget would be more achievable."
Using this lemma, we have a similar conclusion to Theorem 3.1 with less stringent assumptions.
However, the convergence to a unique fixed point in the set is not proven under those less stringent assumptions.
These approaches are advantageous to traditional techniques (e.g. deletion and mean imputation techniques) because they require less stringent assumptions and mitigate the pitfalls of traditional techniques.
My less stringent assumptions are required in order to obtain direct estimates of the response of unemployment duration to changes in available UI weeks, which in turn enables direct comparison of my results to prior papers.
Others have developed accounts of mutual knowledge, approximate common knowledge, and common belief which require less stringent assumptions than the standard account, and which serve as more plausible models of what agents know in cases where strict common knowledge seems impossible (Brandenburger and Dekel 1987, Stinchcombe 1988, Monderer and Samet 1989, Rubinstein 1992).
We thus conclude that both methods, being upwind schemes designed for the ACM, have the same performances in terms of accuracy and convergence speed, even though the revised method is more complex with less stringent assumptions made, while the original CB method is simpler due to the use of extra simplifying assumptions.
In contrast, random models only need the less stringent assumption of missing at random (MAR).
In order to minimize bias in describing how the hazard of injury varies with experience, we employed multiple imputation (MI) techniques, which provide statistically valid results when the data are missing at random (MAR), a less stringent assumption about missingness that allows missingness to be correlated with observed variables only (e.g., gender) [ 26].
IE infers evolution based only on observed (extant and/or fossil) variation and phylogenetic relatedness (no a priori model parameters), and therefore has less stringent data assumptions and a decreased reliance on a prior model assumptions [ 113].
(2) Would less stringent epistemic assumptions suffice to justify Nash equilibrium behavior in a coordination problem?
The GEE approach is widely used due to its less stringent distributional assumptions and robustness properties, yielding valid inference regardless of the data distribution [42].
