The phrase "associated samples" is correct and usable in written English.
It is typically used in statistical contexts to refer to samples that are related or paired in some way, often in experiments or studies.
Example: "In our study, we analyzed the associated samples to determine the effects of the treatment over time."
Alternatives: "paired samples" or "related samples".
We give rates of uniformly asymptotic normality for associated samples.
Wu [47] discussed strong consistency of an M-estimator in the model (1.1) for negatively associated samples.
We multiplied the fresh weight by the ratio of dry weight to fresh weight of the associated samples to derive dry weight or biomass values.
In this paper, we discuss the uniformly asymptotic normality of the weighted function estimate of the fixed design regression model for negatively associated samples.
However, the lack of clearly associated samples from Kayhude and Schlamersdorf makes it difficult to give more than rough estimates of the FRE.
See recent works, for example, Antoniadis et al. [16] and Xue [17] on independent errors; Johnstone and Silverman [18] for correlated noise; Liang et al. [19] on martingale difference errors; Li and Xiao [20] for long memory data; Li et al. [21] on associated samples; Xue [22], Sun and Chai [23], Li and Guo [24] and Liang [25] on mixing error assumptions.
Notation: The continuous time domain signal is denoted by x t); x[k] is the associated sampled discrete time domain signal; and the frequency domain representation of the discrete sample x[k] is X[k].
Fig. 3 Vessel 1978.0161 (overall and detail of adhesive sample location analysed) and associated sample IR spectrum (s1374) identified to be CN based on match with control adhesive reference database.
Under a negatively associated sample, Liang and Baek [17] studied the Berry-Esséen bound and obtained the rate O ( ( log n n ) 1 / 6 ) under some conditions (see Remark 3.1 of Liang and Baek [17]).
However, in a speech recognition application with a large number of voice-tags, the recognition accuracy of each voice-tag tends to increase as its number of associated sample utterances increases.
Lastly, we multiplied each calculated inter-sample normalization factor by all of the net MFI results within each associated sample.
