ライフサイエンスコーパス: moving average

1 moment-to-moment ISCs computed using a 17-s moving average.

2 regressive moving-average models with 2-year moving averages.

3 n index and augmentation pressure at several moving averages.

4 ed statistical significance for 3- and 4-day moving averages.

5 BC moving averages (5-day to 28-day) were associated with i

6 lysis and T-wave alternans (TWA) by modified moving average analysis.

7 was assessed from digitized ECGs by modified moving average analysis.

8 TWA was examined by the modified moving average and intrabeat average analyses.

9 th higher levels of BNP beginning with 2-day moving averages and reached statistical significance for

10 ewhart control chart, exponentially weighted moving average, and cumulative sum charts provide a mean

11 on of colic with certain covariates, using a moving average approach to conditional logistic regressi

12 tion structure and autoregressive integrated moving average (ARIMA) models, magnitude, and age distri

13 ing is based on parameters of autoregressive moving average (ARMA) models of the probed dynamics.

14 TWA magnitude was measured by modified moving average beat analysis, and the complexity of T-wa

15 s (from 35 to >/= 85 years) using three year moving averages between 1982 and 2006.

16 ewhart control chart, exponentially weighted moving average chart, and cumulative sum chart.

17 In Poisson generalized linear models, 3-day moving average concentrations of ozone, nitrogen dioxide

18 An interquartile-range increase of the moving average concentrations of same-day and previous-d

19 Chicago air, examining plots of the 365-day moving average concentrations shows that they do not dec

20 os, the risk-adjusted Exponentially Weighted Moving Average control chart had the shortest median tim

21 The risk-adjusted Exponentially Weighted Moving Average control chart signaled the fastest in mos

22 for the risk-adjusted Exponentially Weighted Moving Average control chart.

23 ime-updated 12 month lagged, 24 month simple moving average cumulative exposure, increased risk of lu

24 A regression with autoregressive moving average errors model was employed to adjust for s

25 onfidence interval: 1.11, 1.15) for 12-month moving average exposures.

26 e by using fixed monitors, and we determined moving averages for 1-7 days preceding vascular testing.

27 Risk ratios generally increased with longer moving averages; for example, an elevation in 60-month m

28 We assessed associations between the 3-day moving average (lag 0-1-2) of Betulaceae (except Alnus s

29 daily counts of ED visits and either the 3-d moving average (lag 0-2) of OP(DTT) or same-day OP(DTT).

30 Our approach is to consider the set of moving-average (linear) processes and study its closure

31 Neighboring probes are combined through a moving average method (MA) or a hidden Markov model (HMM

32 Spectral Method and the time-domain Modified Moving Average method have demonstrated the utility of T

33 ocardiogram-based TWA analysis with Modified Moving Average method have yielded significant predictiv

34 A and TWA were measured by enhanced modified moving average method.

35 After fitting an autoregressive integrative moving average model and taking the residuals, all pairw

36 and used segmented autoregressive integrated moving average models for the analysis.

37 yzed trends using generalized autoregressive moving-average models with 2-year moving averages.

38 ode decomposition detrending and (2) a novel Moving Average (MVG) detrending approach.

39 quartile range increase (2.5 ng/m3) in 7-day moving-average Ni was associated with 2.48-mmHg (95% CI:

40 ormance of embedded digital filters, namely, moving average, numerically simulated low pass RC, and G

41 hou when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 382.

42 hat when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 99.3, there wa

43 an, when the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 68.1, the chan

44 When the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 91.8, there wa

45 A 5 degrees C change in the 4-day moving average of apparent temperature was associated wi

46 er, slope, distances to larger cities, and a moving average of current population, were locally adapt

47 We use a moving average of retained and non-retained genes to fin

48 Country-years were grouped by 10-year moving average of routine measles vaccination coverage (

49 nfounders, a 3.4-mug/m(3) increment in 2-day moving average of same-day and previous-day nitrate conc

50 he strongest associations were observed with moving averages of 2-7 days after a lag of several days.

51 regression model as continuous functions of moving averages of air pollution exposures (over 4 hours

52 Six-day moving averages of all 4 particle metrics were associate

53 A 5 degrees C change in 3- and 4-day moving averages of apparent temperature was associated w

54 We investigated the effects of moving averages of black carbon of 1-5 years before the

55 The 1- and 2-day moving averages of PM2.5, NO2, and O3 before testing wer

56 e also examined linear relationships between moving averages of pollutant concentrations 1, 2, 3, 5,

57 mparison of pp32 with the pp32r1 sequence by moving averages of sequence identity reveals divergence

58 The estimated effect of 28-day BC moving average on systolic BP was 1.95-fold larger for i

59 ssive symptoms, and significantly for 30-day moving average (OR = 1.16; 95% CI: 1.05, 1.29) upon SES

60 toms, with the largest increase for 180-days moving average (OR = 1.61; 95% CI: 1.35, 1.92) after adj

61 ial proportional odds models, both CMV 7-day moving average (OR, 5.1; 95% CI, 2.9-9.1; P < .001) and

62 hat interquartile range increases in 24-hour moving average particulate matter less than 2.5 mum in a

63 nificantly positive associations of 12-month moving average PM2.5 exposures (per 10-mug/m3 increase)

64 rages; for example, an elevation in 60-month moving average PM2.5 exposures was linked to 1.33 times

65 sis using seasonal autoregressive integrated moving-average (SARIMA) models.

66 Non-Gaussian stationary autoregressive moving average sequences are considered.

67 double error shrinkage (DES) method by using moving average statistics based on local-pooled error es

68 In the standard arm, the 2-week moving average systolic BP did not change significantly

69 sequences by a time-dependent autoregressive moving average (TD-ARMA) process.

70 bias variability was refined by employing a moving average technique.

71 ery standard deviation increase in 28-day BC moving average was associated with 0.12 standard deviati

72 standard deviation increase in the 28-day BC moving average was associated with 1.97 mm Hg (95% confi

73 A 3-year, centered, moving average, which was used to display time trends in

74 (TF)-binding sites measured within a 200-bp moving average window through phylogenetically conserved

75 theory is applied (Nonlinear AutoRegressive Moving Average with eXogenous input - NARMAX) to assess

76 tified by a NARMAX (nonlinear autoregression moving average with exogenous input) representation fami

77 arbon, and PM2.5 mass concentrations (4-week moving average) with DNA methylation [expressed as the p

78 the Boston, Massachusetts, area (1- to 4-day moving averages) would be associated with higher levels