ライフサイエンスコーパス: autoregressive

1 the variance in microbial time series is non-autoregressive.

2 The primary outcome was 6 weeks mixed model autoregressive analysis of Cohen-Mansfield Agitation Inv

3 schemes for model building were computed for autoregressive and classic Fourier spectra by using 75%

4 We show how changes in diet are also non-autoregressive and that Operational Taxonomic Units stro

5 The autoregressive (AR) filter offers the advantage of smoot

6 Autoregressive (AR) modeling within and between states e

7 noise correlation is accounted for using an autoregressive (AR) process.

8 Food intake can be regarded as an autoregressive (AR) time series, as the amount of food e

9 with population (dynamical feedback) models: autoregressive [AR(1)] and sinusoidal (1/f) models.

10 this model are compared with those from the autoregressive-based functional mapping approach.

11 data sets, we propose the use of Conditional Autoregressive (CAR) models.

12 Autoregressive centre frequencies averaged 0.012 +/- 0.0

13 e predictions of plaque composition from the autoregressive classification scheme.

14 Autoregressive classification schemes performed better t

15 Autoregressive classification schemes performed better t

16 find a strong phylogenetic signal in the non-autoregressive co-variance from our sVAR model residuals

17 thod developed for econometrics-to model the autoregressive component of gut community dynamics.

18 with dietary variables have much less of an autoregressive component to their variance, which sugges

19 g a generalized additive mixed model with an autoregressive component.

20 ts generalized least squares regression with autoregressive disturbance.

21 g spatial autoregressive models with spatial autoregressive disturbances.

22 ethod represents gene-expression dynamics as autoregressive equations and uses an agglomerative proce

23 essed using segmented linear regression with autoregressive error models, incorporating autocorrelati

24 improved method that utilizes a first-order autoregressive fitting technique to the PCA spectrum.

25 hat query information significantly improves autoregressive inferences, obtaining state-of-the-art pe

26 rtality peaks, autocorrelation structure and autoregressive integrated moving average (ARIMA) models,

27 utilization and outcomes and used segmented autoregressive integrated moving average models for the

28 Time-series analysis using seasonal autoregressive integrated moving-average (SARIMA) models

29 After fitting an autoregressive integrative moving average model and taki

30 A stepwise autoregressive method and exponential smoothing models w

31 lso show that the memory coefficients of the autoregressive model are modulated by pacing stochastici

32 We used a spatial intrinsic conditional autoregressive model for multivariable analysis and exam

33 es a forward-in-time version of the familiar autoregressive model for the exposure time series, while

34 Using a linear autoregressive model in combination with long-term zoopl

35 utility of search data relative to a simple autoregressive model is modest.

36 twork interactions are assumed linear and an autoregressive model is specified, augmented with indica

37 random effects in an intrinsic conditionally autoregressive model within a Bayesian framework.

38 e augment the query-only predictions with an autoregressive model, injecting prior knowledge about th

39 e accuracy to real-time predictions using an autoregressive model.

40 ood pressure variability were analyzed using autoregressive modeling.

41 Underlying the analysis is an autoregressive modelling approach used to identify clima

42 microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra).

43 Seasonal autoregressive models captured a substantial amount of d

44 Seasonal autoregressive models including trigonometric function t

45 in regions was estimated using multivariate, autoregressive models of hidden neuronal variables obtai

46 Three Bayesian spatiotemporal conditional autoregressive models were fitted at the postal area lev

47 ls and evaluated the performance of seasonal autoregressive models with and without climate variables

48 olling for ecological factors, using spatial autoregressive models with spatial autoregressive distur

49 Multivariate autoregressive models, estimated from spontaneous spikin

50 A time series analysis, using autoregressive models, was undertaken on incidence of mi

51 tly improve the predictive power of seasonal autoregressive models.

52 Clustering is based on parameters of autoregressive moving average (ARMA) models of the probe

53 ionary genomic sequences by a time-dependent autoregressive moving average (TD-ARMA) process.

54 A regression with autoregressive moving average errors model was employed

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

56 ng from control theory is applied (Nonlinear AutoRegressive Moving Average with eXogenous input - NAR

57 period, we analyzed trends using generalized autoregressive moving-average models with 2-year moving

58 analysis, then compute a Multivariate Vector AutoRegressive (MVAR) model from the time series of the

59 f interest were fit into linear multivariate autoregressive (MVAR) models.

60 for instantaneous identification considering autoregressive nonlinearities up to the third-order acco

61 We performed fast Fourier transform (and autoregressive) power spectral analyses and integrated l

62 l and modeled using an intrinsic conditional autoregressive prior, allowed estimation of the residual

63 el considered here is a nonstationary vector autoregressive process in which some linear functions ar

64 terized by short-term memory described by an autoregressive process of first order.

65 ce temperature consistent with a first-order autoregressive process.

66 and show that it behaves approximately as an autoregressive process.

67 aracteristic is represented as a first-order autoregressive process.

68 The structure of non-Gaussian autoregressive schemes is described.

69 Bayesian hierarchical intrinsic conditional autoregressive spatial probit models were used to determ

70 We used multivariate autoregressive state-space (MARSS) models to describe tr

71 Gompertz population models and multivariate autoregressive state-space (MARSS) models were fitted, i

72 h abundances (1994-2008), using Multivariate Autoregressive State-Space (MARSS) models.

73 ramework to describe the APD signal using an autoregressive stochastic model, and we establish the in

74 dependency is modeled by Leroux conditional autoregressive structure; the dependency of methylation

75 s the exchangeable, multiset coefficient and autoregressive structures for model reduction, and their

76 forts to develop forecasting tools including autoregressive time series, climate-driven statistical,

77 Autoregressive variance appears to be driven by multi-da