ライフサイエンスコーパス: mean squared error

1 han those of CLS and PLS methods in terms of mean squared error.

2 chieved, bias, standard error, coverage, and mean squared error.

3 ction function with the best cross-validated mean squared error.

4 ed the effect estimate, percentage bias, and mean squared error.

5 specific PPVs in order to reduce the overall mean squared error.

6 ely used methods in terms of normalized root mean squared error.

7 than the PTDM approach (Cox: bias = -0.002, mean squared error = 0.025; PTDM: bias = -1.411, mean sq

8 metabolites (cross-validated R(2) = 0.82 and mean squared error = 0.14).

9 VPG had a Pearson correlation of 0.977 (root mean squared error, 1.57 mm Hg; P < .0001).

10 ificant accuracy (correlation=0.32, P=0.006; mean squared error=176.88, P=0.001).

11 squared error = 0.025; PTDM: bias = -1.411, mean squared error = 2.011).

12 9 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803.

13 4% with respect to estimated cross-validated mean squared error and had an R2 value of 0.201.

14 timator, both in theoretical computations of mean squared error and in data analysis.

15 sonication and without it, furthermore, root mean squared error and standard error values were obtain

16 Root mean squared errors and mean residual errors were used t

17 models having reasonably low normalized root-mean-squared errors and high correlations for both the f

18 tive fidelity (accuracy, precision, and root mean squared error) and locally with relative error in v

19 Mean error, mean squared error, and mean absolute error were calcula

20 -statistic-based sensitivity, group-specific mean squared error, and several gene-specific diagnostic

21 by standard error, bias, square root of the mean-squared error, and 95% confidence interval coverage

22 for the prediction error bias, variance, and mean-squared error are given under general measurement e

23 Within year, hold-out validation yielded mean-squared-error-based R(2) (MSE-R(2)) (i.e., fit arou

24 mulation with desired movements yielded root mean squared errors between approximately 18 and 26%.

25 ecision was estimated from the combined root mean squared error (CRMSE) and the coefficient of determ

26 edure, our approach reduces the bias and the mean squared error, especially for modest effect sizes.

27 ed inverse probability weighting in terms of mean-squared error even under misspecification of one of

28 d Senegal, we calculated bias, variance, and mean squared error for estimates of the prevalence ratio

29 The difference between the mean squared error for groups A and B was not significan

30 ues, the average absolute difference and the mean squared error for MD forecasts with linear extrapol

31 The mean squared error for the automated method was signific

32 The authors compared bias and mean squared error for various PS implementations under

33 oth milk and meat spoilage, and typical root mean squared errors for prediction in test spectra were

34 s in the range approximately 1.1-2.2 A (root mean squared errors for the predicted bond distances of

35 erence panels, we observed a 28% decrease in mean-squared error for imputation and a 73.7% decrease i

36 error for imputation and a 73.7% decrease in mean-squared error for joint-testing.

37 her found a performance benefit (i.e., lower mean squared error) for sequentially passing a propensit

38 erns is computed using four scoring metrics: mean squared error, Haar wavelet distance, mutual inform

39 dependent data showed improvement, with root mean squared error improvement of 6% compared with the l

40 t-of-sample data, significantly reducing the mean squared error in a cross-validation study.

41 duced bias due to unmeasured confounders and mean squared error in most scenarios assessed.

42 Overall, the updated model reduces the root mean squared error in the predicted concentration by 66%

43 ess function based on the combination of the mean-squared error in the calibration set data, the mean

44 uared error in the calibration set data, the mean-squared error in the monitoring set data, and the n

45 validated CC model (average normalized root mean squared error </=11.3%) was then used to evaluate t

46 this scheme achieves the asymptotic minimax mean-squared error M(rho;beta) = lim(M,N --> infinity)in

47 First, the root mean squared error measures the difference between the t

48 Weight variability was modeled with a root mean squared error method to reflect fluctuations in wei

49 fferent normal limits (maximum entropy [ME], mean-squared-error minimization [MSEM], and global minim

50 We also introduce the Bayesian minimum mean squared error (MMSE) conditional error estimator an

51 The average Mean Squared Error (MSE) and Signal Reconstruction Error

52 xperiments show that we can achieve a better mean squared error (MSE) for small rates (bits per quali

53 g the expected diameter and estimates of the mean squared error (MSE) were generated.

54 ssed through correlation coefficient (R(2)), mean squared error (MSE), confusion matrices, receiver o

55 s approaches in terms of bias, variance, and mean squared error (MSE).

56 at such an approach does not always minimize mean squared error (MSE).

57 is only negligible when the normalized root-mean-squared error (NRMSE) in the calibration falls belo

58 ller bias and smaller variance, often with a mean squared error of 0, in estimating the number of pri

59 is 0.04 units of pH, 0.09 of accuracy, and a mean squared error of 0.167.

60 es C, which achieved a model fit with a root mean squared error of 0.20 log units.

61 ation ratios from the literature with a root mean squared error of 0.32-0.53 log units, without any a

62 on the determined descriptors (e.g., a root mean squared error of 0.39 for log Kow).

63 PS model can affect the bias, variance, and mean squared error of an estimated exposure effect.

64 comparative fit index range: .929-.968; root mean squared error of approximation range: .032-.052).

65 reater than 0.97 were obtained with the root mean squared error of calibration (<2.01) and prediction

66 obtained with corresponding values for root mean squared error of calibration and prediction (<0.57

67 ne-cystatin C equation as quantified by root mean squared error of difference scores (differences bet

68 ts and the wild type strain, we decrease the mean squared error of predicted central metabolic fluxes

69 thods was evaluated with respect to bias and mean squared error of the estimated effects of a binary

70 available information, leading to a smaller mean squared error of the genotype risk ratio estimators

71 a smaller sample size; the cost in terms of mean squared error of treatment effects for our preferre

72 oups (Positive Mode-R2 = 97%; Q2 = 93%; root mean squared error of validation (RMSEV) = 13%; Negative

73 is aeruginosa strains was possible with Root Mean Squared Error of Validation (RMSEV) lower or very c

74 brated pp-LFER and COSMO-RS models with root mean squared errors of 0.047 and 0.050, respectively.

75 The root-mean-squared error of prediction (RMSEP) is 92.17 mg/dL

76 The root-mean-squared error of prediction (RMSEP) of 1.8 mM (33.1

77 r the expectation method in terms of smaller mean-squared errors of the estimated QTL effects.

78 ation coefficient of -0.60 +/- 0.04 and root-mean-squared-error of 14.6 +/- 1.5 mmHg (p < 0.05).

79 ent of -0.80 +/- 0.02 (mean +/- SE) and root-mean-squared-error of 7.6 +/- 0.5 mmHg after a best-case

80 ncertainty (interquartile range and the root-mean-squared error) of load estimates a modeling exercis

81 ll type-specific proportion variability, and mean squared error on sensitivity of cell type-specific

82 tion differed for each gene as a function of mean squared error, per group sample sizes, and variabil

83 lly relevant variables, while the prediction mean squared error (PMSE) is comparable or even reduced.

84 redicted partition coefficients exhibit root-mean-squared errors ranging from 0.19 to 0.48 log unit,

85 invertebrate phototransduction using minimum mean squared error reconstruction techniques based on Ba

86 This method showed a mean squared error reduction of over 21.89% oversimple k

87 gs of imputation methods based on three root-mean squared error (RMSE) measures and the rankings base

88 of determination (R(2)) of 0.789 and a root mean squared error (RMSE) of 0.526 were obtained for the

89 e ANSI/HPS N13.30-2011 standard for the root mean squared error (RMSE) of relative bias (Br) and rela

90 Root mean squared error (RMSE) was used to evaluate goodness

91 odel performance was assessed using the root mean squared error (RMSE), R2, and slope and intercept o

92 physical activity relation by using the root mean squared error (RMSE).

93 han a single measurement [difference in root mean squared error (RMSE): 1.3 ng/mL; P< 0.001].

94 of determination (R(2)) = 0.87], lowest root mean squared error (RMSE; 0.87 kg), and fewest outliers

95 he set of parameters that minimized the root-mean-squared error (RMSE) between the model and the expe

96 imated with an r(2) value of 0.98 and a root-mean-squared error (RMSE) of 8.19 mumol m(-2) s(-1) .

97 The root-mean-squared error (RMSE) was used to compare the MR-bas

98 and our new model, using the statistics root-mean-squared-error (RMSE) and median residual and on an

99 ed partition ratios of cVMS accurately (root-mean-squared-error (RMSE) for logKOC 0.76 and for logKDO

100 ients of determination R(2)>/= 0.82 and root mean squared errors (RMSEs) </= 0.47 log(10)CFUg(-1).

101 tcome can be detrimental to an estimate in a mean squared error sense.

102 lihood estimator yields smaller variance and mean squared error than other estimators; and the struct

103 to be optimistic but with lower absolute and mean squared errors than both methods of cross-validatio

104 ng yielded lower average standard errors and mean squared errors than did matching on age and sex.

105 For comparison, mean squared error values were calculated between the me

106 Mean-squared-error values were calculated between the me

107 s used (<10% in 35 of 45 scenarios), and the mean squared error was usually minimized with recall per

108 tance, and vector angle restraints, the root-mean squared error with respect to existing X-ray struct