Sentences with phrase «analysis parameter estimates»
Unstandardized path analysis parameter estimates, s.e. and their significance.
http://rspb.royalsocietypublishing.org/ Not exact matches
[11] Asteroseismic analyses that incorporate the tight observational constraints on the stellar parameters for α Cen A and / or B have yielded age estimates of 7000484999999999999 ♠ 4.85 ± 0.5 Gyr, [7] 7000500000000000000 ♠ 5.0 ± 0.5 Gyr, [27] 5.2 — 7.1 Gyr, [28] 6.4 Gyr, [29] and 7000652000000000000 ♠ 6.52 ± 0.3 Gyr.
Summary estimates were calculated using a general variance - based method (random - effects model) with 95 % CIs.19 Because the potential confounders considered in multivariate analyses vary across studies, we used the parameter estimates in the most complex model, which typically include demographic, lifestyle, and dietary factors.
Demographers do their utmost to estimate as accurately as possible the key parameters of a population, using statistical analysis, models and of course as much relevant data as are available, in particular censuses, surveys and registrations.
There is absolutely no error analysis, and all those spaghetti graphs are the modeler's estimate of what happens to their model once they fiddle the parameters to fit the temperature curves and they change the initial conditions of the time development!
They are simply a first estimate.Where multiple analyses of the biases in other climatological variables have been produced, for example tropospheric temperatures and ocean heat content, the resulting spread in the estimates of key parameters such as the long - term trend has typically been signicantly larger than initial estimates of the uncertainty suggested.
The paper incorporates data - driven estimates of the value of fuel economy into an automotive market simulation model that has three components: a consumer demand function that predicts consumers» vehicle choices as functions of vehicle price, fuel price, and vehicle attributes (the new estimates of the value of fuel economy are used to set the parameters of the demand function); an engineering and economic evaluation of feasible fuel economy improvements by 2010; and a game theoretic analysis of manufacturers» competitive interactions.
You may call it by a different name than statisticians, but it most definitely applies to biasing certain parameter estimates in an analysis!
(3) If you fit a line and then use the parameter estimates of that line as input into other analysis (as was done in our sample paper, referenced below), your results will be too certain.
Like all omitted variables, it biases estimated parameters for included variables if the selection criterion is correlated with variables included in the analysis.
General Introduction Two Main Goals Identifying Patterns in Time Series Data Systematic pattern and random noise Two general aspects of time series patterns Trend Analysis Analysis of Seasonality ARIMA (Box & Jenkins) and Autocorrelations General Introduction Two Common Processes ARIMA Methodology Identification Phase Parameter Estimation Evaluation of the Model Interrupted Time Series Exponential Smoothing General Introduction Simple Exponential Smoothing Choosing the Best Value for Parameter a (alpha) Indices of Lack of Fit (Error) Seasonal and Non-seasonal Models With or Without Trend Seasonal Decomposition (Census I) General Introduction Computations X-11 Census method II seasonal adjustment Seasonal Adjustment: Basic Ideas and Terms The Census II Method Results Tables Computed by the X-11 Method Specific Description of all Results Tables Computed by the X-11 Method Distributed Lags Analysis General Purpose General Model Almon Distributed Lag Single Spectrum (Fourier) Analysis Cross-spectrum Analysis General Introduction Basic Notation and Principles Results for Each Variable The Cross-periodogram, Cross-density, Quadrature - density, and Cross-amplitude Squared Coherency, Gain, and Phase Shift How the Example Data were Created Spectrum Analysis — Basic Notations and Principles Frequency and Period The General Structural Model A Simple Example Periodogram The Problem of Leakage Padding the Time Series Tapering Data Windows and Spectral Density Estimates Preparing the Data for Analysis Results when no Periodicity in the Series Exists Fast Fourier Transformations General Introduction Computation of FFT in Time Series
«The assessment is supported additionally by a complementary analysis in which the parameters of an Earth System Model of Intermediate Complexity (EMIC) were constrained using observations of near - surface temperature and ocean heat content, as well as prior information on the magnitudes of forcings, and which concluded that GHGs have caused 0.6 °C to 1.1 °C (5 to 95 % uncertainty) warming since the mid-20th century (Huber and Knutti, 2011); an analysis by Wigley and Santer (2013), who used an energy balance model and RF and climate sensitivity estimates from AR4, and they concluded that there was about a 93 % chance that GHGs caused a warming greater than observed over the 1950 — 2005 period; and earlier detection and attribution studies assessed in the AR4 (Hegerl et al., 2007b).»
Missing data for longitudinal analysis (HOME Inventory, maternal health, depression, social support, stressful life events, family functioning and experience of being a mother) were dealt with using a three - step procedure to provide a balance between maintaining study power and minimising bias in parameter estimates.27 28 First, participants who had not completed any data points for these outcomes were deleted from analysis.
In the current analyses, GEE was used to predict levels of the dependent variable at time t + 1, after accounting for levels at time t, to produce an aggregate parameter estimate over time and thus utilizing a Transitional Marginal Model (Fitzmaurice et al. 2004).
Full information maximum likelihood (FIML) was used to estimate the parameters in the model, allowing available data from participants with missing values on some of the variables to be included in the analyses.
In addition, since from a practical - clinical perspective effect sizes are the most relevant objective of the analyses, and due to the fact that p - values are strongly dependent on sample size, all effect sizes for the relationships analyzed have been estimated by the confidence interval for the parameters, with the R2 measuring the global predictive capacity of the models (adjusted to the covariates).
Standardized estimated marginal means are calculated based on the parameter estimates that were obtained through the multilevel analysis.
To avoid bias to parameter estimates when 32 % of the missing cases were excluded, we created 20 multiply imputed data sets in STATA using all variables included in the analyses to inform the imputations (Allison, 2002).
Three nested models with increased degrees of constraint were compared in multigroup analyses (fathers versus mothers): We specified a first model of configural invariance, in which the parameters (factor loadings, item intercepts, residual variances, factor variances, and covariance) were freely estimated in each group, whereas the factor means were constrained to zero in both groups.
Item response theory analyses provided item parameter estimates and information functions for 18 externalizing subscale items, revealing their quality of measurement along the continuum of disruptive behaviors in preschool - aged children.