Sentences with phrase «model as a predictor»

The article discusses the strengths and weaknesses of Shiller's CAPE model as a predictor of stock market returns.

Our method uses estimates of ice thickness from a coupled ice - ocean model as predictors for a statistical forecast of the minimum ice extent in September.

If that run is not considered an outlier it does not speak well for the confidence inspired for the GISS model as a predictor.

Ego control, ego resiliency, and the five - factor model as predictors of behavioral and emotional problems in clinic - referred children and adolescents

In the present study we therefore include parental confidence in our model as a predictor of parenting practices.

Not exact matches

If models must compete against one another, we suggest comparing the model sets with and without each candidate predictor variable, as we did when calculating the summed Akaike weights for each variable (2).

To account for demographic differences that might impact social network structure, our model also included binary predictor variables indicating whether subjects in each dyad were of the same or different nationalities, ethnicities, and genders, as well as a variable indicating the age difference between members of each dyad.

Then, we generated proxies with this fitted model, using as predictors the SST time series where the components linearly related to the MEI, PDO and AMO were removed (by linear regression, as above).

How many of the climate models which are used as predictors of future temperature (i.e. climate forecasting) have actually undergone a forecast audit (yes there is such a thing)?

Seven environmental variables, which were previously identified as potential predictors for podoconiosis in Ethiopia (Deribe et al., 2015b), were used to model podoconiosis prevalence.

The mathematical models in the study showed that highly resolved land cover maps could act as an accurate predictor for farm locations and provide accurate advice regarding the optimal strategy to control disease spread.

Presence of FMc was treated as a binary outcome in logistic regression models, with age at diagnosis and various disease characteristics as the predictors.

Most of the residual variance can be explained by adding dinucleotide features as sequence ‐ to ‐ shape predictors to the model.

The unadjusted models only included prior academic achievement as predictor variables, and are shown for comparison purposes only.

And if history is a predictor, the new and improved Apple tablets will hold the same price points as the current models.

As a result, FICO has redeveloped its credit scoring models several times to make sure they remain robust predictors of credit risk.

To compare the predictive strength of maternal style and young adult test performance, we built a single model that incorporated both classes of variables as predictors.

We built a generalized estimating equation (GEE) general linear model (GLM) with outcome as the dependent variable; time in the nursing box, licking / grooming per puppy, vertical nursing per puppy, and ventral nursing per puppy were entered as predictors with breed, maternal parity, sex of puppy, and age at return entered as covariates.

As these data sets expand (paleo - sea level / paleo - temperature) there's every chance we can home - in on some really self - consistent interpretations of temp / sea level / greenhouse gas relationships going back several millenia which we be extraordinarily useful as targets for modelling and as predictors of future scenarioAs these data sets expand (paleo - sea level / paleo - temperature) there's every chance we can home - in on some really self - consistent interpretations of temp / sea level / greenhouse gas relationships going back several millenia which we be extraordinarily useful as targets for modelling and as predictors of future scenarioas targets for modelling and as predictors of future scenarioas predictors of future scenarios.

The forecast model uses Siberian snow cover as one of its main predictors and I have been posting those forecasts to the National Science Foundation website: / / www.nsf.gov...

How many of the climate models which are used as predictors of future temperature (i.e. climate forecasting) have actually undergone a forecast audit (yes there is such a thing)?

Canadian Ice Service, 4.7, Multiple Methods As with CIS contributions in June 2009, 2010, and 2011, the 2012 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter arctic ice thicknesses and extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictorAs with CIS contributions in June 2009, 2010, and 2011, the 2012 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter arctic ice thicknesses and extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoras well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoras an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

re predicting latitude etc — by using the historical record as a predictor, you are in fact using a model to predict (the model is the interpretation of the historical record), and you are using the system itself to «learn» as I indicated.

Canadian Ice Service, 4.7 (+ / - 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

The hierarchical structure of this model gives an unbiased predictor of climate influences on presences, and allows poorly known species to draw inferential strength from the flora as a whole [33].

Canadian Ice Service; 5.0; Statistical As with Canadian Ice Service (CIS) contributions in June 2009 and June 2010, the 2011 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic Multi-Year Ice (MYI) extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere, and sea ice predictorAs with Canadian Ice Service (CIS) contributions in June 2009 and June 2010, the 2011 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic Multi-Year Ice (MYI) extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere, and sea ice predictoras well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere, and sea ice predictoras an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere, and sea ice predictors.

The predicted September sea ice area in the East Siberian and Laptev Seas, from a simple regression model using summer (Aug - Sep - Oct) sea surface temperatures in the North Atlantic as the predictor, is below normal but greater than in 2009.

Using a statistical model based on canonical correlation analysis with fall sea surface temperature anomalies in the North Atlantic as the main predictor, Tivy shows below - normal ice concentrations throughout most of the region (Figure 12), which suggests an earlier - than - normal opening of the shipping season.

They used a structural econometric (regression) model based on time series of flood damage, and population, wealth indicator, and annual precipitation as predictors.

Canadian Ice Service, 4.7 (± 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

The region of ice concentration > 60 % on August 5 from MyOcean (TOPAZ4 model) was used as a predictor variable, and a linear regression was performed of September NSIDC extent vs. > 60 % concentration area on August 5.

As there are more predictors than data realizations (with each CMIP5 model providing one realization), using them directly to predict ΔT would involve massive over-fitting.

the model may be «correct,» but you have gone overboard by adding predictors that are redundant leading to problems such as inflated standard errors for the regression coefficients» (i.e., overconfidence in the prediction algorithm).

As I have shown, in CMIP5 models that relationship is considerably stronger for the OLR seasonal cycle than for any of the other predictors or any combination of predictors.

As with previous CIS contributions, the 2016 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness / extent, as well as winter surface air temperature, spring ice conditions and the summer temperature forecast; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictorAs with previous CIS contributions, the 2016 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness / extent, as well as winter surface air temperature, spring ice conditions and the summer temperature forecast; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoras well as winter surface air temperature, spring ice conditions and the summer temperature forecast; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoras winter surface air temperature, spring ice conditions and the summer temperature forecast; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

The negative exponential growth model was developed quite a while ago before statistical methodology which used enormous calculating power (such as lowess applied to 48000 + observations over 415 predictor points) became commonly available.

A negative RE means your predictor model performs WORSE than just taking the average of X as your predictor.

[3] The four studies involved are: Brient, F., T. Schneider, Z. Tan, S. Bony, X. Qu, and A. Hall, 2015: Shallowness of tropical low clouds as a predictor of climate models» response to warming.

In bivariate and multivariate logistic regression models, 8 social risk factors were tested as independent predictors of 4 parent - reported child health outcomes: global health status, dental health, socioemotional health, and overweight.

Advances in prevention in public health2 provide a model for prevention of adolescent health - risk behaviors by focusing on risk and protective factors predictive of these behaviors.3, 4 Research on the predictors of school failure, delinquency, drug abuse, teen pregnancy, and violence indicates that many of the same factors predict these different outcomes.5, 6 Recent research has shown that bonding to school and family protects against a broad range of health - risk behaviors in adoles cence.6 Yet, prevention studies typically have focused narrowly on a specific outcome, such as preventing substance abuse, and on attitudes and social influences that predict that outcome.7, 8 Previous studies on prevention have not sought to address the shared risk and protective factors for diverse health - risk behaviors that are the main threats to adolescent health.

Discrete - time survival analysis, with person - year the unit of analysis and a logistic link function, was used to examine associations of temporally primary (based on retrospective age - at - onset reports) mental disorders and subsequent first onset of suicidality.29 Time was modeled as a separate dummy predictor variable for each year of life up to age at interview or age at onset of the outcome, whichever came first.

Multiple logistic regression model of client characteristics as predictors of MNCH — FP integration

We individually modeled the 9 - month ITSC variable and combined 9 - month + 2 - year ITSC 4 - category variable as categorical predictors.

A covariate was included in the multivariate analyses if theoretical or empirical evidence supported its role as a risk factor for obesity, if it was a significant predictor of obesity in univariate regression models, or if including it in the full multivariate model led to a 5 % or greater change in the OR.48 Model 1 includes maternal IPV exposure, race / ethnicity (black, white, Hispanic, other / unknown), child sex (male, female), maternal age (20 - 25, 26 - 28, 29 - 33, 34 - 50 years), maternal education (less than high school, high school graduation, beyond high school), maternal nativity (US born, yes or no), child age in months, relationship with father (yes or no), maternal smoking during pregnancy (yes or no), maternal depression (as measured by a CIDI - SF cutoff score ≥ 0.5), maternal BMI (normal / underweight, overweight, obese), low birth weight (< 2500 g, ≥ 2500 g), whether the child takes a bottle to bed at age 3 years (yes or no), and average hours of child television viewing per day at age 3 years (< 2 h / d, ≥ 2 h model led to a 5 % or greater change in the OR.48 Model 1 includes maternal IPV exposure, race / ethnicity (black, white, Hispanic, other / unknown), child sex (male, female), maternal age (20 - 25, 26 - 28, 29 - 33, 34 - 50 years), maternal education (less than high school, high school graduation, beyond high school), maternal nativity (US born, yes or no), child age in months, relationship with father (yes or no), maternal smoking during pregnancy (yes or no), maternal depression (as measured by a CIDI - SF cutoff score ≥ 0.5), maternal BMI (normal / underweight, overweight, obese), low birth weight (< 2500 g, ≥ 2500 g), whether the child takes a bottle to bed at age 3 years (yes or no), and average hours of child television viewing per day at age 3 years (< 2 h / d, ≥ 2 h Model 1 includes maternal IPV exposure, race / ethnicity (black, white, Hispanic, other / unknown), child sex (male, female), maternal age (20 - 25, 26 - 28, 29 - 33, 34 - 50 years), maternal education (less than high school, high school graduation, beyond high school), maternal nativity (US born, yes or no), child age in months, relationship with father (yes or no), maternal smoking during pregnancy (yes or no), maternal depression (as measured by a CIDI - SF cutoff score ≥ 0.5), maternal BMI (normal / underweight, overweight, obese), low birth weight (< 2500 g, ≥ 2500 g), whether the child takes a bottle to bed at age 3 years (yes or no), and average hours of child television viewing per day at age 3 years (< 2 h / d, ≥ 2 h / d).

The ORs become smaller in an additive multivariate survival model that includes all 15 mental disorders as predictors.

Although virtually all of the mental disorders examined were predictors of a suicide attempt in bivariate models, these associations were largely explained by mental disorders as predictors of suicide ideation.

We explored this issue further by estimating additional multilevel models examining the difference between G1 and G2 reports (G1 − G2) as predictors of target (G2) reports and offspring (G3) reports.

Site effects were included as fixed effects in the original models for the primary outcomes, but because site was not a significant predictor, it was not retained in the final models.

The models examined the grandmother — target relationship and the grandfather — target relationship as separate predictors, and the models were estimated in two steps as follows: (a) target reports of grandmother or grandfather (G1) as predictors, (b) grandmother or grandfather reports (G1) regarding target (G2) as predictors.

In the final multivariate model which included two or more adversities as a predictor variable, sexual abuse (OR 9.3, p < 0.001), childhood physical abuse (OR 2.2, p = 0.003) and parental divorce (OR 3.1, p < 0.001) retained significant associations with lifetime suicide attempts in the total sample.