Cost-effectiveness versus Cost-Utility Analyses
... significant to the patient or if there are multiple patient-relevant clinical outcome parameters expressed in different units. If a cost-utility ratio is presented as a reference case analysis result, the corresponding cost per lifeyear gained should also be presented.” “A CUA should be used in the R ...
... significant to the patient or if there are multiple patient-relevant clinical outcome parameters expressed in different units. If a cost-utility ratio is presented as a reference case analysis result, the corresponding cost per lifeyear gained should also be presented.” “A CUA should be used in the R ...
1.14 Polynomial regression
... A quite flexible class of models for the mean of a real valued random variable X given a real valued covariate y is EX = β0 + β1 y + β2 y 2 + . . . + βd y d , thus the mean is a d’th order polynomial in the covariate y. Let y1 , . . . , yn be given, real numbers – the covariates – and Xi = β 0 + β 1 ...
... A quite flexible class of models for the mean of a real valued random variable X given a real valued covariate y is EX = β0 + β1 y + β2 y 2 + . . . + βd y d , thus the mean is a d’th order polynomial in the covariate y. Let y1 , . . . , yn be given, real numbers – the covariates – and Xi = β 0 + β 1 ...
Propensity score adjusted method for missing data
... Propensity score adjustment is a popular technique for handling unit nonresponse in sample surveys. When the response probability does not depend on the study variable that is subject to missingness, conditional on the auxiliary variables that are observed throughout the sample, the response mechani ...
... Propensity score adjustment is a popular technique for handling unit nonresponse in sample surveys. When the response probability does not depend on the study variable that is subject to missingness, conditional on the auxiliary variables that are observed throughout the sample, the response mechani ...
Observed-Score Linking and Equating
... package, equating designs are categorized as either involving a single group, equivalent groups, or nonequivalent groups of examinees, and test forms are then constructed based on the type of group(s) sampled. In the single-group design, one group, sampled from the target population T , takes two di ...
... package, equating designs are categorized as either involving a single group, equivalent groups, or nonequivalent groups of examinees, and test forms are then constructed based on the type of group(s) sampled. In the single-group design, one group, sampled from the target population T , takes two di ...
An Overview of Causal Directed Acyclic Graphs
... data for two reasons. First, DAGs provide a way of precisely specifying a researcher’s causal assumptions, providing a language to clearly state a researcher’s assumptions about what is causing what. By providing this clarity, it allows other investigators to critically evaluate those assumptions—a ...
... data for two reasons. First, DAGs provide a way of precisely specifying a researcher’s causal assumptions, providing a language to clearly state a researcher’s assumptions about what is causing what. By providing this clarity, it allows other investigators to critically evaluate those assumptions—a ...
Machine Learning MLlib and Tensorflow
... It groups data instances that are similar to each other in one cluster, and data instances that are very different from each other into different clusters. ...
... It groups data instances that are similar to each other in one cluster, and data instances that are very different from each other into different clusters. ...
GeoDa and Spatial Regression Modeling
... Stephen A. Matthews Associate Professor of Sociology & Anthropology, Geography and Demography Director of the Geographic Information Analysis Core ...
... Stephen A. Matthews Associate Professor of Sociology & Anthropology, Geography and Demography Director of the Geographic Information Analysis Core ...
The effect of inbreeding on natural selection in a seedfeeding beetle
... interaction effect is conditional on the independent variables, unlike the interaction effect in general linear models) (Ganzach et al., 2000; Ai & Norton, 2003; Norton et al., 2004; Mood, 2010). Thus, statistical inferences regarding selection cannot be drawn from the magnitude or significance of t ...
... interaction effect is conditional on the independent variables, unlike the interaction effect in general linear models) (Ganzach et al., 2000; Ai & Norton, 2003; Norton et al., 2004; Mood, 2010). Thus, statistical inferences regarding selection cannot be drawn from the magnitude or significance of t ...
Structural Econometric Modeling: Rationales and Examples from
... restrictions can be incorporated into non-parametric methods. Although these nonparametric estimation techniques allow the researcher to estimate the joint density of x and y flexibly, they have their drawbacks. Most important, smoothing techniques often require enormous amounts of data to yield muc ...
... restrictions can be incorporated into non-parametric methods. Although these nonparametric estimation techniques allow the researcher to estimate the joint density of x and y flexibly, they have their drawbacks. Most important, smoothing techniques often require enormous amounts of data to yield muc ...
Does Distance Impact Willingness to Pay for Forested Watershed
... Many researchers estimate the non-market values of wildfires, wildfire risk, and reduction. For example, Mueller et al. (2009) find that proximity to wildfires has a statistically significant decrease in sale price of homes using a hedonic property model. Donovan et al. (2007) also apply a hedonic p ...
... Many researchers estimate the non-market values of wildfires, wildfire risk, and reduction. For example, Mueller et al. (2009) find that proximity to wildfires has a statistically significant decrease in sale price of homes using a hedonic property model. Donovan et al. (2007) also apply a hedonic p ...
Bankruptcy Prediction
... For many years MDA models are used as standard tools for the bankruptcy prediction problem, despite the fact that many techniques outperform MDA in prediction corporate bankruptcy. This is mainly due to two facts. Firstly, because MDA models are easy to apply for this bankruptcy prediction problem. ...
... For many years MDA models are used as standard tools for the bankruptcy prediction problem, despite the fact that many techniques outperform MDA in prediction corporate bankruptcy. This is mainly due to two facts. Firstly, because MDA models are easy to apply for this bankruptcy prediction problem. ...
Review of functional data analysis
... standard metrics, such as the L2 norm. Sparse functional data arise in longitudinal studies where subjects are measured at different time points and the number of measurements ni for subject i may be bounded away from infinity, i.e., sup1≤i≤n ni < C < ∞ for some constant C. A rigorous definition of ...
... standard metrics, such as the L2 norm. Sparse functional data arise in longitudinal studies where subjects are measured at different time points and the number of measurements ni for subject i may be bounded away from infinity, i.e., sup1≤i≤n ni < C < ∞ for some constant C. A rigorous definition of ...
Using survey data to assess bias in the Consumer Price Index
... essentially imposes the restriction that the coefficients of these two explanatory variables are equal, but opposite in sign. The analysis presented here rejects this restriction at the 0.001 level, although the percent change in nominal income does have a positive coefficient (1.90) and the percent ...
... essentially imposes the restriction that the coefficients of these two explanatory variables are equal, but opposite in sign. The analysis presented here rejects this restriction at the 0.001 level, although the percent change in nominal income does have a positive coefficient (1.90) and the percent ...
Pivotal Estimation via Square-root Lasso in
... The final contribution is to provide an application of Lasso methods to a generic semi-parametric problem, where some low-dimensional parameters are of interest and these methods are used to estimate nonparametric nui√ sance parameters. These results extend the n consistency and asymptotic normality ...
... The final contribution is to provide an application of Lasso methods to a generic semi-parametric problem, where some low-dimensional parameters are of interest and these methods are used to estimate nonparametric nui√ sance parameters. These results extend the n consistency and asymptotic normality ...
Linear regression
In statistics, linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variables) denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. (This term should be distinguished from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.)In linear regression, data are modeled using linear predictor functions, and unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, linear regression refers to a model in which the conditional mean of y given the value of X is an affine function of X. Less commonly, linear regression could refer to a model in which the median, or some other quantile of the conditional distribution of y given X is expressed as a linear function of X. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of y given X, rather than on the joint probability distribution of y and X, which is the domain of multivariate analysis.Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications. This is because models which depend linearly on their unknown parameters are easier to fit than models which are non-linearly related to their parameters and because the statistical properties of the resulting estimators are easier to determine.Linear regression has many practical uses. Most applications fall into one of the following two broad categories: If the goal is prediction, or forecasting, or error reduction, linear regression can be used to fit a predictive model to an observed data set of y and X values. After developing such a model, if an additional value of X is then given without its accompanying value of y, the fitted model can be used to make a prediction of the value of y. Given a variable y and a number of variables X1, ..., Xp that may be related to y, linear regression analysis can be applied to quantify the strength of the relationship between y and the Xj, to assess which Xj may have no relationship with y at all, and to identify which subsets of the Xj contain redundant information about y.Linear regression models are often fitted using the least squares approach, but they may also be fitted in other ways, such as by minimizing the ""lack of fit"" in some other norm (as with least absolute deviations regression), or by minimizing a penalized version of the least squares loss function as in ridge regression (L2-norm penalty) and lasso (L1-norm penalty). Conversely, the least squares approach can be used to fit models that are not linear models. Thus, although the terms ""least squares"" and ""linear model"" are closely linked, they are not synonymous.