Predictive Mean Matching

	Predictive Mean Matching Predictive mean matching (PMM) is a widely used statistical imputation method for missing values, first proposed by Donald B. Rubin in 1986 and R. J. A. Little in 1988. It aims to reduce the bias introduced in a dataset through imputation, by drawing real values sampled from the data. This is achieved by building a small subset of observations where the outcome variable matches the outcome of the observations with missing values. Compared to other imputation methods, it usually imputes less implausible values (e.g. negative incomes) and takes heteroscedastic data into account more appropriately. References {{DEFAULTSORT:Predictive mean matching Missing data Predictive analytics ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Imputation (statistics) In statistics, imputation is the process of replacing missing data with substituted values. When substituting for a data point, it is known as "unit imputation"; when substituting for a component of a data point, it is known as "item imputation". There are three main problems that missing data causes: missing data can introduce a substantial amount of bias, make the handling and analysis of the data more arduous, and create reductions in efficiency. Because missing data can create problems for analyzing data, imputation is seen as a way to avoid pitfalls involved with listwise deletion of cases that have missing values. That is to say, when one or more values are missing for a case, most statistical packages default to discarding any case that has a missing value, which may introduce bias or affect the representativeness of the results. Imputation preserves all cases by replacing missing data with an estimated value based on other available information. Once all missing values ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
picture info	Bias Bias is a disproportionate weight ''in favor of'' or ''against'' an idea or thing, usually in a way that is closed-minded, prejudicial, or unfair. Biases can be innate or learned. People may develop biases for or against an individual, a group, or a belief. In science and engineering, a bias is a systematic error. Statistical bias results from an unfair sampling of a population, or from an estimation process that does not give accurate results on average. Etymology The word appears to derive from Old Provençal into Old French ''biais'', "sideways, askance, against the grain". Whence comes French ''biais'', "a slant, a slope, an oblique". It seems to have entered English via the game of bowls, where it referred to balls made with a greater weight on one side. Which expanded to the figurative use, "a one-sided tendency of the mind", and, at first especially in law, "undue propensity or prejudice". Types of bias Cognitive biases A cognitive bias is a repeating or basic m ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
picture info	Heteroscedasticity In statistics, a sequence (or a vector) of random variables is homoscedastic () if all its random variables have the same finite variance. This is also known as homogeneity of variance. The complementary notion is called heteroscedasticity. The spellings ''homoskedasticity'' and ''heteroskedasticity'' are also frequently used. Assuming a variable is homoscedastic when in reality it is heteroscedastic () results in unbiased but inefficient point estimates and in biased estimates of standard errors, and may result in overestimating the goodness of fit as measured by the Pearson coefficient. The existence of heteroscedasticity is a major concern in regression analysis and the analysis of variance, as it invalidates statistical tests of significance that assume that the modelling errors all have the same variance. While the ordinary least squares estimator is still unbiased in the presence of heteroscedasticity, it is inefficient and generalized least squares should be used i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
picture info	Missing Data In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data. Missing data can occur because of nonresponse: no information is provided for one or more items or for a whole unit ("subject"). Some items are more likely to generate a nonresponse than others: for example items about private subjects such as income. Attrition is a type of missingness that can occur in longitudinal studies—for instance studying development where a measurement is repeated after a certain period of time. Missingness occurs when participants drop out before the test ends and one or more measurements are missing. Data often are missing in research in economics, sociology, and political science because governments or private entities choose not to, or fail to, report critical statistics, or because the information is not ava ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]