IRUSRT: A Novel Imbalanced Learning Technique by Combining Inverse Random Under Sampling and Random Tree

In this article, a novel technique IRUSRT (inverse random under sampling and random tree) by combining inverse random under sampling and random tree is proposed to implement imbalanced learning. The main idea is to severely under sample the majority class thus creating multiple distinct training sets. With each training set, a random tree is trained to separate the minority class from the majority class. By combining these random trees through fusion, a composite classifier is constructed. The experimental analysis on 23 real-world datasets assessed over area under the ROC curve (AUC), F-measure, and G-mean indicates that IRUSRT performs significantly better when compared with many existing class imbalance learning methods.     

Bayes Estimates for the Price and Income Elasticities of Alcoholic Beverages in Finland From 1955 to 1980

The Lindley–Smith theory of Bayes estimates for multiple regression equations with exchangeability between the regression coefficients of the individual equations is extended to the case in which a first-order autoregressive process generates the regression coefficients. The ensuing formulas are applied to study monthly Finnish consumption of alcohol. The point of this application is that exchangeability between the regression coefficients is less than we can assess beforehand when there is a natural ordering, in this case according to chronological time, of the equations. Still, the general task of the Lindley–Smith estimators, to consider the combined data when estimating individual regression coefficients, is a relevant one.     

C239. The continued fraction for lnr( z): a stieljes conjecture

Estimation in logistic-normal models for correlated and overdispersed binomial data is complicated by the numerical evaluation of often intractable likelihood functions. Penalized quasilikelihood (PQL) estimators of fixed effects and variance components are known to be seriously biased for binary data. A simple correction procedure has been proposed to improve the performance of the PQL estimators. The proposed method is illustrated by analyzing infectious disease data. Its performance is compared, by means of simulations, with that of the Bayes approach using the Gibbs sampler.     

An Appraisal of Methods for the Analysis of Longitudinal Ordinal Response Data with Random Dropout Using a Nonhomogeneous Markov Model

There are many methods for analyzing longitudinal ordinal response data with random dropout. These include maximum likelihood (ML), weighted estimating equations (WEEs), and multiple imputations (MI). In this article, using a Markov model where the effect of previous response on the current response is investigated as an ordinal variable, the likelihood is partitioned to simplify the use of existing software. Simulated data, generated to present a three-period longitudinal study with random dropout, are used to compare performance of ML, WEE, and MI methods in terms of standardized bias and coverage probabilities. These estimation methods are applied to a real medical data set.     

A Simulation Comparison of Approximate Tests for Fixed Effects in Random Coefficients Growth Curve Models

Often, the response variables on sampling units are observed repeatedly over time. The sampling units may come from different populations, such as treatment groups. This setting is routinely modeled by a random coefficients growth curve model, and the techniques of general linear mixed models are applied to address the primary research aim. An alternative approach is to reduce each subject’s data to summary measures, such as within-subject averages or regression coefficients. One may then test for equality of means of the summary measures (or functions of them) among treatment groups. Here, we compare by simulation the performance characteristics of three approximate tests based on summary measures and one based on the full data, focusing mainly on accuracy of p-values. We find that performances of these procedures can be quite different for small samples in several different configurations of parameter values. The summary-measures approach performed at least as well as the full-data mixed models approach.     

Characterization of the quantitative-qualitative measure of inaccuracy for discrete generalized probability distributions

A characterization of the quantitative-qualitative measure of inaccuracy depending upon the additivity postulate and the mean value property for discrete generalized probability distribution has been provided.     

Optimal design for linear regression models in the presence of heteroscedasticity caused by random coefficients

Random coefficients may result in heteroscedasticity of observations. For particular situations, where only one observation is available per individual, we derive optimal designs based on the geometry of the design locus.     

论公平的正义对功利的正义的道德合理性批判与颠覆

罗尔斯与穆勒对正义情感、原则、基础等问题的共同关切,为公平的正义颠覆功利的正义提供了可能与前提。公平的正义用基础的确定性、原则的公平性、关系的合理性超越了功利的正义的基础的模糊性、原则的不公平性、关系的颠倒性,实现了道德契约论的理论建构和价值张扬。     

Optimal change-over designs for correlated observations

Design implications of an autoregressive model for change-over experiments are investigated. In this model, the residual effect due to the previous treatment is assumed to be proportional to the response in the previous period. In addition, the errors from the same experimental subject are assumed to be correlated according to a first-order autoregressive model. Models with fixed and random subject effects are discussed separately. An attempt has been made to identify and construct optimal or nearly optimal designs in various situations. Empirical conclusions of Taka and Armitage [Commun. Statist. Theor. Meth. (1983)12, 865-876] regarding the efficiency of some designs have also been confirmed.     

Risk Premiums and Benefit Measures for Generalized-Expected-Utility Theories

Standard tools for the analysis of economic problems involving uncertainty, including risk premiums, certainty equivalents and the notions of absolute and relative risk aversion, are developed without making specific assumptions on functional form beyond the basic requirements of monotonicity, transitivity, continuity, and the presumption that individuals prefer certainty to risk. Individuals are not required to display probabilistic sophistication. The approach relies on the distance and benefit functions to characterize preferences relative to a given state-contingent vector of outcomes. The distance and benefit functions are used to derive absolute and relative risk premiums and to characterize preferences exhibiting constant absolute risk aversion (CARA) and constant relative risk aversion (CRRA). A generalization of the notion of Schur-concavity is presented. If preferences are generalized Schur concave, the absolute and relative risk premiums are generalized Schur convex, and the certainty equivalents are generalized Schur concave.