Inference for Parameters Defined by Moment Inequalities: A Recommended Moment Selection Procedure

This paper is concerned with tests and confidence intervals for partially-identified parameters that are defined by moment inequalities and equalities. In the literature, different test statistics, critical value methods, and implementation methods (i.e., asymptotic distribution versus the bootstrap) have been proposed. In this paper, we compare a wide variety of these methods. We provide a recommended test statistic, moment selection critical value method, and implementation method. In addition, we provide a data-dependent procedure for choosing the key moment selection tuning parameter and a data-dependent size-correction factor.Asymptotic size, Asymptotic power, Confidence set, Exact size, Generalized moment selection, Moment inequalities, Partial identification, Refined moment selection, Test

Validating linear restrictions in linear regression models with general error structure

A new method for testing linear restrictions in linear regression models is suggested. It allows to validate the linear restriction, up to a specified approximation error and with a specified error probability. The test relies on asymptotic normality of the test statistic, and therefore normality of the errors in the regression model is not required. In a simulation study the performance of the suggested method for model selection purposes, as compared to standard model selection criteria and the t-test, is examined. As an illustration we analyze the US college spending data from 1994

Pitting damage levels estimation for planetary gear sets based on model simulation and grey relational analysis

The planetary gearbox is a critical mechanism in helicopter transmission systems. Tooth failures in planetary gear sets will cause great risk to helicopter operations. A gear pitting damage level estimation methodology has been devised in this paper by integrating a physical model for simulation signal generation, a three-step statistic algorithm for feature selection and damage level estimation for grey relational analysis. The proposed method was calibrated firstly with fault seeded test data and then validated with the data of other tests from a planetary gear set. The estimation results of test data coincide with the actual test records, showing the effectiveness and accuracy of the method in providing a novel way to model based methods and feature selection and weighting methods for more accurate health monitoring and condition prediction

Order selection tests with multiply-imputed data.

We develop nonparametric tests for the null hypothesis that a function has a prescribed form, to apply to data sets with missing observations. Omnibus nonparametric tests do not need to specify a particular alternative parametric form, and have power against a large range of alternatives, the order selection tests that we study are one example. We extend such order selection tests to be applicable in the context of missing data. In particular, we consider likelihood-based order selection tests for multiply- imputed data. A simulation study and data analysis illustrate the performance of the tests. A model selection method in the style of Akaike's information criterion for multiply imputed datasets results along the same lines.Akaike information criterion; Hypothesis test; Multiple imputation; lack-of-fit test; Missing data; Omnibus test; Order selection;

Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration

Testing in Continuous Integration (CI) involves test case prioritization, selection, and execution at each cycle. Selecting the most promising test cases to detect bugs is hard if there are uncertainties on the impact of committed code changes or, if traceability links between code and tests are not available. This paper introduces Retecs, a new method for automatically learning test case selection and prioritization in CI with the goal to minimize the round-trip time between code commits and developer feedback on failed test cases. The Retecs method uses reinforcement learning to select and prioritize test cases according to their duration, previous last execution and failure history. In a constantly changing environment, where new test cases are created and obsolete test cases are deleted, the Retecs method learns to prioritize error-prone test cases higher under guidance of a reward function and by observing previous CI cycles. By applying Retecs on data extracted from three industrial case studies, we show for the first time that reinforcement learning enables fruitful automatic adaptive test case selection and prioritization in CI and regression testing.Comment: Spieker, H., Gotlieb, A., Marijan, D., & Mossige, M. (2017). Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration. In Proceedings of 26th International Symposium on Software Testing and Analysis (ISSTA'17) (pp. 12--22). AC

Covariance and PCA for Categorical Variables

Covariances from categorical variables are defined using a regular simplex expression for categories. The method follows the variance definition by Gini, and it gives the covariance as a solution of simultaneous equations. The calculated results give reasonable values for test data. A method of principal component analysis (RS-PCA) is also proposed using regular simplex expressions, which allows easy interpretation of the principal components. The proposed methods apply to variable selection problem of categorical data USCensus1990 data. The proposed methods give appropriate criterion for the variable selection problem of categoricalComment: 12 pages, 5 figure

Inference for Parameters Defined by Moment Inequalities: A Recommended Moment Selection Procedure

This paper is concerned with tests and confidence intervals for parameters that are not necessarily identified and are defined by moment inequalities. In the literature, different test statistics, critical value methods, and implementation methods (i.e., the asymptotic distribution versus the bootstrap) have been proposed. In this paper, we compare these methods. We provide a recommended test statistic, moment selection critical value method, and implementation method. We provide data-dependent procedures for choosing the key moment selection tuning parameter kappa and a size-correction factor eta.Asymptotic size, Asymptotic power, Bootstrap, Confidence set, Generalized moment selection, Moment inequalities, Partial identification, Refined moment selection, Test, Unidentified parameter

A Sequential Procedure for Testing Unit Roots in the Presence of Structural Break in Time Series Data: An Application to Quarterly Data of Nepal, 1970-2003

Testing for unit roots has special significance in terms of both economic theory and the interpretation of estimation results. as there are several methods available, researchers face method selection problem while conducting the unit root test on time series data in the presence of structural break. this paper proposes a sequential search procedure to determine the best test method for each time series. different test methods or models may be appropriate for different time series. therefore, instead of sticking to one particular test method for all the time series under consideration, selection of a set of mixed methods is recommended for obtaining better results.time series, stationarity, unit root test, structural break, sequential procedure

Sequential Procedure for Testing Unit Roots in the Presence of Structural Break in Time Series Data

Testing for unit roots has special significance in terms of both economic theory and the interpretation of estimation results. As there are several methods available, researchers face method selection problem while conducting the unit root test on time series data in the presence of structural break. This paper proposes a sequential search procedure to determine the best test method for each time series. Different test methods or models may be appropriate for different time series. Therefore, instead of sticking to one particular test method for all the time series under consideration, selection of a set of mixed methods is recommended for obtaining better results.Time Series, Stationarity, Unit Root Test, Structural Break, Sequential Procedure