Efficient Treatment Effect Estimation with Out-of-bag Post-stratification

Post-stratification is often used to estimate treatment effects with higher efficiency. However, most of the existing post-stratification frameworks depend on prior knowledge of the distributions of covariates and assume that the units are classified into post-strata without error. We propose a novel method to determine a proper stratification rule by mapping the covariates into a post-stratification factor (PSF) using predictive regression models. Inspired by the bootstrap aggregating (bagging) method, we utilize the out-of-bag delete-D jackknife to estimate strata boundaries, strata weights, and the variance of the point estimate. Confidence intervals are constructed with these estimators to take into account the additional variability coming from uncertainty in the strata boundaries and weights. Extensive simulations show that our proposed method consistently improves the efficiency of the estimates when the regression models are predictive and tends to be more robust than the regression imputation method

Factors of the Adoption of O2O Service Platforms: Evidence from Small Businesses in Korea

O2O service platforms, which combine online and offline channels to provide more convenient services, are drawing attention as a new way of commerce that can revitalize small businesses that are losing competitiveness and struggling due to the coronavirus disease 2019 pandemic. In this study, we investigated and empirically analyzed the factors affecting the adoption of O2O service platforms in small businesses. We developed a research model that combines the technology acceptance model (TAM), an individual-level theory of IT acceptance, and the technology-organization-environment (TOE) framework, an organizational-level theory of information systems adoption. Data from 279 valid questionnaires were collected from small business owners and analyzed using structural equation modeling. The results show that the technical characteristics of the TOE framework, namely, relative advantage, compatibility, and trialability, and small business owners’ characteristics, namely, innovativeness, risk-taking tendency, and IT knowledge, affect the adoption of O2O service platforms through perceived usefulness and perceived ease of use. The environmental variables of the TOE framework, namely, government support, digital environment change, and competitive pressure, affect the adoption of O2O service platforms through subjective norms. We identify practical implications for the adoption of O2O service platforms by small businesses

Components Analysis on Audio Signal Mixtures

This paper presents a novel multi-label noise classification algorithm that uses a convolutional neural network and applies a sliding window for classification. The existing noise classification method uses a convolutional neural network, in which the input audio must have a fixed time length. On the other hand, time-variant networks such as a time-delay neural network or a recurrent neural network can use any length of time, but have a limitation of classifying only a single label within a short time. Considering such shortcomings, we propose a windowing method that applies multi-label classification in overlapping time windows. For an audio stream with a duration that is longer than the audio stream inputs that the model trained with, the model applies a sliding window with multi-label classification to detect the corresponding classes in each time sequence. The model then identifies the final classes of the input by considering the confidence scores of each output label in each time sequence. The classification accuracy was 94.17% for single-label audio, 85.21% for two-class audio, and averaged 86.39% for audio of various durations.1