Postfunctionalization is a useful strategy to tune the properties of conjugated polymers, while polymer reactions in main chain backbone are still underexplored. Here we report postfunctionalization via nucleophilic aromatic substitution reaction. Poly(9,9-dioctylfluorene-alt-tetrafluoro-p-phenylene) used as precursor react with thiophenol derivatives presence base enable multiple introduction arylthio groups into high yield preserving and dispersity polymer. The structure optoelectronic...

This study presents a prediction model of speaker's willingness level in human-robot interview interaction by using multimodal features (i.e., verbal, audio, and visual). We collected novel corpus, including two types annotation data sets willingness. A binary classification task the (high or low) was implemented to evaluate proposed model. obtained best accuracy 0.6) random forest with audio motion features. The difference between coder's recognition 0.73) 0.13.

In this study, we explore the partial identification of nonseparable models with continuous endogenous and binary instrumental variables. We show that structural function is partially identified when it monotone or concave in explanatory variable. D’Haultfœuille Février (2015, Econometrica 83(3), 1199–1210) Torgovitsky 1185–1197) prove point under a key assumption conditional distribution functions variable for different values variables have intersections. demonstrate that, even if does not...

The goal of many scientific experiments including A/B testing is to estimate the average treatment effect (ATE), which defined as difference between expected outcomes two or more treatments. In this paper, we consider a situation where an experimenter can assign research subjects sequentially. adaptive experimental design, allowed change probability assigning using past observations for estimating ATE efficiently. However, with approach, it difficult apply standard statistical method...

Social signal recognition techniques based on nonverbal behavioral sensing allow conversational robots to understand the user's social signals, thereby enabling them adopt interaction strategies internal states inferred from signals. This research investigates how online and adaptive dialog strategy influences dynamic change in a inner state. For this purpose, we develop semiautonomous interview robot system with an speaker's willingness module question selection level. The model of speaker...

Consider a planner who has to decide whether or not introduce new policy certain local population. The only limited knowledge of the policy's causal impact on this population due lack data but does have access publicized results intervention studies performed for similar policies different populations. How should make use and aggregate existing evidence her decision? Building upon paradigm `patient-centered meta-analysis' proposed by Manski (2020; Towards Credible Patient-Centered...

In this study, we explore the partial identification of nonseparable models with continuous endogenous and binary instrumental variables. We show that structural function is partially identified when it monotone or concave in an explanatory variable. D'Haultfoeuille Fevrier (2015) Torgovitsky prove point under two key assumptions: (a) conditional distribution functions variable given instruments have intersections (b) strictly increasing a scalar unobservable However, demonstrate that, even...

This study examines the nonparametric instrumental variable model with discrete instruments and explores partial identification estimation of target parameter, which is a linear functional structural function. We include numerous parameters, such as difference between values function at two different points average effect hypothetical policy change. Informative bounds on parameter are derived using control approach shape restrictions. Illustrative examples demonstrate that restrictions have...

Regression discontinuity design (RDD) is widely adopted for causal inference under intervention determined by a continuous variable. While one interested in treatment effect heterogeneity subgroups many applications, RDD typically suffers from small subgroup-wise sample sizes, which makes the estimation results highly instable. To solve this issue, we introduce hierarchical (HRDD), Bayes approach pursuing RDD. A key feature of HRDD to employ pseudo-model based on loss function estimate...

This paper explores the identification and estimation of nonseparable panel data models. We show that structural function is nonparametrically identified when it strictly increasing in a scalar unobservable variable, conditional distributions variables do not change over time, joint support explanatory satisfies some weak assumptions. To identify target parameters, existing studies assume does there are "stayers", namely individuals with same regressor values two time periods. Our approach,...

In this study, we develop a novel estimation method for quantile treatment effects (QTE) under rank invariance and stationarity assumptions. Ishihara (2020) explores identification of the nonseparable panel data model these assumptions proposes parametric based on minimum distance method. However, when dimensionality covariates is large, using process computationally demanding. To overcome problem, propose two-step regression methods. We then show uniform asymptotic properties our estimator...

We present a new identification condition for regression discontinuity designs. replace the local randomization of Lee (2008) with two restrictions on its threat, namely, manipulation running variable. Furthermore, we provide first auxiliary assumption McCrary's diagnostic test to detect manipulation. Based our assumption, derive novel expression moments that immediately implies worst-case bounds Gerard, Rokkanen, and Rothe (2020) an enhanced interpretation their target parameters. highlight...

In this study, we develop a novel estimation method for quantile treatment effects (QTE) under rank invariance and stationarity assumptions. Ishihara (2020) explores identification of the nonseparable panel data model these assumptions proposes parametric based on minimum distance method. However, when dimensionality covariates is large, using process computationally demanding. To overcome problem, propose two-step regression methods. We then show uniform asymptotic properties our estimator...

In this study, we develop a novel estimation method for quantile treatment effects (QTE) under rank invariance and stationarity assumptions. Ishihara (2020 Ishihara, T. (2020), "Identification Estimation of Time-Varying Nonseparable Panel Data Models Without Stayers," Journal Econometrics, 215, 184–208. DOI: https://doi.org/10.1016/j.jeconom.2019.08.008.[Crossref] , [Google Scholar]) explores identification the nonseparable panel data model these assumptions proposes parametric based on...

This paper examines the economic consequences of manipulation social insurance benefits. Using administrative data public long-term care (LTCI) in Japan, we document novel discontinuity and bunching distribution health scores that determine benefit levels for LTCI. The observed suggests LTCI recipients tend to receive more generous benefits than they should because medical examiners manipulate recipients' score. To quantify impact on (LTC) expenditures, develop partial identification...