891 research outputs found

    Nonparametric tests of conditional treatment effects

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    We develop a general class of nonparametric tests for treatment effects conditional on covariates. We consider a wide spectrum of null and alternative hypotheses regarding conditional treatment effects, including (i) the null hypothesis of the conditional stochastic dominance between treatment and control groups; ii) the null hypothesis that the conditional average treatment effect is positive for each value of covariates; and (iii) the null hypothesis of no distributional (or average) treatment effect conditional on covariates against a one-sided (or two-sided) alternative hypothesis. The test statistics are based on L1-type functionals of uniformly consistent nonparametric kernel estimators of conditional expectations that characterize the null hypotheses. Using the Poissionization technique of Giné et al. (2003), we show that suitably studentized versions of our test statistics are asymptotically standard normal under the null hypotheses and also show that the proposed nonparametric tests are consistent against general fixed alternatives. Furthermore, it turns out that our tests have non-negligible powers against some local alternatives that are n−½ different from the null hypotheses, where n is the sample size. We provide a more powerful test for the case when the null hypothesis may be binding only on a strict subset of the support and also consider an extension to testing for quantile treatment effects. We illustrate the usefulness of our tests by applying them to data from a randomized, job training program (LaLonde, 1986) and by carrying out Monte Carlo experiments based on this dataset

    Performance comparison of point and spatial access methods

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    In the past few years a large number of multidimensional point access methods, also called multiattribute index structures, has been suggested, all of them claiming good performance. Since no performance comparison of these structures under arbitrary (strongly correlated nonuniform, short "ugly") data distributions and under various types of queries has been performed, database researchers and designers were hesitant to use any of these new point access methods. As shown in a recent paper, such point access methods are not only important in traditional database applications. In new applications such as CAD/CIM and geographic or environmental information systems, access methods for spatial objects are needed. As recently shown such access methods are based on point access methods in terms of functionality and performance. Our performance comparison naturally consists of two parts. In part I we w i l l compare multidimensional point access methods, whereas in part I I spatial access methods for rectangles will be compared. In part I we present a survey and classification of existing point access methods. Then we carefully select the following four methods for implementation and performance comparison under seven different data files (distributions) and various types of queries: the 2-level grid file, the BANG file, the hB-tree and a new scheme, called the BUDDY hash tree. We were surprised to see one method to be the clear winner which was the BUDDY hash tree. It exhibits an at least 20 % better average performance than its competitors and is robust under ugly data and queries. In part I I we compare spatial access methods for rectangles. After presenting a survey and classification of existing spatial access methods we carefully selected the following four methods for implementation and performance comparison under six different data files (distributions) and various types of queries: the R-tree, the BANG file, PLOP hashing and the BUDDY hash tree. The result presented two winners: the BANG file and the BUDDY hash tree. This comparison is a first step towards a standardized testbed or benchmark. We offer our data and query files to each designer of a new point or spatial access method such that he can run his implementation in our testbed

    Testing for Stochastic Dominance Efficiency

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    We propose a new test of the stochastic dominance efficiency of a given portfolio over a class of portfolios. We establish its null and alternative asymptotic properties, and define a method for consistently estimating critical values. We present some numerical evidence that our tests work well in moderate sized samples

    Selection of Optimized Retaining Wall Technique Using Self-Organizing Maps

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    Construction projects in urban areas tend to be associated with high-rise buildings and are of very large-scales; hence, the importance of a project’s underground construction work is significant. In this study, a rational model based on machine learning (ML) was developed. ML algorithms are programs that can learn from data and improve from experience without human intervention. In this study, self-organizing maps (SOMs) were utilized. An SOM is an alternative to existing ML methods and involves a subjective decision-making process because a developed model is used for data training to classify and effectively recognize patterns embedded in the input data space. In addition, unlike existing methods, the SOM can easily create a feature map by mapping multidimensional data to simple two-dimensional data. The objective of this study is to develop an SOM model as a decision-making approach for selecting a retaining wall technique. N-fold cross-validation was adopted to validate the accuracy of the SOM model and evaluate its reliability. The findings are useful for decision-making in selecting a retaining wall method, as demonstrated in this study. The maximum accuracy of the SOM was 81.5%, and the average accuracy was 79.8%

    4,4’-Dichlorodiphenyltrichloroethane (DDT) and 4,4’-dichlorodiphenyldichloroethylene (DDE) promote adipogenesis in 3TL1 adipocyte cell culture

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    4,4’-Dichlorodiphenyltrichloroethane (DDT), a chlorinated hydrocarbon insecticide, was extensively used in the 1940s and 1950s. DDT is mainly metabolically converted into 4,4’- dichlorodiphenyldichloroethylene (DDE). Even though most countries banned DDT in the 1970s, due to the highly lipophilic nature and very stable characteristics, DDT and its metabolites are present ubiquitously in the environment, including food. Recently, there are publications on relationships between exposure to insecticides, including DDT and DDE, and weight gain and altered glucose homeostasis. However, there are limited reports regarding DDT or DDE and adipogenesis, thus we investigated effects of DDT and DDE on adipogenesis using 3T3-L1 preadipocytes. Treatment of DDT or DDE resulted in increased lipid accumulation accompanied by increased expression of CCAAT/enhancer-binding protein (C/EBP), peroxisome-proliferator activated receptor- (PPAR), fatty acid synthase (FAS), acetyl-CoA carboxylase (ACC), adipose triglyceride lipase, and leptin. Moreover, treatment of DDT or DDE increased protein levels of C/EBP, PPAR, AMP-activated protein kinase- (AMPK), and ACC, while significant decrease of phosphorylated forms of AMPK and ACC were observed. These finding suggest that increased lipid accumulation caused by DDT and DDE may mediate AMPK pathway in 3T3-L1 adipocytes