The Experts below are selected from a list of 441360 Experts worldwide ranked by ideXlab platform
Corwin M Zigler - One of the best experts on this subject based on the ideXlab platform.
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uncertainty in the Design Stage of two Stage bayesian propensity score analysis
Statistics in Medicine, 2020Co-Authors: Shirley Liao, Corwin M ZiglerAbstract:The two-Stage process of propensity score analysis (PSA) includes a Design Stage where propensity scores (PSs) are estimated and implemented to approximate a randomized experiment and an analysis Stage where treatment effects are estimated conditional on the Design. This article considers how uncertainty associated with the Design Stage impacts estimation of causal effects in the analysis Stage. Such Design uncertainty can derive from the fact that the PS itself is an estimated quantity, but also from other features of the Design Stage tied to choice of PS implementation. This article offers a procedure for obtaining the posterior distribution of causal effects after marginalizing over a distribution of Design-Stage outputs, lending a degree of formality to Bayesian methods for PSA that have gained attention in recent literature. Formulation of a probability distribution for the Design-Stage output depends on how the PS is implemented in the Design Stage, and propagation of uncertainty into causal estimates depends on how the treatment effect is estimated in the analysis Stage. We explore these differences within a sample of commonly used PS implementations (quantile stratification, nearest-neighbor matching, caliper matching, inverse probability of treatment weighting, and doubly robust estimation) and investigate in a simulation study the impact of statistician choice in PS model and implementation on the degree of between- and within-Design variability in the estimated treatment effect. The methods are then deployed in an investigation of the association between levels of fine particulate air pollution and elevated exposure to emissions from coal-fired power plants.
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uncertainty in the Design Stage of two Stage bayesian propensity score analysis
arXiv: Methodology, 2018Co-Authors: Shirley Liao, Corwin M ZiglerAbstract:The two-Stage process of propensity score analysis (PSA) includes a Design Stage where propensity scores are estimated and implemented to approximate a randomized experiment and an analysis Stage where treatment effects are estimated conditional upon the Design. This paper considers how uncertainty associated with the Design Stage impacts estimation of causal effects in the analysis Stage. Such Design uncertainty can derive from the fact that the propensity score itself is an estimated quantity, but also from other features of the Design Stage tied to choice of propensity score implementation. This paper offers a procedure for obtaining the posterior distribution of causal effects after marginalizing over a distribution of Design-Stage outputs, lending a degree of formality to Bayesian methods for PSA (BPSA) that have gained attention in recent literature. Formulation of a probability distribution for the Design-Stage output depends on how the propensity score is implemented in the Design Stage, and propagation of uncertainty into causal estimates depends on how the treatment effect is estimated in the analysis Stage. We explore these differences within a sample of commonly-used propensity score implementations (quantile stratification, nearest-neighbor matching, caliper matching, inverse probability of treatment weighting, and doubly robust estimation) and investigate in a simulation study the impact of statistician choice in PS model and implementation on the degree of between- and within-Design variability in the estimated treatment effect. The methods are then deployed in an investigation of the association between levels of fine particulate air pollution and elevated exposure to emissions from coal-fired power plants.
Shirley Liao - One of the best experts on this subject based on the ideXlab platform.
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uncertainty in the Design Stage of two Stage bayesian propensity score analysis
Statistics in Medicine, 2020Co-Authors: Shirley Liao, Corwin M ZiglerAbstract:The two-Stage process of propensity score analysis (PSA) includes a Design Stage where propensity scores (PSs) are estimated and implemented to approximate a randomized experiment and an analysis Stage where treatment effects are estimated conditional on the Design. This article considers how uncertainty associated with the Design Stage impacts estimation of causal effects in the analysis Stage. Such Design uncertainty can derive from the fact that the PS itself is an estimated quantity, but also from other features of the Design Stage tied to choice of PS implementation. This article offers a procedure for obtaining the posterior distribution of causal effects after marginalizing over a distribution of Design-Stage outputs, lending a degree of formality to Bayesian methods for PSA that have gained attention in recent literature. Formulation of a probability distribution for the Design-Stage output depends on how the PS is implemented in the Design Stage, and propagation of uncertainty into causal estimates depends on how the treatment effect is estimated in the analysis Stage. We explore these differences within a sample of commonly used PS implementations (quantile stratification, nearest-neighbor matching, caliper matching, inverse probability of treatment weighting, and doubly robust estimation) and investigate in a simulation study the impact of statistician choice in PS model and implementation on the degree of between- and within-Design variability in the estimated treatment effect. The methods are then deployed in an investigation of the association between levels of fine particulate air pollution and elevated exposure to emissions from coal-fired power plants.
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uncertainty in the Design Stage of two Stage bayesian propensity score analysis
arXiv: Methodology, 2018Co-Authors: Shirley Liao, Corwin M ZiglerAbstract:The two-Stage process of propensity score analysis (PSA) includes a Design Stage where propensity scores are estimated and implemented to approximate a randomized experiment and an analysis Stage where treatment effects are estimated conditional upon the Design. This paper considers how uncertainty associated with the Design Stage impacts estimation of causal effects in the analysis Stage. Such Design uncertainty can derive from the fact that the propensity score itself is an estimated quantity, but also from other features of the Design Stage tied to choice of propensity score implementation. This paper offers a procedure for obtaining the posterior distribution of causal effects after marginalizing over a distribution of Design-Stage outputs, lending a degree of formality to Bayesian methods for PSA (BPSA) that have gained attention in recent literature. Formulation of a probability distribution for the Design-Stage output depends on how the propensity score is implemented in the Design Stage, and propagation of uncertainty into causal estimates depends on how the treatment effect is estimated in the analysis Stage. We explore these differences within a sample of commonly-used propensity score implementations (quantile stratification, nearest-neighbor matching, caliper matching, inverse probability of treatment weighting, and doubly robust estimation) and investigate in a simulation study the impact of statistician choice in PS model and implementation on the degree of between- and within-Design variability in the estimated treatment effect. The methods are then deployed in an investigation of the association between levels of fine particulate air pollution and elevated exposure to emissions from coal-fired power plants.
Ahmad Jrade - One of the best experts on this subject based on the ideXlab platform.
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integrating building information modeling bim and leed system at the conceptual Design Stage of sustainable buildings
Sustainable Cities and Society, 2015Co-Authors: Farzad Jalaei, Ahmad JradeAbstract:Abstract Designing environmentally friendly buildings that provide both high performance and cost savings is of increasing interest in the development of sustainable cities. Today, we are looking at not just buildings’ certification but sustainable practices that go beyond ratings to satisfy our social responsibilities. The construction industry in general will benefit from an integrated tool that will help optimize the selection process of materials, equipments, and systems at every Stage of a proposed building's life. Building information modeling (BIM) has the potential to aid Designers to select the right type of materials during the early Design Stage and to make vital decisions that have great impacts on the life cycle of sustainable buildings. This paper describes a methodology that integrates BIM with the Canadian green building certification system (LEED©). Also, it explains how this integration would assist project teams in making sustainability related decisions while accumulating the required number of points based on the applied green building rating system. The methodology depicts the implementation of a model that automatically calculates the compiled number of LEED certification points and related registration costs for green and certified materials used in Designing sustainable buildings all within the concepts of BIM. Using BIM in this methodology will help Designers to invent and animate sustainable buildings in 3D mode easily and efficiently at the conceptual Stage. The Design information of the proposed sustainable building will be produced in a timely manner by using new plug-ins, which are developed for that reason, and which will link the BIM model with an external database that stores sustainable materials and assembly groups. A real case project is presented to illustrate the usefulness and capabilities of the proposed model.
Philippe Rigo - One of the best experts on this subject based on the ideXlab platform.
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Ship complexity assessment at the concept Design Stage
Journal of Marine Science and Technology, 2011Co-Authors: Jean-david Caprace, Philippe RigoAbstract:An innovative complexity metric is introduced that provides a way to compare similar or different ship types and sizes at the contract Design Stage. The goal is to provide the Designer with this information throughout the Design process so that an efficient Design is obtained during the first Design run. Application to and validation on real passenger ships indicate that there is a significant correlation between the error in an engineer’s judgement of complexity and the cost assessment error. It follows that this tool could be used to improve knowledge of the ship’s complexity at the contract Design Stage, and even to try to optimise the Design if the complexity criteria are not fixed by the shipowners.
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an analytical cost assessment module for the detailed Design Stage
2006Co-Authors: Jean-david Caprace, Renaud Warnotte, Philippe Rigo, Sandrine LeviolAbstract:The main goal of the project is to implement a “real time” and automatic cost assessment model of the ship hull construction, which integrates the Design criteria and production parameters. The presented method for short-term cost assessment promises to increase the productivity. Nowadays, cost assessment is a key task of an integrated ship Design. The various methods to estimate the production cost differs with the required information (input data). The less information is needed, the earlier this method can be used in the Design process. The more information is used, the better we can assess the differences between Design alternatives. This means: − At the basic Design Stage: validate the budget and give a reliable bidding price, − At the detailed Design Stage:, plan the deadlines and establish the production schedule, − At the scheduling production Stage: distribute the workload between the various production workshops and assess the productivity. A first prototype of cost assessment module reaches the validation Stage at the Alstom Marine St Nazaire shipyard (Chantiers de l’Atlantique) within the framework of the European project InterSHIP which has partly financed the study. In the future, cost assessment will become increasing important. It is proposed to assist/help the Designers by a “real time” follow-up of the cost, starting at the earliest conceptual Design Stage up to the latest detailed Design Stage. The development such a cost assessment tool requires considering simultaneously the Design criteria and the production parameters. Designers will consequently be able to choose the least expensive options at each step of the Design procedure (earlier is of course better).
Jaeil Park - One of the best experts on this subject based on the ideXlab platform.
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Study on the Design Factors Affecting the Operating Actions that Can Be Used Easily at the Design Stage
Advanced Materials Research, 2014Co-Authors: Jaeil ParkAbstract:Evaluating the assemblability at the Design Stage is important because of the cost competitiveness of company. However by compared results of the sequence of bottle neck process of production Stage, the Lucas DFA (Design for assembly) and Boothroyd & Dewhurst DFA is not enough to evaluate the assenblability accurately. Therefore, in this paper, we proposed three Design factors which is affecting assembly but not mentioned at DFA. These Design factors would help the Designers to evaluate assemblability and improve the productivity.
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time estimation method for manual assembly using modapts technique in the product Design Stage
International Journal of Production Research, 2014Co-Authors: Jaeil ParkAbstract:This paper aims to propose an accurate and quick assembly time estimation method using the modular arrangement of predetermined time standards in the product Design Stage. It describes a classification of 2382 assembly operations that are incurred in manually assembling consumer electronics such as air conditioners, washing machines and refrigerators, and a method of choosing representative motions comprising work elements by examining the frequency distribution of the assembly operation’s motions. It then presents criteria for assigning time values associated with the movement of the representative motions using the Design factors employed in Design for assembly and the layout factors of an assembly line. A case study then presents the practicality of the method, the statistical results of which indicate that the proposed method would be accurate enough for practical purposes.
