In estimating the average treatment effect in observational studies, the influence of confounders should be appropriately addressed. To this end, the propensity score is widely used. If the propensity scores are known...
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In estimating the average treatment effect in observational studies, the influence of confounders should be appropriately addressed. To this end, the propensity score is widely used. If the propensity scores are known for all the subjects, bias due to confounders can be adjusted by using the inverse probability weighting (IPW) by the propensity score. Since the propensity score is unknown in general, it is usually estimated by the parametric logistic regression model with unknown parameters estimated by solving the score equation under the strongly ignorable treatment assignment (SITA) assumption. Violation of the SITA assumption and/or misspecification of the propensity score model can cause serious bias in estimating the average treatment effect (ATE). To relax the SITA assumption, the IPW estimator based on the outcome-dependent propensity score has been successfully introduced. However, it still depends on the correctly specified parametric model and its identification. In this paper, we propose a simple sensitivity analysis method for unmeasured confounders. In the standard practice, the estimating equation is used to estimate the unknown parameters in the parametric propensity score model. Our idea is to make inferences on the (ATE) by removing restrictive parametric model assumptions while still utilizing the estimating equation. Using estimating equations as constraints, which the true propensity scores asymptotically satisfy, we construct the worst-case bounds for the ATE with linear programming. Differently from the existing sensitivity analysis methods, we construct the worst-case bounds with minimal assumptions. We illustrate our proposal by simulation studies and a real-world example.
In this note, two new very simple and computationally convenient external penalty functions are proposed for approximate solving of the linear programming problem with constraints-inequalities. The author ca...
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In this paper, a decision analysis method based on time series analysis and planning model is proposed. By establishing multiple regression equations, this paper analyzes the correlation between sales volume and cost-...
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Multi-objective programming is frequently used in forest management research to reconcile conflicting policy objectives. Given the frequent occurrence of fire in Pinus canariensis forests in the Canary Islands, forest...
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We consider the problem of designing output feedback controllers that use measurements from a set of landmarks to navigate through a cell-decomposable environment using duality, control Lyapunov function and control b...
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We consider the problem of designing output feedback controllers that use measurements from a set of landmarks to navigate through a cell-decomposable environment using duality, control Lyapunov function and control barrier function, and linear programming. We propose two objectives for navigating in an environment, one to traverse the environment by making loops and one by converging to a stabilization point while smoothing the transition between consecutive cells. We test our algorithms in a simulation environment, evaluating the robustness of the approach to practical conditions, such as bearing-only measurements, and measurements acquired with a camera with a limited field of view.
Both the increased utilization of photovoltaic (PV) and the consequent expansion of energy storage, leading to higher costs, are critical factors in the development of a techno-economic microgrid. To address this trad...
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The stable roommates problem is a non-bipartite version of the well-known stable matching problem. Teo and Sethuraman proved that, for each instance of the stable roommates problem in the complete graphs, there exists...
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Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph has no cycles of odd number of negative edges. Lapl...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph has no cycles of odd number of negative edges. Laplacian of a balanced signed graph has eigenvectors that map simply to ones in a similarity-transformed positive graph Laplacian, thus enabling reuse of well-studied spectral filters designed for positive graphs. We propose a fast method to learn a balanced signed graph Laplacian directly from data. Specifically, for each node i, to determine its polarity β i ∈{−1,1} and edge weights $\left\{ {{w_{i,j}}} \right\}_{j = 1}^N$, we extend a sparse inverse covariance formulation based on linear programming (LP) called CLIME, by adding linear constraints to enforce "consistent" signs of edge weights $\left\{ {{w_{i,j}}} \right\}_{j = 1}^N$ with the polarities of connected nodes—i.e., positive/negative edges connect nodes of same/opposing polarities. For each LP, we adopt projections on convex set (POCS) to determine a suitable CLIME parameter ρ > 0 that guarantees LP feasibility. We solve the resulting LP via an off-the-shelf LP solver. Experiments on synthetic and real-world datasets show that our balanced graph learning method outperforms competing methods and enables the use of spectral filters and graph neural networks designed for positive graphs on balanced signed graphs.
The research on cargo volume forecasting and manpower demand involves utilizing appropriate models and algorithms to predict future information based on historical cargo volume and personnel allocation data, thereby e...
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ISBN:
(数字)9798350389579
ISBN:
(纸本)9798350389586
The research on cargo volume forecasting and manpower demand involves utilizing appropriate models and algorithms to predict future information based on historical cargo volume and personnel allocation data, thereby enhancing resource optimization and driving development. After preprocessing the data provided by a certain enterprise, this study utilizes LSTM (Long Short-Term Memory) patterns with reference to the daily cargo volume of each sorting center over the past four months and the hourly cargo volume over the past 30 days. A stationary time series model is constructed, and LSTM neural network parameters are set. Through rolling forecasts conducted via Python programs, an effective prediction of the hourly cargo volume for each of the 57 sorting centers over the next 30 days is achieved. Subsequently, a linear programming model is established to determine the objective function for maximizing personnel arrangement efficiency. Constraints such as single-shift attendance and employee headcount limits are selected. The Branch and Bound algorithm is employed to initialize upper and lower bounds and determine the feasible space, effectively yielding personnel deployment plans for different time periods at each sorting center over the next 30 days. The forecasting methods and data obtained in this paper play a significant role in resource allocation and service quality improvement in related industries.
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