A semiparametric method for estimating the scale of fluctuation

AbstractObjectivesEstimation of the cascade of HIV care is essential for evaluating care and treatment programs, informing policy makers and assessing targets such as 90-90-90. A challenge to estimating the cascade based on electronic health record concerns patients “churning” in and out of care. Correctly estimating this dynamic phenomenon in resource-limited settings, such as those found in sub-Saharan Africa, is challenging because of the significant death under-reporting. An approach to partially recover information on the unobserved deaths is a double-sampling design, where a small subset of individuals with a missed clinic visit is intensively outreached in the community to actively ascertain their vital status. This approach has been adopted in several programs within the East Africa regional IeDEA consortium, the context of our motivating study. The objective of this paper is to propose a semiparametric method for the analysis of competing risks data with incomplete outcome ascertainment.MethodsBased on data from double-sampling designs, we propose a semiparametric inverse probability weighted estimator of key outcomes during a gap in care, which are crucial pieces of the care cascade puzzle.ResultsSimulation studies suggest that the proposed estimators provide valid estimates in settings with incomplete outcome ascertainment under a set of realistic assumptions. These studies also illustrate that a naïve complete-case analysis can provide seriously biased estimates. The methodology is applied to electronic health record data from the East Africa IeDEA Consortium to estimate death and return to care during a gap in care.ConclusionsThe proposed methodology provides a robust approach for valid inferences about return to care and death during a gap in care, in settings with death under-reporting. Ultimately, the resulting estimates will have significant consequences on program construction, resource allocation, policy and decision making at the highest levels.

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A semiparametric method for predicting bankruptcy

Journal of Forecasting ◽

10.1002/for.1027 ◽

2007 ◽

Vol 26 (5) ◽

pp. 317-342 ◽

Cited By ~ 31

Author(s):

Ruey-Ching Hwang ◽

K. F. Cheng ◽

Jack C. Lee

Keyword(s):

Semiparametric Method

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Semiparametric method for identifying multiple change-points in financial market

Communications in Statistics - Simulation and Computation ◽

10.1080/03610918.2017.1359285 ◽

2020 ◽

Vol 49 (6) ◽

pp. 1429-1444

Author(s):

Shuxia Zhang ◽

Boping Tian

Keyword(s):

Financial Market ◽

Change Points ◽

Semiparametric Method

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Scale of fluctuation as a descriptor of soil variability

Probabilistic Methods in Geotechnical Engineering ◽

10.1201/9781003077749-22 ◽

2020 ◽

pp. 233-239

Author(s):

Damika Wickremesinghe ◽

R.G. Campanella

Keyword(s):

Soil Variability ◽

Scale Of Fluctuation

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Probabilistic Bearing Capacity of a Pavement Resting on Fibre Reinforced Embankment Considering Soil Spatial Variability

Frontiers in Built Environment ◽

10.3389/fbuil.2021.628016 ◽

2021 ◽

Vol 7 ◽

Author(s):

Kouseya Choudhuri ◽

Debarghya Chakraborty

Keyword(s):

Spatial Variability ◽

Soil Properties ◽

Bearing Capacity ◽

Coefficient Of Variation ◽

Friction Angle ◽

Horizontal Scale ◽

Vertical Scale ◽

Scale Of Fluctuation ◽

The Mean ◽

The Finite Difference Method

This paper intends to examine the influence of spatial variability of soil properties on the probabilistic bearing capacity of a pavement located on the crest of a fibre reinforced embankment. An anisotropic random field, in combination with the finite difference method, is used to carry out the probabilistic analyses. The cohesion and internal friction angle of the soil are assumed to be lognormally distributed. The Monte Carlo simulations are carried out to obtain the mean and coefficient of variation of the pavement bearing capacity. The mean bearing capacity of the pavement is found to decrease with the increase in horizontal scale of fluctuation for a constant vertical scale of fluctuation; whereas, the coefficient of variation of the bearing capacity increases with the increase in horizontal scale of fluctuation. However, both the mean and coefficient of variation of bearing capacity of the pavement are observed to be increasing with the increase in vertical scale of fluctuation for a constant horizontal scale of fluctuation. Apart from the different scales of fluctuation, the effects of out of the plane length of the embankment and randomness in soil properties on the probabilistic bearing capacity are also investigated in the present study.

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An Analysis of Long Memory in Volatility for Asian Stock Markets

Review of Pacific Basin Financial Markets and Policies ◽

10.1142/s0219091500000200 ◽

2000 ◽

Vol 03 (03) ◽

pp. 309-330 ◽

Cited By ~ 7

Author(s):

Huimin Chung ◽

William T. Lin ◽

Soushan Wu

Keyword(s):

Financial Markets ◽

Memory Effect ◽

Long Memory ◽

Stock Markets ◽

Asia Pacific ◽

Size Distortion ◽

Asset Return ◽

Semiparametric Method ◽

Aggregation Size ◽

And Shifts

One of the important questions in studies of asset return and volatility has been how long the effects of shocks persist. In this article, the modified R/S statistic of Lo (1991) and the robust semiparametric method of Lobato and Robinson (1997) are applied to investigate the long memory properties in return and volatility of Asian financial markets. For the return series, we find little evidence of long memory, while the empirical results support the hypothesis of long memory in volatility for Asia-Pacific stock markets. We also discuss the possible causes of spurious long memory effect in volatility, namely aggregation, size distortion, and shifts in variance. Our empirical evidence shows that spurious long memory effect in volatility might occur as a result of shifts in variance for some Asian stock markets.

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Semiparametric Regression Analysis on the AB Data with Zero-Point Drift

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.568-570.227 ◽

2014 ◽

Vol 568-570 ◽

pp. 227-232

Author(s):

Yuan Tian ◽

Jie Luo

Keyword(s):

Least Squares Method ◽

Simulation Analysis ◽

Semiparametric Regression ◽

Zero Point ◽

Filter Method ◽

Best Linear Unbiased Estimator ◽

Penalized Least Squares ◽

Semiparametric Regression Model ◽

Semiparametric Method ◽

Best Linear Unbiased

To deal with the AB data with zero-point drift, Swanson and Schlamminger have proposed a filter method to remove the drift of any desired polynomial order, and then give the best linear unbiased estimator of the observable, on the condition that the order of drift is known. Since this method directly removes the drift, one can not know the tendency of the systematic error. This paper proposes to construct a semiparametric regression model for the data, and then take the penalized least squares method to estimate the observable and the drift tendency simultaneously. Simulation analysis shows that the semiparametric method can reach the same accuracy of the filter method, and the estimation of the drift fits well with its actual value.

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