Bias Reduction Technique Research Articles

New directions in cancer and aging: State of the science and recommendations to improve the quality of evidence on the intersection of aging with cancer control.

BackgroundThe global population of older cancer survivors is growing. However, the intersections of aging‐related health risks across the cancer control continuum are poorly understood, limiting the integration of aging into cancer control research and practice. The objective of this study was to review the state of science and provide future directions to improve the quality of evidence in 6 priority research areas in cancer and aging.MethodsThe authors identified priority research areas in cancer and aging through an evidence‐based Research Jam process involving 32 investigators and trainees from multiple disciplines and research centers in aging and cancer; then, they conducted a narrative review of the state of the science and future directions to improve the quality of evidence in these research areas. Priority research areas were defined as those in which gaps in scientific evidence or clinical practice limit the health and well‐being of older adults with cancer.ResultsSix priority research areas were identified: cognitive and physical functional outcomes of older cancer survivors, sampling issues in studies of older cancer survivors, risk and resilience across the lifespan, caregiver support and well‐being, quality of care for older patients with cancer, and health disparities. Evidence in these areas could be improved through the incorporation of bias reduction techniques into longitudinal studies of older cancer survivors, novel data linkage, and improved representation of older adults in cancer research.ConclusionsThe priority research areas and methodologies identified here may be used to guide interdisciplinary research and improve the quality of evidence on cancer and aging.

Radiomics: A primer for the radiation oncologist

PurposeRadiomics are a set of methods used to leverage medical imaging and extract quantitative features that can characterize a patient's phenotype. All modalities can be used with several different software packages. Specific informatics methods can then be used to create meaningful predictive models. In this review, we will explain the major steps of a radiomics analysis pipeline and then present the studies published in the context of radiation therapy. MethodsA literature review was performed on Medline using the search engine PubMed. The search strategy included the search terms “radiotherapy”, “radiation oncology” and “radiomics”. The search was conducted in July 2019 and reference lists of selected articles were hand searched for relevance to this review. ResultsA typical radiomics workflow always includes five steps: imaging and segmenting, data curation and preparation, feature extraction, exploration and selection and finally modeling. In radiation oncology, radiomics studies have been published to explore different clinical outcome in lung (n=5), head and neck (n=5), esophageal (n=3), rectal (n=3), pancreatic (n=2) cancer and brain metastases (n=2). The quality of these retrospective studies is heterogeneous and their results have not been translated to the clinic. ConclusionRadiomics has a great potential to predict clinical outcome and better personalize treatment. But the field is still young and constantly evolving. Improvement in bias reduction techniques and multicenter studies will hopefully allow more robust and generalizable models.

Speckle Noise Suppression in SAR Images Using a Three-Step Algorithm.

Speckle noise can reduce the image quality of synthetic aperture radar (SAR) and complicate image interpretation. This study proposes a novel three-step approach based on the conventional probabilistic patch-based (PPB) algorithm to minimize the impact of bright structures on speckle suppression. The first step improves the calculation accuracy of the weight by pre-processing speckle noise with a linear minimum mean-square error filter and reassessing similarity between pixels. In the second step, an iterative method is developed to avoid interfering with bright structures and acquires a more accurate homogeneous factor by adaptively changing the size of the search window. In the final step, the spreading and blurring of bright structures is corrected using a modified bias-reduction technique. Experimental results demonstrate the proposed algorithm has improved performance for both speckle suppression and preservation of edges and textures, evaluated by indicators including the equivalent number of looks, the edge preservation index, the mean, and standard deviation of ratio images.

Bias reduction in kernel density estimation

ABSTRACTIn this paper, we propose two kernel density estimators based on a bias reduction technique. We study the properties of these estimators and compare them with Parzen–Rosenblatt's density estimator and Mokkadem, A., Pelletier, M., and Slaoui, Y. (2009, ‘The stochastic approximation method for the estimation of a multivariate probability density’, J. Statist. Plann. Inference, 139, 2459–2478) is density estimators. It turns out that, with an adequate choice of the parameters of the two proposed estimators, the rate of convergence of two estimators will be faster than the two classical estimators and the asymptotic MISE (Mean Integrated Squared Error) will be smaller than the two classical estimators. We corroborate these theoretical results through simulations.

Thinking about Thinking: Beyond Decision-Making Rationalism and the Emergence of Behavioral Ethics

This article examines behavioral ethics propositions, cognitive distortions, and bias reduction techniques. This is followed by the practitioner and academician response of the public administration profession to this nascent, intriguing field. The purpose is to review critical components of behavioralism and to examine the present status of behavioral decision science in the practice and study of public service ethics. How people think about thinking is important, because to address problems effectively, there must be an understanding of why individuals believe what they believe, how they think about issues, and why decisions are made.

Prediction of weakly locally stationary processes by auto-regression

In this contribution we introduce weakly locally stationary time series through the local approximation of the non-stationary covariance structure by a stationary one. This allows us to define autoregression coefficients in a non-stationary context, which, in the particular case of a locally stationary Time Varying Autoregressive (TVAR) process, coincide with the generating coefficients. We provide and study an estimator of the time varying autoregression coefficients in a general setting. The proposed estimator of these coefficients enjoys an optimal minimax convergence rate under limited smoothness conditions. In a second step, using a bias reduction technique, we derive a minimax-rate estimator for arbitrarily smooth time-evolving coefficients, which outperforms the previous one for large data sets. In turn, for TVAR processes, the predictor derived from the estimator exhibits an optimal minimax prediction rate.

BOOSTING AND BAGGING IN KERNEL DENSITY ESTIMATION

Boosting is a bias reduction technique while bagging is a variance reduction method. These two methods aim at reducing the asymptotic mean integrated square error (AMISE). This study aims to show that bagging is a boosting algorithm in kernel density estimation since both techniques use large smoothing parameter(s). This relationship was verified by real and simulated data

Residential segregation of foreigners: An analysis of the Italian city of Palermo

Ethnic residential segregation in Italy is an emerging key question, which will be crucial in the definition and implementation of both urbanistic and social policies. This paper focuses on this phenomenon in the Italian city of Palermo. We use individual data for all the population residing in the city at 31 December 2011, organized by ethnicity and district. Firstly, we describe the spatial distribution and the residential segregation of foreigners in the city, applying many different segregation measures. Among the others, we also apply the Duncan and Duncan dissimilarity index. Finally, we apply a recent methodological approach, which allows reducing the bias of the Duncan’s index and performs better than previous bias reduction techniques, in terms of both mean bias and mean square error. Annalisa Busetta, Researcher, Department of Economics, Business and Statistics, University of Palermo, Italy. E-mail: annalisa.busetta@unipa.it . Angelo Mazza, Assistant Professor, Department of Economics and Business, University of Catania, Italy. E-mail: a.mazza@unict.it . Manuela STRANGES, Researcher, Department of Economics, Statistics and Finance, University of Calabria, Italy. E-mail: manuela.stranges@unical.it .

Bias reduction in sub-pixel image registration based on the anti-symmetric feature

A simple but effective bias reduction technique is developed based on the anti-symmetric feature of the sub-pixel image registration bias. Depending on the error propagation theory, the anti-symmetric feature is mathematically derived through a classical subset-based digital image correlation algorithm considering the most common error sources i.e. the grey-intensity interpolation scheme and random noise. This leads to the sub-pixel registration bias formulated in the form of an analytic expression that consists of the interpolation-induced phase error and the random noise induced bias, which is also further illustrated by numerical simulations. Bias reduction is achieved by compensating the bias at a certain sub-pixel displacement with the bias at the corresponding anti-symmetric sub-pixel displacement where the Fourier shift theorem is employed to alter the displacement without introducing extra bias. The performance of proposed method is validated using numerical case studies with different interpolation schemes and noise levels, by which the sub-pixel registration bias is shown to be significantly reduced.

A Multiplicative Bias Corrected Nonparametric Estimator for a Finite Population Mean

Nonparametric regression has been widely exploited in survey sampling to construct estimators for the finite population mean and total. It offers greater flexibility with regard to model specification and is therefore applicable to a wide range of problems. A major drawback of estimators constructed under this framework is that they are generally biased due to the boundary problem and therefore require modification at the boundary points. In this study, a bias robust estimator for the finite population mean based on the multiplicative bias reduction technique is proposed. A simulation study is performed to develop the properties of this estimator as well as assess its performance relative to other existing estimators. The asymptotic properties and coverage rates of our proposed estimator are better than those exhibited by the Nadaraya Watson estimator and the ratio estimator.

Resampling to Address the Winner's Curse in Genetic Association Analysis of Time to Event.

ABSTRACTThe “winner's curse” is a subtle and difficult problem in interpretation of genetic association, in which association estimates from large‐scale gene detection studies are larger in magnitude than those from subsequent replication studies. This is practically important because use of a biased estimate from the original study will yield an underestimate of sample size requirements for replication, leaving the investigators with an underpowered study. Motivated by investigation of the genetics of type 1 diabetes complications in a longitudinal cohort of participants in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Genetics Study, we apply a bootstrap resampling method in analysis of time to nephropathy under a Cox proportional hazards model, examining 1,213 single‐nucleotide polymorphisms (SNPs) in 201 candidate genes custom genotyped in 1,361 white probands. Among 15 top‐ranked SNPs, bias reduction in log hazard ratio estimates ranges from 43.1% to 80.5%. In simulation studies based on the observed DCCT/EDIC genotype data, genome‐wide bootstrap estimates for false‐positive SNPs and for true‐positive SNPs with low‐to‐moderate power are closer to the true values than uncorrected naïve estimates, but tend to overcorrect SNPs with high power. This bias‐reduction technique is generally applicable for complex trait studies including quantitative, binary, and time‐to‐event traits.

On the occurrence times of componentwise maxima and bias in likelihood inference for multivariate max-stable distributions

Full likelihood-based inference for high-dimensional multivariate extreme value distributions, or max-stable processes, is feasible when incorporating occurrence times of the maxima; without this information, $d$-dimensional likelihood inference is usually precluded due to the large number of terms in the likelihood. However, some studies have noted bias when performing high-dimensional inference that incorporates such event information, particularly when dependence is weak. We elucidate this phenomenon, showing that for unbiased inference in moderate dimensions, dimension $d$ should be of a magnitude smaller than the square root of the number of vectors over which one takes the componentwise maximum. A bias reduction technique is suggested and illustrated on the extreme value logistic model.

Robust explicit estimation of the two-parameter Birnbaum–Saunders distribution

The two-parameter Birnbaum–Saunders distribution is widely applicable to model failure times of fatiguing materials. Its maximum-likelihood estimators (MLEs) are very sensitive to outliers and also have no closed-form expressions. This motivates us to develop some alternative estimators. In this paper, we develop two robust estimators, which are also explicit functions of sample observations and are thus easy to compute. We derive their breakdown points and carry out extensive Monte Carlo simulation experiments to compare the performance of all the estimators under consideration. It has been observed from the simulation results that the proposed estimators outperform in a manner that is approximately comparable with the MLEs, whereas they are far superior in the presence of data contamination that often occurs in practical situations. A simple bias-reduction technique is presented to reduce the bias of the recommended estimators. Finally, the practical application of the developed procedures is illustrated with a real-data example.

Reducing the mean squared error in kernel density estimation

In this article, we propose a version of a kernel density estimator which reduces the mean squared error of the existing kernel density estimator by combining bias reduction and variance reduction techniques. Its theoretical properties are investigated, and a Monte Carlo simulation study supporting theoretical results on the proposed estimator is given.

Signal detection theory and vestibular perception: III. Estimating unbiased fit parameters for psychometric functions

Psychophysics generally relies on estimating a subject's ability to perform a specific task as a function of an observed stimulus. For threshold studies, the fitted functions are called psychometric functions. While fitting psychometric functions to data acquired using adaptive sampling procedures (e.g., "staircase" procedures), investigators have encountered a bias in the spread ("slope" or "threshold") parameter that has been attributed to the serial dependency of the adaptive data. Using simulations, we confirm this bias for cumulative Gaussian parametric maximum likelihood fits on data collected via adaptive sampling procedures, and then present a bias-reduced maximum likelihood fit that substantially reduces the bias without reducing the precision of the spread parameter estimate and without reducing the accuracy or precision of the other fit parameters. As a separate topic, we explain how to implement this bias reduction technique using generalized linear model fits as well as other numeric maximum likelihood techniques such as the Nelder-Mead simplex. We then provide a comparison of the iterative bootstrap and observed information matrix techniques for estimating parameter fit variance from adaptive sampling procedure data sets. The iterative bootstrap technique is shown to be slightly more accurate; however, the observed information technique executes in a small fraction (0.005 %) of the time required by the iterative bootstrap technique, which is an advantage when a real-time estimate of parameter fit variance is required.

Comparison of two bias reduction techniques for the Rasch model

This study examines the effect of two different techniques of bias reduction in the case of the fixed persons-fixed items formulation of the Rasch model. A first approach can be considered “corrective”, because it consists simply in correcting ex-post the joint maximum likelihood estimates by a factor (m-1)/m, were m represents the number of items and/or persons. A second approach, which is an application of a quite general formula for reducing the maximum likelihood estimation bias, can be considered “preventive”, because it arises from a modification of the score function. A comparative study of these two techniques was done using simulated data.

A bias-reduction technique for Monte Carlo pricing of early-exercise options

We present a new method for reducing the bias present in Monte-Carlo estimators of the price of American-style contingent claims. At each exercise opportunity (in a time discretization), we assume there is an unbiased estimator of the claim value at the next exercise opportunity. We approximate the distribution of this statistic using the central limit theorem, and use this to derive an asymptotic expression for the bias. This expression is easily estimated in the context of a simulation, which allows for the straightforward computation of bias-reduced estimators of the claim value. We conclude by presenting a well-studied multivariate pricing example to show that this method offers significant improvements over the vanilla stochastic mesh technique, and that it is much more computationally efficient approach to reducing bias than nonparametric bootstrapping.

Response shrinkage estimation in multinomial logit models

A shrinkage estimation method for multinomial logit models is developed. The proposed method is based on shrinking the responses for each category towards the underlying probabilities. The estimator is also used in combination with Pregibon's resistant fitting. The resulting estimator can also be used to control the over-estimation of Pregibon's resistant estimator. The proposed method handles not only the problem of separation in multinomial logit models but estimates also exist when the number of covariates is large relative to the sample size. Estimates exist even when the MLE does not exist. Estimates can be easily computed with all commonly used statistical packages supporting the fitting procedures with weights. Estimates are compared with the usual MLE and Firth's bias reduction technique in a simulation study and an application.

Simulation-Based Valuation and Counterparty Exposure Estimation of American Options

Valuing American options is a central problem in option pricing since the early-exercise feature is very common among financial or insurance derivatives products. For high-dimensional American options, Monte Carlo simulation is generally regarded as the only viable approach to price them, and this is the focus of our work. We propose a new regression-based Monte Carlo algorithm for pricing American options. This method typically generates an upper bound of the option value. It is computationally efficient and generates accurate price estimates. To improve the convergence rate, we apply a bias reduction technique to the least-squares Monte Carlo estimators of American option value. It works by subtracting a bias approximation from the original option value estimators at each exercise opportunity. The bias approximation is derived using large sample properties of the least-squares regression estimators. The resulting expression is easy to evaluate, and is applicable to any payoff structures and underlying processes. Numerical results show that this technique can significantly reduce the bias. However, it introduces non-negligible computational costs, thus careful treatment is required when it is adopted in practice. Finally, we extend the least-squares Monte Carlo algorithm to estimate the counterparty exposures of American options. The new algorithm is termed optimized least-squares Monte Carlo (OLSM), which is combined with variance reduction techniques, initial state dispersion and multiple bucketing to enhance its performance. The biggest advantage of OLSM is that it avoids nested simulations, allowing for the computation of risk measures on various time horizons under a reasonable computational budget.

Confident estimation for density of a biological population based on line transect sampling

Line transect sampling is a very useful method in survey of wildlife population. Confident interval estimation for density D of a biological population is proposed based on a sequential design. The survey area is occupied by the population whose size is unknown. A stopping rule is proposed by a kernel-based estimator of density function of the perpendicular data at a distance. With this stopping rule, we construct several confidence intervals for D by difference procedures. Some bias reduction techniques are used to modify the confidence intervals. These intervals provide the desired coverage probability as the bandwidth in the stopping rule approaches zero. A simulation study is also given to illustrate the performance of this proposed sequential kernel procedure.