scholarly journals manuscript

2021 ◽  
Author(s):  
Juan Liu ◽  
Liyaling ◽  
Xu Lian ◽  
Chanjing Zheng
Keyword(s):  
Parameter Estimation ◽  
Empirical Study ◽  
Dominance Rank ◽  
Joint Estimation ◽  
Personality Test ◽  
Simulation Studies ◽  
Cognitive Assessments ◽  
Irt Model ◽  
Industrial Organizational Psychology ◽  
Response Biases

Forced choice (FC) is one of the most used forms measurement for non-cognitive assessments, which can effectively resist faking and some other response biases compared to the Likert-types scales, and has been a popular topic in the field of industrial organizational psychology in recent years. Inspired by Lee et al., (2019) study, the present study proposed a 2PL-RANK model as a variant of the GGUM-RANK for fitting dominance RANK items. To improve the efficiency of parameter estimation, the authors apply the stEM algorithm to the 2PL-RANK model, which greatly improves the efficiency of parameter estimation in joint estimation. What’s more, we derived information functions for this model based on the logic of Joo et al., (2018). Then, simulation studies were conducted to examined the recovery of model's parameters with RANK triplet responses, which manipulated four factors, with sample size, the number of dimensions, the number of blocks measured in each dimension, and the correlation between dimensions. Results show that the 2PL-RANK model performed well in estimating item and trait parameters. Finally, the utility of 2PL-RANK and Thurstonian IRT model (TIRT) in a 24-dimensional FC personality test was compared. An empirical study was then conducted based on a 24-dimensional FC personality test to illustrate the practical use of the model.

scholarly journals GGUM-RANK Statement and Person Parameter Estimation With Multidimensional Forced Choice Triplets

2018 ◽  
Vol 43 (3) ◽  
pp. 226-240 ◽  
Author(s):  
Philseok Lee ◽  
Seang-Hwane Joo ◽  
Stephen Stark ◽  
Oleksandr S. Chernyshenko
Keyword(s):  
Parameter Estimation ◽  
Forced Choice ◽  
Future Research ◽  
Simulation Studies ◽  
Scoring Methods ◽  
Mcmc Algorithm ◽  
High Stake ◽  
Parameter Recovery ◽  
Irt Model ◽  
Person Parameter

Historically, multidimensional forced choice (MFC) measures have been criticized because conventional scoring methods can lead to ipsativity problems that render scores unsuitable for interindividual comparisons. However, with the recent advent of item response theory (IRT) scoring methods that yield normative information, MFC measures are surging in popularity and becoming important components in high-stake evaluation settings. This article aims to add to burgeoning methodological advances in MFC measurement by focusing on statement and person parameter recovery for the GGUM-RANK (generalized graded unfolding-RANK) IRT model. Markov chain Monte Carlo (MCMC) algorithm was developed for estimating GGUM-RANK statement and person parameters directly from MFC rank responses. In simulation studies, it was examined that how the psychometric properties of statements composing MFC items, test length, and sample size influenced statement and person parameter estimation; and it was explored for the benefits of measurement using MFC triplets relative to pairs. To demonstrate this methodology, an empirical validity study was then conducted using an MFC triplet personality measure. The results and implications of these studies for future research and practice are discussed.

scholarly journals Bayesian parameter estimation in non-stationary semiflexible polymers from ensembles of trajectories

2018 ◽  
Author(s):  
Christopher A Penfold
Keyword(s):  
Parameter Estimation ◽  
Epigenetic Modification ◽  
Single Cells ◽  
Semiflexible Polymers ◽  
Simulation Studies ◽  
Biophysical Parameters ◽  
Heterogeneous Populations ◽  
Bending Modulus ◽  
Polymer Models ◽  
Chromosome Organisation

During the cell-cycle and meiosis, during development, or in response to stress, chromosomes undertake dramatic programs of reorganisation, which can result in major changes to genomic architecture, as well as local changes to chromatin structure via chromatin remodelling and epigenetic modification. The biophysical properties of the genome may therefore vary significantly over time, from region to region, and from cell to cell. Semifleixble polymer models are frequently used to decipher the spatial and temporal aspects of chromosome organisation. Such models allow for parameter estimation from experimental observations (Bystricky et al., 2004, Ding et al., 2006, Koszul et al., 2008, Arbona et al., 2017), and so provide a concise quantification of the state of the system in terms of meaningful biophysical parameters, such as the compaction factor and bending-modulus. Simulation studies using appropriately parameterised models may also provide novel insights, and allow for predictions without confounding pleiotropic effects (Penfold et al., 2012), thus guiding future studies. Most semifleixble polymer models do not explicitly consider the spatial non-stationarity of chromosomes and chromatin. Furthermore, recent advances in chromosome conformation capture (3C)-based allow chromosome organisation to be (indirectly) measured in single cells (Belton et al., 2012, Nagano et al., 2013, 2016). The increasing availability of ensembles of trajectories sampled from potentially heterogeneous populations of cells means it is of interest to develop polymer statistic models that can capture both the spatial nonstationarity of the biophysical parameters, and the statistical relationships that exist within the population. Here we outline a statistical framework for non-stationary semiflexible polymers, and demonstrate how inference can be performed using ensembles of trajectories. For cells belonging to a homogenous population where the biophysical parameters are approximately identical in all cells, a (transformed) Gaussian process prior is assigned to the bending-modulus, and Markov chain Monte Carlo (MCMC) used to infer the posterior distribution of free parameters. For heterogeneous populations of cells, a transformed hierarchical GP (HGP) prior is assigned to the biophysical parameters, which naturally captures the statistical dependency of the parameters that exist across the population. Simulation studies demonstrate the accuracy of the model for homogenous and heterogeneous populations, while applications to yeast chromosome data demonstrates an improved ability to recapitulate trajectories of held out loci compared to related stationary models.

scholarly journals Simultaneous state-parameter estimation of rainfall-induced landslide displacement using data assimilation

2019 ◽  
Author(s):  
Jing Wang ◽  
Guigen Nie ◽  
Shengjun Gao ◽  
Changhu Xue
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Prediction Method ◽  
State Parameter ◽  
Joint Estimation ◽  
Model Parameters ◽  
Displacement Estimation ◽  
Landslide Stability ◽  
Landslide Displacement Prediction ◽  
Using Data

Abstract. Landslide displacement prediction has great practical engineering significance to landslide stability evaluation and early warning. The evolution of landslide is a complex dynamic process, applying classical prediction method will result in significant error. Data assimilation method offers a new way to merge multi-source data with the model. However, data assimilation is still deficient in the ability to meet the demand of dynamic landslide system. In this paper, simultaneous state-parameter estimation (SSPE) using particle filter-based data assimilation is applied to predict displacement of the landslide. Landslide SSPE assimilation strategy can make use of time-series displacements and hydrological information for the joint estimation of landslide displacement and model parameters, which can improve the performance considerably. We select Xishan Village, Sichuan province, China as experiment site to test SSPE assimilation strategy. Based on the comparison of actual monitoring data with prediction values, results strongly suggest the effectiveness and feasibility of SSPE assimilation strategy in short-term landslide displacement estimation.

scholarly journals An adaptive observer design approach for a class of discrete-time nonlinear systems

2018 ◽  
Vol 28 (1) ◽  
pp. 55-67 ◽  
Author(s):  
Krishnan Srinivasarengan ◽  
José Ragot ◽  
Christophe Aubrun ◽  
Didier Maquin
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Linear Systems ◽  
Discrete Time ◽  
Design Procedure ◽  
Observer Design ◽  
Joint Estimation ◽  
Adaptive Observer ◽  
Waste Water Treatment Plant ◽  
Joint State

AbstractWe consider the problem of joint estimation of states and some constant parameters for a class of nonlinear discrete-time systems. This class contains systems that could be transformed into a quasi-LPV (linear parameter varying) polytopic model in the Takagi-Sugeno (T-S) form. Such systems could have unmeasured premise variables, a case usually overlooked in the observer design literature. We assert that, for such systems in discrete-time, the current literature lacks design strategies for joint state and parameter estimation. To this end, we adapt the existing literature on continuous-time linear systems for joint state and time-varying parameter estimation. We first develop the discrete-time version of this result for linear systems. A Lyapunov approach is used to illustrate stability, and bounds for the estimation error are obtained via the bounded real lemma. We use this result to achieve our objective for a design procedure for a class of nonlinear systems with constant parameters. This results in less conservative conditions and a simplified design procedure. A basic waste water treatment plant simulation example is discussed to illustrate the design procedure.

Prediction of Finite Population Proportion When Responses Are Misclassified

2020 ◽  
Author(s):  
Sumanta Adhya ◽  
Surupa Roy ◽  
Tathagata Banerjee
Keyword(s):  
Empirical Study ◽  
Finite Population ◽  
Asymptotic Properties ◽  
Auxiliary Variable ◽  
Binary Response ◽  
Simulation Studies ◽  
Variance Estimator ◽  
Computationally Efficient ◽  
Extensive Simulation ◽  
Population Proportion

Abstract We propose a model-based predictive estimator of the finite population proportion of a misclassified binary response, when information on the auxiliary variable(s) is available for all units in the population. Asymptotic properties of the misclassification-adjusted predictive estimator are also explored. We propose a computationally efficient bootstrap variance estimator that exhibits better performance compared to usual analytical variance estimator. The performance of the proposed estimator is compared with other commonly used design-based estimators through extensive simulation studies. The results are supplemented by an empirical study based on literacy data.

scholarly journals A Tukey’s g-and-h distribution based approach with PSO for degradation reliability modeling

2019 ◽  
Vol 36 (5) ◽  
pp. 1699-1715
Author(s):  
Jinbao Zhang ◽  
Yongqiang Zhao ◽  
Ming Liu ◽  
Lingxian Kong
Keyword(s):  
Parameter Estimation ◽  
Design Methodology ◽  
Failure Time ◽  
Simulation Studies ◽  
Reliability Modeling ◽  
Content Type ◽  
Wide Range ◽  
Gaas Laser ◽  
Laser Devices ◽  
Generalized Distribution

Purpose A generalized distribution with wide range of skewness and elongation will be suitable for the data mining and compatible for the misspecification of the distribution. Hence, the purpose of this paper is to present a distribution-based approach for estimating degradation reliability considering these conditions. Design/methodology/approach Tukey’s g-and-h distribution with the quantile expression is introduced to fit the degradation paths of the population over time. The Newton–Raphson algorithm is used to approximately evaluate the reliability. Simulation verification for parameter estimation with particle swarm optimization (PSO) is carried out. The effectiveness and validity of the proposed approach for degradation reliability is verified by the two-stage verification and the comparison with others’ work. Findings Simulation studies have proved the effectiveness of PSO in the parameter estimation. Two degradation datasets of GaAs laser devices and crack growth are performed by the proposed approach. The results show that it can well match the initial failure time and be more compatible than the normal distribution and the Weibull distribution. Originality/value Tukey’s g-and-h distribution is first proposed to investigate the influence of the tail and the skewness on the degradation reliability. In addition, the parameters of the Tukey’s g-and-h distribution is estimated by PSO with root-mean-square error as the object function.

scholarly journals Multitarget Parameter Estimation of Monopulse Radar Based on RJ-MCMC Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Jian Gong ◽  
Yiduo Guo ◽  
Hui Yuan ◽  
Qun Wan
Keyword(s):  
Parameter Estimation ◽  
Target Location ◽  
Estimation Method ◽  
Observation Time ◽  
Joint Estimation ◽  
Estimation Methods ◽  
Mcmc Algorithm ◽  
Target Number ◽  
Real Time Processing ◽  
Location Parameters

A multiple parameter estimation method based on RJ-MCMC for multiple nondiscernible targets is proposed in this paper. Different from the traditional estimation methods, the proposed method can simultaneously complete the joint estimation of the target number and the target location parameters. More importantly, the method proposed in this chapter is applicable to many situations with different power and nondistinguishable target. The simulation results show that the method proposed in this chapter requires less observation time to obtain similar and even better estimation performance than the ML-MDL method, which is of great significance for real-time processing.

scholarly journals A note on parameter estimation for discretely sampled SPDEs

2019 ◽  
Vol 20 (03) ◽  
pp. 2050016 ◽  
Author(s):  
Igor Cialenco ◽  
Yicong Huang
Keyword(s):  
Parameter Estimation ◽  
Bounded Domain ◽  
Asymptotic Properties ◽  
Mesh Size ◽  
Fixed Time ◽  
Estimation Problem ◽  
Joint Estimation ◽  
Heat Equations ◽  
Whole Space ◽  
Stochastic Heat Equations

We consider a parameter estimation problem for one-dimensional stochastic heat equations, when data is sampled discretely in time or spatial component. We prove that, the real valued parameter next to the Laplacian (the drift), and the constant parameter in front of the noise (the volatility) can be consistently estimated under somewhat surprisingly minimal information. Namely, it is enough to observe the solution at a fixed time and on a discrete spatial grid, or at a fixed space point and at discrete time instances of a finite interval, assuming that the mesh-size goes to zero. The proposed estimators have the same form and asymptotic properties regardless of the nature of the domain –bounded domain or whole space. The derivation of the estimators and the proofs of their asymptotic properties are based on computations of power variations of some relevant stochastic processes. We use elements of Malliavin calculus to establish the asymptotic normality properties in the case of bounded domain. We also discuss the joint estimation problem of the drift and volatility coefficient. We conclude with some numerical experiments that illustrate the obtained theoretical results.

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