Extensions of Multiple-Group Item Response Theory Alignment: Application to Psychiatric Phenotypes in an International Genomics Consortium

Large-scale studies spanning diverse project sites, populations, languages, and measurements are increasingly important to relate psychological to biological variables. National and international consortia already are collecting and executing mega-analyses on aggregated data from individuals, with different measures on each person. In this research, we show that Asparouhov and Muthén’s alignment method can be adapted to align data from disparate item sets and response formats. We argue that with these adaptations, the alignment method is well suited for combining data across multiple sites even when they use different measurement instruments. The approach is illustrated using data from the Whole Genome Sequencing in Psychiatric Disorders consortium and a real-data-based simulation is used to verify accurate parameter recovery. Factor alignment appears to increase precision of measurement and validity of scores with respect to external criteria. The resulting parameter estimates may further inform development of more effective and efficient methods to assess the same constructs in prospectively designed studies.

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Data Pooling in Stochastic Optimization

Management Science ◽

10.1287/mnsc.2020.3933 ◽

2021 ◽

Author(s):

Vishal Gupta ◽

Nathan Kallus

Keyword(s):

Stochastic Optimization ◽

Inventory Management ◽

Large Scale ◽

Optimization Problems ◽

Lot Sizing ◽

A Priori ◽

Real Data ◽

Data Pooling ◽

Combining Data ◽

A Chain

Managing large-scale systems often involves simultaneously solving thousands of unrelated stochastic optimization problems, each with limited data. Intuition suggests that one can decouple these unrelated problems and solve them separately without loss of generality. We propose a novel data-pooling algorithm called Shrunken-SAA that disproves this intuition. In particular, we prove that combining data across problems can outperform decoupling, even when there is no a priori structure linking the problems and data are drawn independently. Our approach does not require strong distributional assumptions and applies to constrained, possibly nonconvex, nonsmooth optimization problems such as vehicle-routing, economic lot-sizing, or facility location. We compare and contrast our results to a similar phenomenon in statistics (Stein’s phenomenon), highlighting unique features that arise in the optimization setting that are not present in estimation. We further prove that, as the number of problems grows large, Shrunken-SAA learns if pooling can improve upon decoupling and the optimal amount to pool, even if the average amount of data per problem is fixed and bounded. Importantly, we highlight a simple intuition based on stability that highlights when and why data pooling offers a benefit, elucidating this perhaps surprising phenomenon. This intuition further suggests that data pooling offers the most benefits when there are many problems, each of which has a small amount of relevant data. Finally, we demonstrate the practical benefits of data pooling using real data from a chain of retail drug stores in the context of inventory management. This paper was accepted by Chung Piaw Teo, Special Issue on Data-Driven Prescriptive Analytics.

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Matrix Normal Cluster-Weighted Models

Journal of Classification ◽

10.1007/s00357-021-09389-2 ◽

2021 ◽

Author(s):

Salvatore D. Tomarchio ◽

Paul D. McNicholas ◽

Antonio Punzo

Keyword(s):

Simulated Data ◽

Real Data ◽

Information Criterion ◽

Parameter Estimates ◽

Finite Mixtures ◽

Parameter Recovery ◽

Model Based Clustering ◽

Data Points ◽

Matrix Normal ◽

Mixtures Of Regressions

AbstractFinite mixtures of regressions with fixed covariates are a commonly used model-based clustering methodology to deal with regression data. However, they assume assignment independence, i.e., the allocation of data points to the clusters is made independently of the distribution of the covariates. To take into account the latter aspect, finite mixtures of regressions with random covariates, also known as cluster-weighted models (CWMs), have been proposed in the univariate and multivariate literature. In this paper, the CWM is extended to matrix data, e.g., those data where a set of variables are simultaneously observed at different time points or locations. Specifically, the cluster-specific marginal distribution of the covariates and the cluster-specific conditional distribution of the responses given the covariates are assumed to be matrix normal. Maximum likelihood parameter estimates are derived using an expectation-conditional maximization algorithm. Parameter recovery, classification assessment, and the capability of the Bayesian information criterion to detect the underlying groups are investigated using simulated data. Finally, two real data applications concerning educational indicators and the Italian non-life insurance market are presented.

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IRT and MIRT Models for Item Parameter Estimation With Multidimensional Multistage Tests

Journal of Educational and Behavioral Statistics ◽

10.3102/1076998619881790 ◽

2019 ◽

Vol 45 (4) ◽

pp. 383-402

Author(s):

Paul A. Jewsbury ◽

Peter W. van Rijn

Keyword(s):

Item Response Theory ◽

Item Response ◽

Latent Variable ◽

Large Scale ◽

Real Data ◽

Item Parameter ◽

Practical Reasons ◽

Parameter Estimates ◽

Response Theory ◽

Item Parameter Estimates

In large-scale educational assessment data consistent with a simple-structure multidimensional item response theory (MIRT) model, where every item measures only one latent variable, separate unidimensional item response theory (UIRT) models for each latent variable are often calibrated for practical reasons. While this approach can be valid for data from a linear test, unacceptable item parameter estimates are obtained when data arise from a multistage test (MST). We explore this situation from a missing data perspective and show mathematically that MST data will be problematic for calibrating multiple UIRT models but not MIRT models. This occurs because some items that were used in the routing decision are excluded from the separate UIRT models, due to measuring a different latent variable. Both simulated and real data from the National Assessment of Educational Progress are used to further confirm and explore the unacceptable item parameter estimates. The theoretical and empirical results confirm that only MIRT models are valid for item calibration of multidimensional MST data.

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Kumaraswamy Generalized Power Lomax Distributionand Its Applications

Stats ◽

10.3390/stats4010003 ◽

2021 ◽

Vol 4 (1) ◽

pp. 28-45

Author(s):

Vasili B.V. Nagarjuna ◽

R. Vishnu Vardhan ◽

Christophe Chesneau

Keyword(s):

Hazard Rate ◽

Real Data ◽

Rate Function ◽

Maximum Likelihood Estimates ◽

Parameter Estimates ◽

Parameter Distribution ◽

Data Sets ◽

Lomax Distribution ◽

Entropy Measures ◽

Modeling Behavior

In this paper, a new five-parameter distribution is proposed using the functionalities of the Kumaraswamy generalized family of distributions and the features of the power Lomax distribution. It is named as Kumaraswamy generalized power Lomax distribution. In a first approach, we derive its main probability and reliability functions, with a visualization of its modeling behavior by considering different parameter combinations. As prime quality, the corresponding hazard rate function is very flexible; it possesses decreasing, increasing and inverted (upside-down) bathtub shapes. Also, decreasing-increasing-decreasing shapes are nicely observed. Some important characteristics of the Kumaraswamy generalized power Lomax distribution are derived, including moments, entropy measures and order statistics. The second approach is statistical. The maximum likelihood estimates of the parameters are described and a brief simulation study shows their effectiveness. Two real data sets are taken to show how the proposed distribution can be applied concretely; parameter estimates are obtained and fitting comparisons are performed with other well-established Lomax based distributions. The Kumaraswamy generalized power Lomax distribution turns out to be best by capturing fine details in the structure of the data considered.

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Constructing Large-Scale Genetic Maps Using an Evolutionary Strategy Algorithm

Genetics ◽

10.1093/genetics/165.4.2269 ◽

2003 ◽

Vol 165 (4) ◽

pp. 2269-2282

Author(s):

D Mester ◽

Y Ronin ◽

D Minkov ◽

E Nevo ◽

A Korol

Keyword(s):

Discrete Optimization ◽

High Performance ◽

Large Scale ◽

Simulated Data ◽

Real Data ◽

Genetic Maps ◽

Chromosome 1 ◽

Evolutionary Strategy ◽

Group A ◽

The One

Abstract This article is devoted to the problem of ordering in linkage groups with many dozens or even hundreds of markers. The ordering problem belongs to the field of discrete optimization on a set of all possible orders, amounting to n!/2 for n loci; hence it is considered an NP-hard problem. Several authors attempted to employ the methods developed in the well-known traveling salesman problem (TSP) for multilocus ordering, using the assumption that for a set of linked loci the true order will be the one that minimizes the total length of the linkage group. A novel, fast, and reliable algorithm developed for the TSP and based on evolution-strategy discrete optimization was applied in this study for multilocus ordering on the basis of pairwise recombination frequencies. The quality of derived maps under various complications (dominant vs. codominant markers, marker misclassification, negative and positive interference, and missing data) was analyzed using simulated data with ∼50-400 markers. High performance of the employed algorithm allows systematic treatment of the problem of verification of the obtained multilocus orders on the basis of computing-intensive bootstrap and/or jackknife approaches for detecting and removing questionable marker scores, thereby stabilizing the resulting maps. Parallel calculation technology can easily be adopted for further acceleration of the proposed algorithm. Real data analysis (on maize chromosome 1 with 230 markers) is provided to illustrate the proposed methodology.

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Meta-Descent for Online, Continual Prediction

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33013943 ◽

2019 ◽

Vol 33 ◽

pp. 3943-3950

Author(s):

Andrew Jacobsen ◽

Matthew Schlegel ◽

Cameron Linke ◽

Thomas Degris ◽

Adam White ◽

...

Keyword(s):

Gradient Descent ◽

Large Scale ◽

Time Series Prediction ◽

Real Data ◽

Second Order ◽

Stochastic Gradient Descent ◽

Step Size ◽

Vector Approximation ◽

Prediction Problems ◽

Stationary Problems

This paper investigates different vector step-size adaptation approaches for non-stationary online, continual prediction problems. Vanilla stochastic gradient descent can be considerably improved by scaling the update with a vector of appropriately chosen step-sizes. Many methods, including AdaGrad, RMSProp, and AMSGrad, keep statistics about the learning process to approximate a second order update—a vector approximation of the inverse Hessian. Another family of approaches use meta-gradient descent to adapt the stepsize parameters to minimize prediction error. These metadescent strategies are promising for non-stationary problems, but have not been as extensively explored as quasi-second order methods. We first derive a general, incremental metadescent algorithm, called AdaGain, designed to be applicable to a much broader range of algorithms, including those with semi-gradient updates or even those with accelerations, such as RMSProp. We provide an empirical comparison of methods from both families. We conclude that methods from both families can perform well, but in non-stationary prediction problems the meta-descent methods exhibit advantages. Our method is particularly robust across several prediction problems, and is competitive with the state-of-the-art method on a large-scale, time-series prediction problem on real data from a mobile robot.

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Self-Adaptive K-Means Based on a Covering Algorithm

Complexity ◽

10.1155/2018/7698274 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Yiwen Zhang ◽

Yuanyuan Zhou ◽

Xing Guo ◽

Jintao Wu ◽

Qiang He ◽

...

Keyword(s):

Large Scale ◽

Clustering Algorithm ◽

Real Data ◽

Second Phase ◽

Data Sets ◽

Number Of Clusters ◽

Large Scale Data ◽

Long Time ◽

Two Phases ◽

Selection Of

The K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time. However, the value of the clustering number k in the K-means algorithm is not always easy to be determined, and the selection of the initial centers is vulnerable to outliers. This paper proposes an improved K-means clustering algorithm called the covering K-means algorithm (C-K-means). The C-K-means algorithm can not only acquire efficient and accurate clustering results but also self-adaptively provide a reasonable numbers of clusters based on the data features. It includes two phases: the initialization of the covering algorithm (CA) and the Lloyd iteration of the K-means. The first phase executes the CA. CA self-organizes and recognizes the number of clusters k based on the similarities in the data, and it requires neither the number of clusters to be prespecified nor the initial centers to be manually selected. Therefore, it has a “blind” feature, that is, k is not preselected. The second phase performs the Lloyd iteration based on the results of the first phase. The C-K-means algorithm combines the advantages of CA and K-means. Experiments are carried out on the Spark platform, and the results verify the good scalability of the C-K-means algorithm. This algorithm can effectively solve the problem of large-scale data clustering. Extensive experiments on real data sets show that the accuracy and efficiency of the C-K-means algorithm outperforms the existing algorithms under both sequential and parallel conditions.

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Reduced order system identification for UAVs

The Aeronautical Journal ◽

10.1017/s0001924000004279 ◽

2015 ◽

Vol 119 (1218) ◽

pp. 961-980 ◽

Cited By ~ 3

Author(s):

P-D. Jameson ◽

A. K. Cooke

Keyword(s):

System Identification ◽

Equations Of Motion ◽

Real Data ◽

Flight Test ◽

Rate Of Change ◽

Order System ◽

Parameter Estimates ◽

Reduced Order Models ◽

Test Scenario ◽

Reduced Order

Abstract Reduced order models representing the dynamic behaviour of symmetric aircraft are well known and can be easily derived from the standard equations of motion. In flight testing, accurate measurements of the dependent variables which describe the linearised reduced order models for a particular flight condition are vital for successful system identification. However, not all the desired measurements such as the rate of change in vertical velocity (Ẇ) can be accurately measured in practice. In order to determine such variables two possible solutions exist: reconstruction or differentiation. This paper addresses the effect of both methods on the reliability of the parameter estimates. The methods are used in the estimation of the aerodynamic derivatives for the Aerosonde UAV from a recreated flight test scenario in Simulink. Subsequently, the methods are then applied and compared using real data obtained from flight tests of the Cranfield University Jetstream 31 (G-NFLA) research aircraft.

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Microwave Dielectric Property Retrieval from Open-Ended Coaxial Probe Response with Deep Learning

10.36227/techrxiv.16992394.v1 ◽

2021 ◽

Author(s):

Cemanur Aydinalp ◽

Sulayman Joof ◽

Mehmet Nuri Akinci ◽

Ibrahim Akduman ◽

Tuba Yilmaz

Keyword(s):

Deep Learning ◽

Dielectric Property ◽

Large Scale ◽

Real Data ◽

Learning Model ◽

Data Generation ◽

Retrieval Technique ◽

Design Flexibility ◽

A New Technique ◽

Deep Learning Model

In the manuscript, we propose a new technique for determination of Debye parameters, representing the dielectric properties of materials, from the reflection coefficient response of open-ended coaxial probes. The method retrieves the Debye parameters using a deep learning model designed through utilization of numerically generated data. Unlike real data, using synthetically generated input and output data for training purposes provides representation of a wide variety of materials with rapid data generation. Furthermore, the proposed method provides design flexibility and can be applied to any desired probe with intended dimensions and material. Next, we experimentally verified the designed deep learning model using measured reflection coefficients when the probe was terminated with five different standard liquids, four mixtures,and a gel-like material.and compared the results with the literature. Obtained mean percent relative error was ranging from 1.21±0.06 to 10.89±0.08. Our work also presents a large-scale statistical verification of the proposed dielectric property retrieval technique.

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Rapid Deployment and Application Exploration of the Mobile Shelter Laboratories Under T He Outbreak of COVID-19 Epidemic

10.21203/rs.3.rs-968627/v1 ◽

2021 ◽

Author(s):

Yi Luan ◽

Rui Ding ◽

Wenshen Gu ◽

Xiaofan Zhang ◽

Xinliang Chen ◽

...

Keyword(s):

Large Scale ◽

Mainland China ◽

Reference Value ◽

Guangdong Province ◽

Real Data ◽

Practical Experience ◽

Rapid Expansion ◽

Nucleic Acid Detection ◽

Short Period ◽

Detection Capabilities

Abstract Since the end of 2019, the COVID-19 epidemic has swept the world. With the widespread spread of the COVID-19 and the continuous emergence of mutated strains, the situation for the prevention and control of the COVID-19 epidemic remains severe. On May 21, 2021, Guangzhou City, Guangdong Province, notified the discovery of a new locally confirmed case. Guangzhou became the first city in mainland China to compete with the delta mutant strain. As a local hospital with strong nucleic acid detection capabilities, Sun Yat-sen University Sun Yat-sen Memorial Hospital took the lead in launching the construction and deployment of the Mobile Shelter Laboratories and large-scale screening work in Foshan and Zhanjiang, Guangdong Province. Through summarizing "practical" experience, observation and comparison data analysis, we use real data to verify a feasible solution for rapid expansion of detection capabilities in a short period of time. We hope that these experiences will have certain reference value for other countries or regions, especially the underdeveloped areas of medical and health care.

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