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Computing maximum likelihood estimates in linear structural equation models is generally a difficult problem. The critical equations are usually non-linear and have numerous solutions, even for purely…

Computing maximum likelihood estimates in linear structural equation models is generally a difficult problem. The critical equations are usually non-linear and have numerous solutions, even for purely observational data. The block-coordinate descent (BCD) algorithm proposed by Drton et al. (2019)[1] is an efficient way to solve the optimization problem by decomposing it into a series of sub-problems with closed-form solutions, and which works with observational data. In this work, we describe the general problem of a BCD-type scheme for computing maximum likelihood estimates in linear structural equation models without hidden variables, integrating multiple observational and interventional environments. With interventional data, the degrees of both the original likelihood equations and the block-coordinate update equations could increase greatly. We study special setups in which the block optimization subproblems have a degree of at most 2 and provide closed-form solutions in these cases. Additionally, we discuss the potential applications of the model and algorithm to health and well-being data.

The data come from cross-sectional surveys conducted on representative samples of the non-institutionalised adult population in the Czech Republic in 2017, 2019 and 2022. The data include basic demogr…

The data come from cross-sectional surveys conducted on representative samples of the non-institutionalised adult population in the Czech Republic in 2017, 2019 and 2022. The data include basic demographic data and data from four questionnaires.  Data on mental health problems were assessed using the Mini International Neuropsychiatric Interview (M.I.N.I.) in 2017 and 2022. The Self-identification of Mental Illness (SELF-I) scale was used to assess self-identification as having a mental illness in these years. Stigma associated with mental health was assessed using the Reported and Intended Behaviour Scale (RIBS) and the Community Attitudes towards Mental Illness (CAMI) scale in 2019 and 2022.   Data pocházejí z průřezových šetření provedených na reprezentativních vzorcích neinstitucionalizované dospělé populace v České republice v letech 2017, 2019 a 2022. Data zahrnují základní demografické údaje a údaje ze čtyř dotazníků. Údaje o problémech v oblasti duševního zdraví byly hodnoceny pomocí Mini mezinárodního neuropsychiatrického rozhovoru (M.I.N.I.) v letech 2017 a 2022. K posouzení sebeidentifikace jako osoby s duševním onemocněním byla v těchto letech použita škála Self-identification of Mental Illness (SELF-I). Stigmatizace spojená s duševním zdravím byla hodnocena pomocí škály Reported and Intended Behaviour Scale (RIBS) a škály Community Attitudes towards Mental Illness (CAMI) v letech 2019 a 2022.

This paper examines the approximation capabilities of lattice integral transforms and their compositions in reconstructing lattice-valued functions. By introducing an integral kernel 𝑄 on the function…

This paper examines the approximation capabilities of lattice integral transforms and their compositions in reconstructing lattice-valued functions. By introducing an integral kernel 𝑄 on the function domain, we define the concept of a 𝑄-inverse integral kernel, which generalizes the traditional inverse kernel defined as a transposed integral kernel. Leveraging these 𝑄-inverses, we establish upper and lower bounds for a transformed version of the original function induced by the integral kernel 𝑄. The quality of approximation is analyzed using a lattice-based modulus of continuity, specifically designed for functions valued in complete residuated lattices. Additionally, under specific conditions, we demonstrate that the approximation quality for extensional functions with respect to the kernel 𝑄 can be estimated through the integral of the square of 𝑄, and in certain cases, these extensional functions can be perfectly reconstructed. The theoretical findings, illustrated through examples, provide a strong foundation for further theoretical advancement and practical applications.

A new concept, termed virtual neural networks, is introduced, where the count of trainable parameters is kept constant, and scalability is attained purely through computational resources. This concept…

A new concept, termed virtual neural networks, is introduced, where the count of trainable parameters is kept constant, and scalability is attained purely through computational resources. This concept is an abstract framework that can be realized using any standard convolutional neural network. It merges siamese neural networks with a deep ensemble technique by generating numerous virtual models that share weights derived from a small set of physical models. The ensemble comprises up to hundreds of trained models simultaneously. All virtual networks take the same input, and their interconnected structure induces an internal distortion that boosts the entire ensemble robustness. The accuracy of the ensemble improves as the number of virtual networks increases, without changing the capacity. Virtual neural networks outperform larger capacity models, typical deep ensembles, and contemporary approaches like SWA and Masksembles. Additionally, the highest performing individual model from the ensemble surpasses other models trained individually, even those with a greater number of parameters.

Inter-rater reliability (IRR) is one of the commonly used tools for assessing the quality of ratings from multiple raters. However, applicant selection procedures based on ratings from multiple raters…

Inter-rater reliability (IRR) is one of the commonly used tools for assessing the quality of ratings from multiple raters. However, applicant selection procedures based on ratings from multiple raters usually result in a binary outcome - the applicant is either selected or not. This final outcome is not considered in IRR, which instead focuses on the ratings of the individual subjects or objects. We outline the connection between the ratings' measurement model (used for IRR) and a binary classification framework. We develop a simple way of approximating the probability of correctly selecting the best applicants which allows us to compute error probabilities of the selection procedure (i.e., false positive and false negative rate) or their lower bounds. We draw connections between the IRR and the binary classification metrics, showing that binary classification metrics depend solely on the IRR coefficient and proportion of selected applicants. We assess the performance of the approximation in a simulation study and apply it in an example comparing the reliability of multiple grant peer review selection procedures. We also discuss other possible uses of the explored connections in other contexts, such as educational testing, psychological assessment, and health-related measurement, and implement the computations in the R package IRR2FPR.

BACKGROUND: Stroke often impairs upper extremity motor function, with recovery in the sub-acute phase being crucial for regaining independence. This study examines changes in isometric muscle strength…

BACKGROUND: Stroke often impairs upper extremity motor function, with recovery in the sub-acute phase being crucial for regaining independence. This study examines changes in isometric muscle strength, dexterity, and self-care independence during this period, and evaluates the effects of a comprehensive intensive rehabilitation (COMIRESTROKE). METHODS: Individuals in sub-acute stroke recovery and age- and sex-matched controls were assessed for pre- and post-rehabilitation differences in primary outcomes (grip/pinch strength, Nine Hole Peg Test [NHPT], Action Research Arm Test [ARAT]). COMIRESTROKE’s effects on primary and secondary outcomes (National Institute of Health Stroke Scale [NIHSS], Modified Rankin Scale [MRS], Functional Independence Measure [FIM]) were evaluated. Outcomes were analyzed for dominant and non-dominant limbs, both regardless of impairment and with a focus on impaired limbs. RESULTS: Fifty-two individuals with stroke (NIHSS 7.51 ± 5.71, age 70.25 ± 12.66 years, 21.36 ± 12.06 days post-stroke) and forty-six controls participated. At baseline, individuals with stroke showed significantly lower strength (dominant grip, key pinch, tip-tip pinch, padj < 0.05), higher NHPT scores (padj < 0.05), and lower ARAT scores (padj < 0.001). COMIRESTROKE led to improvements in dominant key pinch, non-dominant tip-tip pinch, NHPT, and both dominant and non-dominant ARAT (padj < 0.05). Notably, non-dominant key pinch improved significantly when considering only impaired hands. Pre- and post-test differences between groups were significant only for ARAT (both limbs), even after adjustment (padj < 0.05). All secondary outcomes (NIHSS, MRS, FIM) showed significant improvement post-COMIRESTROKE (padj < 0.001). CONCLUSION: Individuals with stroke exhibit reduced muscle strength and dexterity, impairing independence. However, comprehensive intensive rehabilitation significantly improves these functions. Data are available from the corresponding author upon request and are part of a sub-study of NCT05323916.

Výroční Mezinárodní konferenci Psychometrické společnosti (International Meeting of Psychometric Society, IMPS 2024) ve dnech 15.−19. července 2024 hostil &Uac…

Výroční Mezinárodní konferenci Psychometrické společnosti (International Meeting of Psychometric Society, IMPS 2024) ve dnech 15.−19. července 2024 hostil Ústav informatiky Akademie věd České republiky (AV ČR) ve spolupráci s Pedagogickou fakultou UK (PedF UK) a Vysokou školou ekonomickou (VŠE) v Praze. V rámci Ústavu informatiky AV ČR konferenci pořádala Skupina výpočetní psychometrie, která se zabývá výpočetními aspekty, vývojem a aplikací statistických metod pro analýzu měření v psychologii, vzdělávání a dalších sociálních vědách. Na VŠE organizaci podpořili členové fakulty informatiky a statistiky. Z PedF UK se zapojili členové Ústavu výzkumu a rozvoje vzdělávání, který se zaobírá mj. analýzou dat z velkých mezinárodních studií.The annual International Meeting of the Psychometric Society (IMPS 2024), held from July 15–19, 2024, was hosted by the Institute of Computer Science of the Czech Academy of Sciences (CAS) in cooperation with the Faculty of Education at Charles University (PedF UK) and the University of Economics (VŠE) in Prague. Within the Institute of Computer Science at CAS, the conference was organized by the Computational Psychometrics Group, which focuses on the computational aspects, development, and application of statistical methods for measurement analysis in psychology, education, and other social sciences. The Faculty of Informatics and Statistics at the Prague University of Economics and Business also provided organizational support. From the Faculty of Education at Charles University, members of the Institute for Research and Development in Education, which specializes in analyzing data from large international studies, participated in the event organization.

This article explores innovations for parameter estimation in generalized linear and nonlinear models, which may be used in item response modeling to account for guessing/pretending or slipping/dissim…

This article explores innovations for parameter estimation in generalized linear and nonlinear models, which may be used in item response modeling to account for guessing/pretending or slipping/dissimulation and for the effect of covariates. We introduce a new implementation of the EM algorithm and propose a new algorithm based on the parametrized link function. The two novel iterative algorithms are compared to existing methods in a simulation study. Additionally, the study examines software implementation, including the specification of initial values for numerical algorithms and asymptotic properties with an estimation of standard errors. Overall, the newly proposed algorithm based on the parametrized link function outperforms other procedures, especially for small sample sizes. Moreover, the newly implemented EM algorithm provides additional information regarding respondents’ inclination to guess or pretend and slip or dissimulate when answering the item. The study also discusses applications of the methods in the context of the detection of differential item functioning and addresses the measurement error. Methods are offered in the difNLR package and in the interactive application of the ShinyItemAnalysis package; demonstration is provided using real data from psychological and educational assessments.