Wald Test Research Articles

Effect of external and domestic Public debt on Private investment (A VAR Model analysis)

The objective of this paper is to examine the relationship between private investment and public external and domestic debt for the period of 1980 to 2021. In this study, we checked the unit-root test by using Augmented Dickey-Fuller (ADF) test and Pillips-Perron (PP) test. Moreover, the result of Johansen test states that there is no long-run cointegration between variables. Therefore, this study used VAR-Model for analysis. Furthermore, different type of tests has been applied such as, Shapiro-Wilk W test, Breusch-Pagan/ Cook-Weisberg Test, Breusch-Godfrey LM test, Impulse response analysis and Ramsey Reset test. The estimated result shows a negative and significant relationship between private investment with credits to the private investments by banks and public external debt. However, there is a positive and significant association between private investment with domestic public debt. The Granger Causality Wald test confirms that the most of the previous value of one variable helps to predict the future value of other variables. Hence, it is concluded that the government should revisit their external debt policy to support the country infrastructure, macroeconomic balance and for other public expenditure because this increasing public debt is detrimental to the private investment.

Theranostics of uterine leiomyoma: present and future

Research objectives: to develop an algorithm for predicting the growth of uterine fibrodis, taking into account the state of epigenetic regulation.Materials and methods. The study was conducted on the basis of clinical divisions of the Department of Obstetrics and Gynecology in 2018–2021. 28 patients with uterine fibroids were examined. Expression of miRNA-29b and miRNA-146a in tumor tissue was determined in all women using real-time PCR. Obtained data were analyzed using the statistical program Statistica 10.0 (StatSoft Inc., USA).Results. The average age of the patients was 39.3 ± 1.0 years. The average number of nodes was 2.7 ± 0.4. The expression levels of microRNA-29b were most frequently determined in the range of 2–7 units and microRNA-146a in the range of 30–67 units in most of the examined tissue samples. The expression of miRNA-29b has the greatest significance for the growth forecast (Wald statistic). According to the logistic regression equation the prognostic factors are patient’s age, estradiol and progesterone level in the I and II phases of the menstrual cycle, diameter of the largest node, expression of miRNA-29b and miRNA-146a.Conclusions. This study shows that the use of miRNA expression profile as an additional marker in the diagnosis and treatment of uterine fibroids is promising and requires more detailed research. Further study of the correlation of clinical and paraclinical parameters can make it possible to predict the course of uterine fibroids, and therefore to apply an effective personalized treatment plan. Theranostics is a new approach to the diagnosis and treatment of patients, based on the uniqueness of each person in order to choose the optimal treatment strategy against the background of the molecular genetic technologies, in particular to determine epigenetic changes in hyperproliferative diseases. The study of miRNA expression may find its place in the theranostics of uterine fibroids in the future.

Predictive Value of Pre-Treatment MRI Radiomics for Distant Brain Metastases Following Stereotactic Radiosurgery/Radiotherapy

Local intracranial therapy for brain metastases (BM) has taken on particular importance as survival among metastatic patients improves. However, the development of distant BMs (DBMs) outside the treated area remains a stubborn problem for which canonical clinical features (age, histology, ECOG PS) have limited predictive capability. In this study, we hypothesized that MRI-based "radiomic" features (sub-visual cues extracted from diagnostic images) can accurately predict the time-to-DBM development (TTDD) on a retrospectively curated dataset of patients treated with stereotactic radiosurgery/radiotherapy (SRS/SRT). We queried our treatment planning system for patients treated with brain SRS/SRT between 2014 and 2021, and curated the incidence/timing of DBMs manually. Pre-RT MRI sequences (T1 pre, T1 post, T2, and FLAIR) and planning data were obtained for each patient. MRI and CT simulations were co-registered using affine transformations, and regions of interest (ROIs) were identified based on contoured structures (GTV) and discrete isodose ranges (0-25%, 25-50%, 50-75%, 75%+). Radiomic features were extracted from these ROIs, and clinical features (ECOG PS, tumor volume, age) were recorded for baseline comparison. Features were selected using Wald test scores from univariate Cox proportional hazard (CPH) models. Multivariate CPH models were then trained to predict TTDD using combinations of selected features. Predictive capability was evaluated using concordance index (c-index) values. A radiomic risk score (RRS) was created to discriminate patients with low and high-risk for DBMs, and evaluated using a log-rank test. A total of 105 patients were selected with a median follow up of 356 days. 53 patients developed DBMs (median time 118 days). Radiomic CPH models achieved a c-index of 0.63 compared to clinical baseline of 0.49. The combination of radiomic and clinical features achieved the highest c-index of 0.69. Overall, radiomic features with and without clinical features were able to stratify patients into low and high-risk groups with statistically significant differences in TTDD development (see Table 1). Clinical features alone were not significant. The most predictive radiomic features were identified within the T1 pre-contrast MRI from the 50-75% isodose regions, followed by T2 FLAIR/GTV and T2/GTV combinations. Radiomic features from routine MR scans were more predictive of TTDD than baseline clinical features. The contribution from the 50-75% isodose region suggests importance within the peritumoral environment in addition to the tumor itself.

Democracy, green energy, trade, and environmental progress in South Asia: Advanced quantile regression perspective

Unquestionably, the industrial revolution of the twenty-first century contributes to global warming. Excessive amounts of carbon emissions into the atmosphere are responsible for global warming. Therefore, this research aims to assess the impact of GDP, green energy consumption, population, trade openness, and democracy on CO2 emissions in four selected South Asian countries from 1990 to 2019. This research also attempts to evaluate the EKC hypothesis in terms of economic growth (GDP2). The unit root of panel data and cointegration tests are executed in this study as a prelude to the regression analysis. Quantile regression for panel data, which (Powell, 2016) devised to deal with the fixed effect problem, is used in this study, and (Powell, 2016) empirical findings are the main focus. The estimated coefficient of GDP is positively significant, demonstrating that economic activity increases the burning of fossil fuels and upsurges atmospheric CO2 emissions. After attaining economic development, the reversed U-shaped EKC theory is valid for four selected South Asian countries. Economic development encourages these countries to use green technology, which helps mitigate CO2 emissions. The research, however, reveals that green energy is to blame for CO2 emissions. Burning biomass releases carbon dioxide that negatively impacts the quality of the environment. The study confirms that human activities are the leading contributor to environmental deterioration. Population growth has a worsening effect on the environment. The association between population and CO2 emissions is positively significant. The estimated coefficient of trade openness is positive, which increases CO2 emissions significantly. The estimated coefficient of democracy is quite negative. Therefore, this study suggests prioritizing democracy to reduce CO2 emissions. Citizens who live in democracies are better informed, more organized, and able to protest, all of which contribute to increased government responsiveness to environmental preservation. The results of the Wald test support the differential effects at various quantiles. The Dumitrescu-Hurlin (2012) panel causality tests are also used in this analysis to check causality between variables. Based on the findings, this research makes many policy suggestions for lowering carbon emissions.

Do monetary policy credibility and disagreements in inflation and interest rate expectations affect business confidence? Evidence from an inflation targeting developing country

PurposeBusiness confidence is crucial to firm decisions, but it is deeply related to professional forecasters' expectations. Since Brazil is an important inflation targeting country, this paper investigates whether monetary policy credibility and disagreements in inflation and interest rate expectations relate to business confidence in Brazil. The study considers the aggregate business confidence index and the business confidence indexes for 11 industrial sectors in Brazil.Design/methodology/approachThe authors run ordinary least squares and generalized method of moments regressions to assess the direct effects of disagreements in expectation and monetary policy credibility on business confidence. The authors also make use of Wald test of parameter equality to observe whether there are “offsetting effects” of monetary credibility in mitigating the effects of both disagreements in expectations on business confidence. Besides, the authors run quantile regressions to analyze the effect of the main explanatory variables of interest on business confidence in contexts where business confidence is low (pessimistic) or high (optimistic).FindingsDisagreements in inflation expectations reduce business confidence, monetary policy credibility improves business confidence and credibility mitigates the adverse effects of disagreements in expectations on business confidence. The sectors most sensitive to monetary policy credibility are Rubber, Motor Vehicles, Metallurgy, Metal Products and Cellulose. The findings also suggest the effect of disagreement in inflation expectations on business confidence decreases as confidence increases, and the effect of monetary policy credibility on business confidence increases as entrepreneurs are more optimistic.Originality/valueWhile there is evidence that monetary policy credibility is beneficial to the economy, there are no studies on the effects of disagreements in inflation and interest rate expectations on business confidence (at the aggregate and sectoral levels). Besides, there are no studies that have investigated whether monetary policy credibility can mitigate the effects of disagreements in inflation and interest rate expectations on business confidence (at the aggregate and sectoral levels). Therefore, there are gaps to be filled in the literature addressing business confidence, monetary policy credibility and disagreements in expectations. These issues are particularly important to inflation targeting developing countries.

How do you feel reflecting on your teaching practice?

Reflecting on teaching situations is important for student teachers’ professionalization and can be understood as a learning opportunity. However, despite being important for their professional development, only little is known about the interindividual psychological processes that occur when student teachers reflect on their teaching. The present study examines 146 student teachers’ teaching-related self-efficacy beliefs at two timepoints (at the beginning and end of one semester) and their emotion word use in 239 written reflections on differently perceived teaching situations (135 mastery and 104 challenging) using Wald tests and path modelling. First, our results show that student teachers use more positive than negative emotion words in their written reflections. Second, examining relations between situation and emotions, we show that emotion word use differs significantly between teaching situations. Further, higher negative emotion word use in reflections on mastery experiences (positive teaching situations) is associated with lower self-efficacy at the end of one semester. Thus, it is important to focus particularly on the challenges that student teachers experience during teaching when aiming to promote their self-efficacy beliefs.

Linear mixed models with two longitudinal factors in the study of sugarcane dry root mass

There are agronomic experiments where measurements of a response variable are carried out over more than one longitudinal factor, for example, at different depths over time. These observations, made systematically in each experimental unit, can be correlated and might have heterogeneous variances at the different levels of the longitudinal factor. It was possible to model this correlation between repeated measures and the heterogeneity of variances by using mixed models. Thus, it was necessary to adapt some covariance structures that are common in experiments with only one longitudinal factor. The objective of this study was to use the class of linear mixed models to study sugarcane root dry mass. The experiment was the randomized complete blocks design and the parcels received four nitrogen doses. Repeated measurements were made over two longitudinal factors, one being qualitative ordinal (depths) and one being quantitative (distances from the planting line). It was possible to select a parsimonious covariancestructure and another one to explain the average behavior of the responses through likelihood ratio tests, Wald tests, and using the AIC and BIC information criteria. The adjustment of the selected model was verified by using residual diagnostics graphs.

Inter-reader agreement of breast magnetic resonance imaging and contrast-enhanced mammography in breast cancer diagnosis: a multi-reader retrospective study.

Breast magnetic resonance imaging (MRI) and contrast-enhanced mammography (CEM) are nowadays used in breast imaging but studies about their inter-reader agreement are lacking. Therefore, we compared the inter-reader agreement of CEM and MRI in breast cancer diagnosis in the same patients. Breast MRI and CEM exams performed in a single center (09/2020-09/2021) for an IRB-approved study were retrospectively and independently evaluated by four radiologists of two different centers with different levels of experience who were blinded to the clinical and other imaging data. The reference standard was the histological diagnosis or at least 1-year negative imaging follow-up. Inter-reader agreement was examined using Cohen's and Fleiss' kappa (κ) statistics and compared with the Wald test. Of the 750 patients, 395 met inclusion criteria (44.5 ± 14 years old), with 752 breasts available for CEM and MRI. Overall agreement was moderate (κ = 0.60) for MRI and substantial (κ = 0.74) for CEM. For expert readers, the agreement was substantial (κ = 0.77) for MRI and almost perfect (κ = 0.82) for CEM; for non-expert readers was fair (κ = 0.39); and for MRI and moderate (κ = 0.57) for CEM. Pairwise agreement between expert readers and non-expert readers was moderate (κ = 0.50) for breast MRI and substantial (κ = 0.74) for CEM and it showed a statistically superior agreement of the expert over the non-expert readers only for MRI (p = 0.011) and not for CEM (p = 0.062). The agreement of CEM was superior to that of MRI (p = 0.012), including for both expert (p = 0.031) and non-expert readers (p = 0.005).

Campus Food Pantry Use May Improve Daily Frequency of Fruit and Vegetable Intake Among California University Students Experiencing Food Insecurity

BackgroundUniversity students are at increased risk for both food insecurity and suboptimal fruit and vegetable (F/V) intake. Campus food pantries (CFP) have been a common intervention in response to student food insecurity, but there have been few evaluations of the effectiveness of this strategy on improving students’ dietary intake. ObjectiveTo examine differences in the frequency of F/V intake by food security status, and whether the number of monthly CFP visits was associated with frequency of F/V intake among university students who were CFP clients. DesignCross-sectional ParticipantsThe study population included 1,188 university students across the 10-campus University of California system who had accessed their CFP or Basic Needs Center. Main measuresUnited States Department of Agriculture (USDA) 6-item short food security module, monthly CFP visits, and self-reported frequency of F/V intake (daily). Statistical analyses performedT-tests were performed to compare differences in mean daily frequency of F/V intake by food security status. Generalized linear models were used to examine associations between monthly CFP visits and mean daily frequency of F/V intake. Using a post hoc Wald test, an interaction term (monthly CFP visits × food security status) was included to the models to determine whether food security status modified associations. ResultsStudents experiencing food insecurity consumed total F/V 0.48 fewer times per day compared with students who were food secure (2.64 ± 2.11 vs 3.12 ± 2.01; P < .001). Among students experiencing food insecurity, each monthly CFP visit was associated with higher daily frequencies of intake for total F/V (β = 0.06), whole fruit (β = 0.03), 100% fruit juice (β = 0.01), and leafy greens (β = 0.01); however, no associations were found among the food-secure group. ConclusionsFindings suggest that CFPs may be supporting students in need of nutritional assistance increase their daily frequency of F/V intake. Future studies are needed to validate these findings to inform investment in campus food pantries and support university students in need of nutritional assistance.

The Asymmetric Effects of Foreign Direct Investment Inflow on Economic Growth: Fresh Evidence from Somalia

Purposes: Although there has been some progress since the 2012 establishment of the Federal Government Somalia, Foreign direct investment is the one of critical engines in economic growth. The latest value of foreign direct investment for 2021 was 5.97% of GDP, comparison of previous years to 2020 was 6.74% of GDP in Somalia. This paper aims to investigate the asymmetric effects of foreign direct investment (FDI) inflow on economic growth in Somalia from 1977-2021. In addition to FDI, inflation, gross capital formation, and trade openness are used as control variables to examine their influence on economic growth. Design/Methodology/approach: This research employs the nonlinear autoregressive distributed lag (NARDL) technique. Finding: The findings reveal that a positive shock in FDI positively influences Somalia's economic growth, while a negative FDI has a negative impact. The Wald test establishes the asymmetric effect of FDI on economic growth in both the short and long run. Moreover, trade openness and inflation rate significantly reduce the pace of economic growth in both the long and short run. However, gross capital formation only boosts economic growth in the long run. Research limitations/ implications: although Somali faced a lot of challenges including civil war, terrorists, macroeconomics instability, poor infrastructure, and corruptions are not considered owing to data availability. Policy implications: firstly, increase revenue through tax reforms and anti-corruption measures. Secondly, initiatives policies that help public finance investments and reduce trade barriers. Finally, maintain macroeconomic stability of inflation, and exchange rates, can help reduce the negative impact on economic growth improve the investment climate and Somalia’s image abroad.

Fusion detection in distributed MIMO radar under hybrid-order Gaussian model

In this paper, we deal with the point-like target fusion detection in a partially homogeneous environment with distributed MIMO radar. Specifically, we consider the imperfect waveform separation problem, which means that the matched filter output contains not only the auto-correlation term of the current matched waveform but also the cross-correlation terms, called waveform residuals, with the remaining transmitted waveforms. To this end, a hybrid-order Gaussian (HOG) model is utilized, where the target amplitude is deterministic but unknown, and the waveform residuals obey the Gaussian distribution. Then two adaptive detectors are developed according to the GLRT and Wald test, named HOG-GLRT and HOG-Wald respectively. At the fusion detection stage, we focus on the problem of signal-to-noise ratio (SNR) diversity, i.e., the detection performance degradation due to the average weighting of spatial diversity channels when the echo SNR differs. Combined with the Model Order Selection criterion and multiple hypothesis test, two modified fusion detectors based on channel selection are proposed, named MHOG-GLRT and MHOG-Wald. Finally, the numerical simulation results show that the HOG-GLRT is sensitive against waveform residuals, while the HOG-Wald exhibits strong robustness. And it also demonstrates the effectiveness of the MHOG-GLRT and MHOG-Wald facing the extreme scene of SNR diversity.

Examining the Dynamics of Labour Force Participation, Carbon Dioxide Emission and Population Health in Sub-Saharan Africa

This study examined the dynamic causal relationship between population health, carbon dioxide (CO2) emission and labour force participation. The study employed the block exogeneity wald test after estimating a panel VAR model using data for 24 sub-Saharan African countries from 1990 to 2018. The dynamic interactions among the variables and effect of shocks was also examined using the variance decomposition analysis and impulse response functions. For robustness, the estimation was done using both aggregate labour force participation and disaggregated labour force participation inorder to investigate the differentials by gender. The results showed evidence of a feedback effect between life expectancy and total labour force participation. This also holds for male labour force participation however, a unidirectional causality was found from female labour force participation to life expectancy. A one standard deviation shock in CO2 emission had a negative impact on life expectancy. Life expectancy also had the greatest contribution to variations in CO2 emission. Male labour force participation shocks had a consistently negative impact over the ten-year period, however, shocks from female labour force participation had the least impacts on CO2 emission. Environmental reforms encouraging less polluting energy and technology use is important for CO2 emission reduction, which further increases life expectancy and ultimately labour force participation rates.

Homogeneity Test of Response Rate Functions in Bilateral Correlated Data under Dallal’s Model

In clinical studies, paired binary metadata are often encountered during collecting information from paired organs or parts. The risk difference, relative risk ratio, and odds ratios of response rates are widely used to measure cured effects for such data. Under Dallal’s model, the paper extends these measures to the general case in g groups. We propose eight test statistics for various functions when the dependency measures γ i i = 1 , … , g are equal or not. Simulation results show that the Score and Rosner-type tests can produce robust empirical type I error rates, while Wald-type and likelihood ratio tests have better power in testing the risk difference, relative risk ratio, and odds ratios. Finally, two real examples illustrate our proposed method’s practicability.

Poverty and financial development: an asymmetric and nonlinear ARDL analysis for India

PurposeThe primary objective of the paper is to examine the asymmetric Cointegration and asymmetric causality between financial development and poverty alleviation on annual data in Indian context over the period from 1980 to 2019.Design/methodology/approachFirst nonlinearity test by Brooks et al. (1999) is applied to ascertain the nonlinear behavior of the variables used. Once the nonlinear behavior of variables is confirmed, asymmetric and nonlinear unit root tests by Kapetanios and Shin (2008) are applied to check for the order of integration of selected variables. Next, nonlinear autoregressive distributed lag model (NARDL) is employed to analyze the asymmetric Cointegration. Finally, Hatemi-j- asymmetric causality tests is applied to work out the direction of asymmetric causality.FindingsThe empirical findings document the existence of asymmetries in the short-run as well as long-run between poverty and financial development. The asymmetry reveals that negative financial development shocks leave a more profound impact on poverty alleviation than their positive equivalents. The findings of Wald's test also confirm the presence of asymmetric Cointegration. The asymmetric cumulative dynamic multipliers used to examine the behavior of asymmetries and adjustments with respect to time lend credence to the results calculated using NARDL estimator. This result exhibits the robustness of the model. Furthermore, the result emanating from recently introduced asymmetric causality test reveals a unidirectional asymmetric causality between negative shocks in financial development and poverty. The findings of the present study necessitate the need for investigating asymmetric and nonlinear effects in finance–poverty nexus, which existent literature has completely neglected, in order to have relevant policy conclusions.Research limitations/implicationsThe study used “Per capita consumption expenditure” as a measure for poverty due to lack of continuous time series data on headcount ratio. In future, researchers can extend this study by incorporating headcount ratio as a measure of poverty in their respective works. There is further scope of research on this issue by finding out the impact of formal and informal sources of credit on poverty separately. A panel data study for developing countries over a period of time could further confirm/negate the findings of the present study.Originality/valueTo the best of the authors’ knowledge none of the studies in Indian context has scrutinized asymmetric and nonlinear impact of financial development on poverty. To dredge up asymmetric structures at work, the authors have used the highly celebrated NARDL estimator. To enrich the existent body of knowledge along the lines of asymmetric (nonlinear) linkages, the authors have also used recently introduced asymmetric causality test by Hatemi-j-(2012) to find out the direction asymmetric causality.

Non-O ABO blood group genotypes differ in their associations with Plasmodium falciparum rosetting and severe malaria.

Blood group O is associated with protection against severe malaria and reduced size and stability of P. falciparum-host red blood cell (RBC) rosettes compared to non-O blood groups. Whether the non-O blood groups encoded by the specific ABO genotypes AO, BO, AA, BB and AB differ in their associations with severe malaria and rosetting is unknown. The A and B antigens are host RBC receptors for rosetting, hence we hypothesized that the higher levels of A and/or B antigen on RBCs from AA, BB and AB genotypes compared to AO/BO genotypes could lead to larger rosettes, increased microvascular obstruction and higher risk of malaria pathology. We used a case-control study of Kenyan children and in vitro adhesion assays to test the hypothesis that "double dose" non-O genotypes (AA, BB, AB) are associated with increased risk of severe malaria and larger rosettes than "single dose" heterozygotes (AO, BO). In the case-control study, compared to OO, the double dose genotypes consistently had higher odds ratios (OR) for severe malaria than single dose genotypes, with AB (OR 1.93) and AO (OR 1.27) showing most marked difference (p = 0.02, Wald test). In vitro experiments with blood group A-preferring P. falciparum parasites showed that significantly larger rosettes were formed with AA and AB host RBCs compared to OO, whereas AO and BO genotypes rosettes were indistinguishable from OO. Overall, the data show that ABO genotype influences P. falciparum rosetting and support the hypothesis that double dose non-O genotypes confer a greater risk of severe malaria than AO/BO heterozygosity.

Evaluation of a Deep Learning-based Algorithm for Post-Radiotherapy Prostate Cancer Local Recurrence Detection Using Biparametric MRI

ObjectiveTo evaluate a biparametric MRI (bpMRI)-based artificial intelligence (AI) model for the detection of local prostate cancer (PCa) recurrence in patients with radiotherapy history. Materials and MethodsThis study included post-radiotherapy patients undergoing multiparametric MRI and subsequent MRI/US fusion-guided and/or systematic biopsy. Histopathology results were used as ground truth. The recurrent cancer detection sensitivity of a bpMRI-based AI model, which was developed on a large dataset to primarily identify lesions in treatment-naïve patients, was compared to a prospective radiologist assessment using the Wald test. Subanalysis was conducted on patients stratified by the treatment modality (external beam radiation treatment [EBRT] and brachytherapy) and the prostate volume quartiles. ResultsOf the 62 patients included (median age = 70 years; median PSA = 3.51 ng/ml; median prostate volume = 27.55 ml), 56 recurrent PCa foci were identified within 46 patients. The AI model detected 40 lesions in 35 patients. The AI model performance was lower than the prospective radiology interpretation (Rad) on a patient-(AI: 76.1% vs. Rad: 91.3%, p = 0.02) and lesion-level (AI: 71.4% vs. Rad: 87.5%, p = 0.01). The mean number of false positives per patient was 0.35 (range: 0–2). The AI model performance was higher in EBRT group both on patient-level (EBRT: 81.5% [22/27] vs. brachytherapy: 68.4% [13/19]) and lesion-level (EBRT: 79.4% [27/34] vs. brachytherapy: 59.1% [13/22]). In patients with gland volumes >34 ml (n = 25), detection sensitivities were 100% (11/11) and 94.1% (16/17) on patient- and lesion-level, respectively. ConclusionThe reported bpMRI-based AI model detected the majority of locally recurrent prostate cancer after radiotherapy. Further testing including external validation of this model is warranted prior to clinical implementation.

Hypothesis Tests under Separation

Abstract Separation commonly occurs in political science, usually when a binary explanatory variable perfectly predicts a binary outcome. In these situations, methodologists often recommend penalized maximum likelihood or Bayesian estimation. But researchers might struggle to identify an appropriate penalty or prior distribution. Fortunately, I show that researchers can easily test hypotheses about the model coefficients with standard frequentist tools. While the popular Wald test produces misleading (even nonsensical) p-values under separation, I show that likelihood ratio tests and score tests behave in the usual manner. Therefore, researchers can produce meaningful p-values with standard frequentist tools under separation without the use of penalties or prior information.

Family Resilience and Dyadic Coping during the Outbreak of the COVID-19 Pandemic in Italy: Their Protective Role in Hedonic and Eudaimonic Well-Being.

The COVID-19 pandemic outbreak has dramatically worsened people's psychological well-being. Our aim was to examine for the first time the concurrent and longitudinal relations of family resilience with hedonic and eudaimonic well-being, and the moderating role of socio-demographics. For people having a romantic partner, we also explored whether family resilience and dyadic coping were uniquely related to well-being. One cross-sectional study (N = 325) and one 10-week follow-up study (N = 112) were carried out during the outbreak of the COVID-19 pandemic (April-May 2020) in Northern Italy. Adult participants completed an online questionnaire in both studies. Correlation, multivariate regression, and moderation analyses were carried out with IBM SPSS version 28 and its PROCESS macro. Significance of differences in correlation and regression coefficients was tested through Steiger's procedure, Wald test, and SUEST method. Family resilience was found to relate more strongly to eudaimonic (versus hedonic) well-being concurrently and to hedonic (versus eudaimonic) well-being longitudinally. The concurrent or longitudinal relations with hedonic well-being were generally stronger for females, part-time workers, and people undergoing multiple stressors. For people having a romantic partner, family resilience was concurrently associated with well-being independently of dyadic coping, whereas dyadic coping was longitudinally related to well-being independently of family resilience. Family resilience was found to protect, in the short term, the psychological well-being of people facing the pandemic outbreak. Its protective role mainly concerned hedonic well-being and was more pronounced for more vulnerable people. For persons having a romantic partner, however, dyadic coping seemed to have equal, if not greater, positive short-term effects.

Comparing MIMIC and MIMIC-interaction to Alignment Methods for Investigating Measurement Invariance concerning a Continuous Violator

Measurement invariance holds when a latent construct is measured in the same way across different levels of background variables (continuous or categorical) while controlling for the true value of that construct. Using Monte Carlo simulation, this paper compares the multiple indicators, multiple causes (MIMIC) model and MIMIC-interaction to a novel use of alignment optimization (AO) for detecting measurement noninvariance when the violator is a continuous variable. Results showed that MIMIC and MIMIC-interaction in sequential likelihood ratio tests and Wald tests with a Bonferroni correction provided a good balance between identifying invariant and noninvariant (linear violations) items when n ≥ 500 in terms of classification accuracy (CA). AO (CA ≥ .86) was as competitive as MIMIC and MIMIC-interaction to linear invariance violations but was far better under nonlinear quadratic violations when n ≥ 1,000 (i.e., 100 per group for 10 groups).

Dual ICA to extract interacting sets of genes and conditions from transcriptomic data.

One of the challenges in RNA-Seq studies is finding subsets of genes that share a common mechanism of action or are associated with a regulon/pathway. Existing approaches often extract modules that reflect quantitative similarities (such as genes with correlated log-fold-changes) but do not adequately capture biological significance. In this work, we propose the Dual ICA methodology, which provides an agnostic way to extract "interacting modules" composed of sets of genes and conditions that exhibit strong associations. Dual ICA involves performing Independent Component Analysis (ICA) twice, once on the genes and once on the conditions. Using the resulting signal matrices, we extract respective sets of genes and conditions. The interaction between these sets is quantified using the coefficients from a linear regression and significance is determined through the Wald test and Z-score filtering. These coefficients are equivalent to the outer product of independent components obtained from the two signal matrices. Not only do the gene sets extracted align with known regulons, but the significant interacting modules they instantiate also encompass conditions that influence the expression of these regulons through shared mechanisms of action. Compared to traditional unsupervised clustering methods, Dual ICA demonstrates superior performance and provides explicit gene-condition sets for exploring functional relationships.