Large Sample Tests Research Articles

Government influence and the use of taxable income in CEO compensation

We examine the role of taxable income in CEO compensation within the Chinese setting, where government influence is strong. We survey Chinese listed firms and find that 43% of the participating companies indicate their use of tax return information in executive compensation and turnover decisions. Large-sample tests show that CEO compensation rises as taxable income increases, and the effect of taxable income is more pronounced under greater government influence. Furthermore, the likelihood of CEO turnover decreases as taxable income increases, and this effect intensifies as government influence strengthens. Overall, our results suggest that strong government influence shapes the role of taxable income in CEO compensation and turnover decisions.

Do investors care about carbon emissions? Evidence based on stock return co-movement with machine learning-augmented data

PurposeThis paper represents the first attempt to examine investor behavior for green stocks through the lens of return co-movement, and provides evidence indicating that green investment practices have gained traction after 2012.Design/methodology/approachWe empirically test the hypotheses that the stock returns of firms with similar carbon dioxide emissions levels move together and, if so, whether this co-movement has increased over time as people become more “carbon-conscious.” Our baseline sample, based on carbon emissions data from public company disclosures, suffers from limited coverage, particularly before 2016, leading to low statistical power and sample selection bias. To address this, we employ machine learning methodologies to forecast the carbon emissions of firms that do not disclose such information, nearly quadrupling the sample size. Our findings indicate that stocks with similar carbon emissions exhibit higher co-movement in stock returns in both the baseline and augmented data samples. Furthermore, this co-movement has increased during the 2012–2020 period compared to the 2004–2011 period, suggesting that green investment has gained traction over time.FindingsWe find that stocks with similar carbon emissions exhibit higher co-movement in stock returns in both the baseline sample and the augmented data sample, and the co-movement has increased in the 2012–2020 period compared to the 2004–2011 years, suggesting that green investment has gained traction over time.Originality/value(1) We use machine learning methodology to augment carbon emissions sample which goes back to 2004. Our approach almost quadruples the original data, enabling large-sample testing. (2) We are the first paper to examine how green companies' stock returns co-move and thus provide complementary results on the research on expected returns and carbon emissions.

Preliminary Test Estimation for Parallel 2-Sampling in Autoregressive Model

The purpose of this paper is to discuss the problem of estimation and testing the equality of two autoregressive parameters of two first-order autoregressive processes AR(1), where for each process, the observations are made at different time points. The primary interest is to propose the testing procedures for the homogeneity of autocorrelation parameters ρ1 and ρ2. Furthermore, we are interested in estimating ρ1 under uncertain and weak prior information about the possible equality of ρ1 and ρ2, though we may not have full confidence in the tenacity of this information. A large sample test for the homogeneity of the parameters is developed. Pooled “P” (or restricted estimator) and preliminary test “PT” estimators are proposed, and their properties are investigated and compared with the unrestricted estimator “UE” of ρ1.

Common odds ratio test and interval estimation for stratified bilateral and unilateral data.

In clinical research, data are commonly collected bilaterally from paired organs or bodily parts within individual subjects. However, unilateral data arise when constraints or limiting factors impede the collection of complete bilateral data. In this article, we propose three large-sample tests and five confidence interval methods for making inferences on the common treatment effect, measured by the odds ratio, in a stratified design under integrated bilateral and unilateral data. Our simulation results show that the likelihood ratio-based and score-based tests, along with their associated confidence interval methods, demonstrate robust control of type I error and close-to-nominal coverage probabilities. We apply the proposed methods to real-world datasets of acute otitis media and myopic eyes to showcase their validity and applicability in clinical practice.

Model Selection in Generalized Linear Models

The problem of model selection in regression analysis through the use of forward selection, backward elimination, and stepwise selection has been well explored in the literature. The main assumption in this, of course, is that the data are normally distributed and the main tool used here is either a t test or an F test. However, the properties of these model selection procedures are not well-known. The purpose of this paper is to study the properties of these procedures within generalized linear regression models, considering the normal linear regression model as a special case. The main tool that is being used is the score test. However, the F test and other large sample tests, such as the likelihood ratio and the Wald test, the AIC, and the BIC, are included for the comparison. A systematic study, through simulations, of the properties of this procedure was conducted, in terms of level and power, for symmetric and asymmetric distributions, such as normal, Poisson, and binomial regression models. Extensions for skewed distributions, over-dispersed Poisson (the negative binomial), and over-dispersed binomial (the beta-binomial) regression models, are also given and evaluated. The methods are applied to analyze two health datasets.

Deliberative panels as a source of public knowledge: A large-sample test of the Citizens' Initiative Review.

Evolving US media and political systems, coupled with escalating misinformation campaigns, have left the public divided over objective facts featured in policy debates. The public also has lost much of its confidence in the institutions designed to adjudicate those epistemic debates. To counter this threat, civic entrepreneurs have devised institutional reforms to revitalize democratic policymaking. One promising intervention is the Citizens' Initiative Review (CIR), which has been adopted into law in Oregon and tested in several other states, as well as Switzerland and Finland. Each CIR gathers a demographically stratified random sample of registered voters to form a deliberative panel, which hears from pro and con advocates and neutral experts while assessing the merits of a ballot measure. After four-to-five days of deliberation, each CIR writes an issue guide for voters that identifies key factual findings, along with the most important pro and con arguments. This study pools the results of survey experiments conducted on thirteen CIRs held from 2010 to 2018, resulting in a dataset that includes 67,120 knowledge scores collected from 10,872 registered voters exposed to 82 empirical claims. Analysis shows that reading the CIR guide had a positive effect on voters' policy knowledge, with stronger effects for those holding greater faith in deliberation. We found little evidence of directional motivated reasoning but some evidence that reading the CIR statement can spark an accuracy motivation. Overall, the main results show how trust in peer deliberation provides one path out of the maze of misinformation shaping voter decisions during elections.

Simultaneous screening of zebrafish larvae cardiac and respiratory functions: a microfluidic multi-phenotypic approach.

Multi-phenotypic screening of multiple zebrafish larvae plays an important role in enhancing the quality and speed of biological assays. Many microfluidic platforms have been presented for zebrafish phenotypic assays, but multi-organ screening of multiple larvae, from different needed orientations, in a single device that can enable rapid and large-sample testing is yet to be achieved. Here, we propose a multi-phenotypic quadruple-fish microfluidic chip for simultaneous monitoring of heart activity and fin movement of 5-7-day postfertilization zebrafish larvae trapped in the chip. In each experiment, fin movements of four larvae were quantified in the dorsal view in terms of fin beat frequency (FBF). Positioning of four optical prisms next to the traps provided the lateral views of the four larvae and enabled heart rate (HR) monitoring. The device's functionality in chemical testing was validated by assessing the impacts of ethanol on heart and fin activities. Larvae treated with 3% ethanol displayed a significant drop of 13.2 and 35.8% in HR and FBF, respectively. Subsequent tests with cadmium chloride highlighted the novel application of our device for screening the effect of heavy metals on cardiac and respiratory function at the same time. Exposure to 5$\mu$g/l cadmium chloride revealed a significant increase of 8.2% and 39.2% in HR and FBF, respectively. The device can be employed to monitor multi-phenotypic behavioral responses of zebrafish larvae induced by chemical stimuli in various chemical screening assays, in applications such as ecotoxicology and drug discovery.

Wild Boar (Sus scrofa)—Fascioloides magna Interaction from the Perspective of the MHC Genes

Fascioloidosis is a parasitic disease caused by a trematode Fascioloides magna. Since major histocompatibility complex (MHC) genes play an important role in the immune response, the aim of this study was to compare the potential differences in MHC class II SLA-DRB1 exon 2 genes between wild boar populations from infected (cases) and non-infected areas (controls). During the winter of 2021, a total of 136 wild boar tissue samples were collected, 39 cases and 97 controls. DNA was extracted and sequenced using the Illumina platform. Differences in distributions of allele combinations were calculated using the Chi-Square test for homogeneity and between proportions using the large-sample test and Fisher-Irwin test. Analysis revealed 19 previously described swine leucocyte antigen (SLA) alleles. The number of polymorphic sites was 79 (29.6%), with 99 mutations in total. Nucleotide diversity π was estimated at 0.11. Proportions of the alleles SLA-DRB1*12:05 (p = 0.0008379) and SLA-DRB1*0101 (p = 0.0002825) were statistically significantly higher in controls, and proportions of the SLA-DRB1*0602 (p = 0.006059) and SLA-DRB1*0901 (p = 0.0006601) in cases. Alleles SLA-DRB1*04:09, SLA-DRB1*0501, SLA-DRB1*11:09, and SLA-DRB1*1301 were detected only in cases, while SLA-DRB1*0404, SLA-DRB1*0701, SLA-DRB1*02:10, and SLA-DRB1*04:08 were present only in controls. We did not confirm the existence of specific alleles that could be linked to F. magna infection. Detected high variability of the MHC class II SLA-DRB1 exon 2 genes indicate high resistance potential against various pathogens.

A nonparametric test for equality of survival medians using right-censored prevalent cohort survival data.

The median is a robust summary commonly used for comparison between populations. The existing literature falls short in testing for equality of survival medians when the collected data do not form representative samples from their respective target populations and are subject to right censoring. Such data commonly occur in prevalent cohort studies with follow-up. We consider a particular case where the disease under study is stable, that is, the incidence rate of the disease is stable. It is known that survival data collected on diseased cases, when the disease under study is stable, form a length-biased sample from the target population. We fill the gap for the particular case of length-biased right-censored survival data by proposing a large-sample test using the nonparametric maximum likelihood estimator of the survivor function in the target population. The small sample performance of the proposed test statistic is studied via simulation. We apply the proposed method to test for differences in survival medians of Alzheimer's disease and dementia groups using the survival data collected as part of the Canadian Study of Health and Aging.

Metal Oxide Nanomaterials as Rice Transgenic Carriers

For the development of nanotechnology, nanomaterials are in an important position. Due to their special structure and a series of unique properties, nano-metal oxide materials are widely used in nanoelectronics, catalysis, environmental monitoring, electrochemical sensors, and many other fields. Rice, as the most important food crop in the world, has a wide range of planting areas. Therefore, in this paper, the research on the construction of rice transgenic vectors is based on nano-metal oxide materials. In this paper, particle swarm optimization is used to optimize the weights of rice-growing convolutional neural networks. Secondly, it used rice as the test plant to study the effects of seven metal oxide nanomaterials on rice seed germination and root elongation. This sheds light on the toxic effects of metal oxide nanomaterials on rice growth and discusses the factors that affect the phytotoxicity of nanomaterials. The experimental results show that before optimization, the accuracy of the small batch sample test is 87.0456%, the accuracy of the large batch sample test is 93.5876%, and the accuracy of the test is increased by 6.542%. After optimization, the accuracy of the small batch sample test is 93.5876%, the accuracy of the large batch sample test is 99.0133%, and the accuracy of the test is increased by 5.4466%. The inhibition rates of nano-CuO and ZnO on rice root length were 97.28% and 66.75%.

The Effect of the Integrated Chinese and Western Medicine for the Treatment of Parkinson's Disease: A Meta-Analysis.

Objective Traditional Chinese medicine (TCM) has been used to treat Parkinson's disease (PD), but the efficacy is still not clear. The aim of this study was to evaluate the effect of the integrated Chinese and Western medicine (ICWM) for PD through a meta-analysis. Methods We searched randomized controlled trials comparing integrated Chinese and Western medicine (ICWM) versus conventional Western medicine (CWM) for Parkinson's disease. Data were extracted from eligible studies. We sought to evaluate pretreatment and posttreatment symptoms of PD patients and their quality of life and reduce adverse reactions. The results were expressed as risk ratio (RR) and mean difference (MD) with accompanying 95% confidence intervals. Results Twenty-three studies were included in this study with a total of 1769 patients. The pooled results revealed that ICWM significantly improved the UPDRS score than CWM, the MD of UPDRS-I, II, III, and IV was -1.05 (95% CI: -1.42 to -0.69, P < 0.00001), -2.55 (95% CI: -3.19 to -1.90, P < 0.00001), -3.64 (95% CI: -4.69 to -2.60, P < 0.00001), and -0.61 (95% CI: -0.96 to -0.27, P = 0.0004), respectively, and ICWM also had a better score of PDQ-39 (MD = −8.71, 95% CI: -13.52 to -3.90, P = 0.0004) and MoCA scores (MD = 3.35, 95% CI: 1.65 to 5.04, P = 0.0001) compared with CWM. ICWM had certain advantages in terms of effective rate (RR = 1.27, 95% CI: 1.18 to 1.37, P < 0.00001) and adverse reactions (RR = 0.21, 95% CI: 0.13 to 0.36, P < 0.00001). Conclusion Our research supported that ICWM had important health benefits for patients with PD and can effectively improve the symptoms of PD patients and their quality of life and reduce adverse reactions. Due to the lower quality of the included studies, large sample and multicenter randomized control test should be performed to verify our conclusions.

Automating rainfall recording: Ensuring homogeneity when instruments change

Sub-daily rainfall is used in modelling of small catchments, design of urban drainage, calculating soil erosion, understanding the water balance of vegetated catchments, and assessing the impact of climate change on short duration storms. Hence it is critical to ensure that sub-daily rainfall records are homogeneous, but with a global shift to automated measurement of rainfall, there has been a change in the rainfall recording instrumentation. Although changes in instrumentation should be documented, in many cases this is not the case and station metadata are missing. As result, studies of rainfall patterns, particularly those investigating trends, often cannot report their treatment of possible inhomogeneities and must assume the data is suitable for further analysis.Here, we argue that standard quality assurance methods for checking inhomogeneities in rainfall may not identify a change in the rainfall record due to a change in instrumentation. Through testing (1) an aggregation of rainfall to the coarsest instrument resolution, (2) removal of rainfall below a minimum rainfall depth, (3) removal of rainfall below a minimum accumulation (event) depth, and (4) a combination of the above adjustments, we present recommendations for ensuring sub-daily rainfall is homogenous with minimum alteration to the rainfall record.We assess the proposed methods using the case study of Australia’s sub-daily rainfall monitoring network, where, in 1996 the pluviograph network was switched to tipping buckets, creating a large-sample test bed with a known systematic instrumentation change. Our results show that event-based statistics are considerably more sensitive to instrument change than annually aggregated rainfall statistics and hence standard methods for quality assurance may not identify possible inhomogeneities. We suggest an aggregation of the data to the coarsest instrument resolution, with the removal of small rainfall accumulations, can alleviate inhomogeneities for event-based statistics, whereas, for annual statistics, aggregation to the coarsest instrument resolution is sufficient. In the absence of a known instrumentation change, removing rainfall at or below the instrument resolution is also a viable technique for improving rainfall record homogeneity for event-based statistics. These findings will aid future sub-daily rainfall studies by justifying the use of a minimum rainfall or event depth for subsequent analysis.

Alternative tests for the significance of the intraclass correlation coefficients under unequal family sizes

The intraclass correlation coefficient ρ is used as a measure of the degree of resemblance among family members with respect to some biological or psychological attribute, such as blood pressure level or IQ. Many studies have been done in the area. However, a significant gap in the literature is that few statistics are available to test the significance ( against ) of this important biological measure and no study is available as to the performance of these statistics with respect to level and power. In this paper we develop three large sample test statistics and compare these, in terms of level and power, using extensive simulation studies of these, the Wald test developed in this paper is found to have a very simple form and highest power while maintaining level well.

Gaussian Prepivoting for Finite Population Causal Inference

AbstractIn finite population causal inference exact randomization tests can be constructed for sharp null hypotheses, hypotheses which impute the missing potential outcomes. Oftentimes inference is instead desired for the weak null that the sample average of the treatment effects takes on a particular value while leaving the subject-specific treatment effects unspecified. Tests valid for sharp null hypotheses can be anti-conservative should only the weak null hold. We develop a general framework for unifying modes of inference for sharp and weak nulls, wherein a single procedure simultaneously delivers exact inference for sharp nulls and asymptotically valid inference for weak nulls. We employ randomization tests based upon prepivoted test statistics, wherein a test statistic is first transformed by a suitably constructed cumulative distribution function and its randomization distribution assuming the sharp null is then enumerated. For a large class of test statistics, we show that prepivoting may be accomplished by employing the push-forward of a sample-based Gaussian measure based upon a suitable covariance estimator. The approach enumerates the randomization distribution (assuming the sharp null) of a p-value for a large-sample test known to be valid under the weak null, and uses the resulting randomization distribution for inference. The versatility of the method is demonstrated through many examples, including rerandomized designs and regression-adjusted estimators in completely randomized designs.

Developing a remote sensing-based ecological index based on improved biophysical features

More and more human activities have caused varying degrees of interference with the ecosystem. As an effective quantitative means of eco-environmental quality, remote sensing has been widely used. The remote sensing ecological index (RSEI) is the most popular ecological evaluation model at present. However, the model is affected by the direction of the eigenvector, and two completely opposite results will appear in the calculation of the model. In the past, researchers often manually judged whether the two results met the expectations according to a priori knowledge to select the appropriate calculation results. With the development of remote sensing big data, applying this artificial discrimination method to eco-environmental monitoring under the background of big data is difficult. Therefore, we test the evolution of the RSEI model eigenvector in time series through large samples. It is found that the results of the two models are completely opposite in space. For any model, changing the order of input bands may result in RSEI also being the opposite. Through the large sample test in different time periods, the eigenvectors of each ecological factor affect the direction of the corresponding RSEI. Therefore, we propose an improved model to judge the characteristic contribution direction of the first principal component by selecting the wet factor, which is less affected by seasonal changes. The model direction can be automatically modified without the intervention of researchers according to subjective experience. The improved model can adapt different periods of RSEI calculation. At the same time, no matter how the input band order changes, the final result direction is correct. The improved model makes it possible to monitor and calculate the eco-environmental quality of remote sensing big data and provides a solid scientific basis for the development of the model through research on the mechanism of the model.

Market-oriented green innovation model: conceptualisation and scale development of disruptive green innovation

ABSTRACT Market-oriented green innovation is receiving increasing attention. However, due to the dominance of traditional technologies and the underdevelopment of green technology capabilities, the speed at which green innovation enters the market is slow. Overcoming competitive disadvantages for latecomer green innovations has become a key issue to be solved. Disruptive innovation theory provides latecomers with opportunities for leapfrog development. Combined with the theory of disruptive innovation and green innovation, this study proposes a new model for market-oriented green innovation – disruptive green innovation. This study then attempts to develop a scale of disruptive green innovation. Through qualitative methods such as literature deduction, interview induction and expert screening, the initial items of disruptive green innovation are set up. Through quantitative methods such as pretests and large sample tests, the initial items are refined and analyzed. Finally, the scale of disruptive green innovation composed of seven items is developed. This study not only provides important theoretical guidance for the market strategy deployment of green innovation in China and clear measurement tools for follow-up research but also provides important practical enlightenment for green innovation to catch up in the fierce market competition.

“Infeliz” or “Triste”: A Paradigm for Mixed Methods Exploration of Outcome Measures Adaptation Across Language Variants

The literature on measure translation tends to hold, overtly or covertly, a questionable assumption about the possibility of exact translation and almost completely ignores issues of within language variation. Equally, psychometric methods used to assess cross-cultural validity after translation focus on large sample tests of cross-sectional measurement invariance. Such invariance is often not found and is of dubious pertinence to change/outcome measures usually used in psychotherapy research. We present a sequential process of three substudies using quantitative and qualitative procedures to explore whether an outcome measure needs to be changed when used across language variation. Qualitative data confirmed that an item was not ideal in the new context. However, quantitative exploration showed that, although statistically significant and affected by gender and item order, the impact of changing the item in the overall score was small, allowing retention of the existing Spanish translation. We argue that the myth of perfect translation and over-reliance on large-sample psychometric testing pursuing measurement invariance limit exploration of language effects. We recommend that these be used in the companion of user-based, sequential, mixed-method exploration to support the development of a richer field of understanding of outcomes and change self-report measures across languages and cultures and both across and within languages.

A new family of tests for DMTTF alternatives under complete and censored samples

A family of nonparametric tests is proposed for detecting DMTTF property. The exact distributions of the test statistics are derived under the null hypothesis of exponentiality and critical values are calculated. The proposed test statistics are shown to be asymptotically normal and the consistency of the tests is established. Pitman asymptotic efficacy values are calculated. A large sample test under random censoring is also proposed and the relevant asymptotics are worked out. A Monte Carlo simulation study is carried out in order to assess the merit of the proposed tests. Finally, the test is applied to some real data sets for illustration.

Solution of an open reality based word-problem in two secondary schools

This survey through an open reality based word problem is intended to assess - in two secondary schools in Komárom (Hungary) and in Komarno (Slovakia, Hungarian name: Révkomárom) in grade 10 - the ability of students to realize openness of a task. The comparison is justified by the fact that the language of teaching is Hungarian in both secondary schools, but with different curricula. This survey is related to the Content Pedagogy Research Program by the Hungarian Academy of Sciences. It is preceded by several surveys with a word problem (Pocket Money) of the third author and led by her between 2012 and 2015, and within that project in 2017 within a large sample test, among about 1500 students and university students in Hungary (?, ?) (?, ?). In our research we wanted first to assess how openly work students in two schools of the two cities mentioned in solving the same task. The answer to this question was similar to the large sample test results, so most of the students worked in a closed way, when solving this word problem. So we went on and tried to explore how students thought about their own solution given to this task, through mixed-type interviews.&#x0D; Subject Classification: 97D70, 97F90, 97D50, 97M10

Contact-implicit preference relationships: Two large sample tests

ABSTRACT This work examines relationships between friendships and implicit preferences across two large samples. There is considerable evidence in the contact literature suggesting that friendships relate to more favorable attitudes toward outgroups, however, most evidence reflects explicit self-report measures. Using samples of 235,543 participants who completed the Disability IAT and 533,220 participants who completed the Sexuality IAT on the Project Implicit website, results indicate that participants reporting either a disabled friend or close acquaintance demonstrated weaker implicit preferences for abled over disabled people. Similarly, those with gay friends demonstrated weaker implicit preference for “straight” over gay. The size of these relationships were considerably smaller than found for explicit evaluations. These effect size estimates should be useful to researchers studying contact-implicit preference relationships as it informs power analyses and sample size planning decisions.