Significance Test Method Research Articles

Significance testing for cross correlation: A critical examination of correlations between ENSO and GRACE-derived terrestrial water storage variabilities

The cross correlation has a wide range of applications in geophysical fields for measuring linear connections or relationships among physical quantities. Nonetheless, there remains a dearth of comprehensive discourse regarding its statistical significance testing, which is crucial for differentiating meaningful outcomes from those merely stemming from fortuity or pure randomness. Conventional theoretical methods for significance testing, commonly used in statistical analysis tools such as SPSS and MATLAB, are only applicable when dealing with idealized circumstances such as white noise. In discretionary application of these methods to analyze geophysical signals with, say, red noise may result in potentially unjustified conclusions. This study aims to develop a comprehensive approach based on the t-distribution within a rigorous statistical context, aiming to facilitate significance tests of cross correlation for general signals with specified time shifts or ranges. Extensive Monte Carlo experiments substantiate its robustness, thereby paving the way for accurate and expeditious identification of statistically (hence potentially physically) meaningful correlations in general. As an example, we examine critically the previously purported significant correlations between ENSO (El Niño Southern Oscillation) and global terrestrial water storage variations derived from the GRACE (Gravity Recovery and Climate Experiment) satellite mission, demonstrating that they are subject to questioning in the absence of complete significance testing.

Spatiotemporal Variation Characteristics and Driving Factors of Cultivated Land NPP in the Shandong Area Around the Bohai Sea

The Bohai Rim Plain is an important grain-producing area in China. The cultivated land resources have great potential for production, but there are many influencing factors. Understanding the spatiotemporal change characteristics and driving factors of the net primary productivity （NPP） of cultivated land vegetation in this region is of great significance to improve the regional cultivated land production conditions, excavate and enhance the production capacity of cultivated land, and ensure national food security. In this study, Theil-Sen Median trend analysis, Mann-Kendall significance test, Hurst index, and other methods were used to explore the spatiotemporal change characteristics, stability, and sustainability of regional cultivated land NPP. The influence of driving factors on the spatial heterogeneity of cultivated land NPP was analyzed by using optical parameters-based geographical detectors. The results showed that： ① From 2001 to 2019, due to the expansion of construction land during industrialization and urbanization, the cumulative decrease in the area of cultivated land in the Shandong area around the Bohai Sea was 2 004.51 km2. ② During the selected research period, the interannual variation of average cultivated land NPP showed a fluctuating and increasing trend. In terms of spatial distribution, the NPP of cultivated land was bounded by the Dongying District, with the spatial heterogeneity in the north being significantly lower than that in the south. ③ The area with increasing NPP of cultivated land accounted for 88.06% of the total area of cultivated land, mainly with low and medium fluctuations. The NPP of cultivated land will maintain an overall sustained trend of increase across the region in the future. ④ The average annual relative humidity study area had the strongest explanatory power for the spatial variability of NPP in cropland, with a q-value of 0.26, followed by surface soil salinity and subsoil salinity, with q-values greater than 0.2. The interactions between the different drivers all showed either nonlinear enhancement or bifactorial enhancement. The results of this study will help to reveal the characteristics of the changes in cultivated land production capacity and its driving forces in the Bohai Sea region and also provide a theoretical basis for the ecological protection and sustainable development of the region.

Simulation research on interactive entertainment e-learning based on visual saliency testing in music multimedia teaching system

In traditional music teaching method, students passively accept information and lack sufficient interaction and participation. Entertainment robots can provide students with a more interesting and engaging learning experience through means such as multimedia content, gamified elements and interactive design. The goal of this research is to develop a technology based on visual saliency testing to improve the performance of entertainment robots in music teaching systems, making them more intelligent and interactive. The research collected a large number of music multimedia teaching materials, and marked and analyzed them. The visual significance test method is used to determine the key points and important information in the teaching materials, and the computer vision technology is used to identify the areas of visual interest to students in real time. Through interaction with students, the system can adapt its output based on student feedback to ensure personalized exploration and teaching of students' points of interest. Finally, an interactive entertainment robot system is designed, which can perform intelligent feedback and interaction according to the significant information in music teaching materials. The results show that the entertainment robot system based on visual salience test shows higher intelligence and interactivity in music multimedia teaching, which greatly improves the teaching experience.

Frequentist, Bayesian Analysis and Complementary Statistical Tools for Geriatric and Rehabilitation Fields: Are Traditional Null-Hypothesis Significance Testing Methods Sufficient?

Null hypothesis significant testing (NHST) is the dominant statistical approach in the geriatric and rehabilitation fields. However, NHST is routinely misunderstood or misused. In this case, the findings from clinical trials would be taken as evidence of no effect, when in fact, a clinically relevant question may have a "non-significant" p-value. Conversely, findings are considered clinically relevant when significant differences are observed between groups. To assume that p-value is not an exclusive indicator of an association or the existence of an effect, researchers should be encouraged to report other statistical analysis approaches as Bayesian analysis and complementary statistical tools alongside the p-value (eg, effect size, confidence intervals, minimal clinically important difference, and magnitude-based inference) to improve interpretation of the findings of clinical trials by presenting a more efficient and comprehensive analysis. However, the focus on Bayesian analysis and secondary statistical analyses does not mean that NHST is less important. Only that, to observe a real intervention effect, researchers should use a combination of secondary statistical analyses in conjunction with NHST or Bayesian statistical analysis to reveal what p-values cannot show in the geriatric and rehabilitation studies (eg, the clinical importance of 1kg increase in handgrip strength in the intervention group of long-lived older adults compared to a control group). This paper provides potential insights for improving the interpretation of scientific data in rehabilitation and geriatric fields by utilizing Bayesian and secondary statistical analyses to better scrutinize the results of clinical trials where a p-value alone may not be appropriate to determine the efficacy of an intervention.

Research on the Reform of Higher Vocational English ESP Teaching Mode Oriented by Vocational Needs

Abstract Based on the theoretical knowledge of ESP teaching, this paper constructs a vocational demand-oriented ESP teaching model for higher vocational English and analyzes the structure and implementation principles of this teaching model in depth. The non-English major full-time higher vocational students enrolled in 2022 at a regional higher vocational college are selected as the research sample, and they are experimentally grouped. A questionnaire was used to obtain the data for analyzing the effect of the ESP teaching model of the study, and the mean IUT and standard deviation F test in the significance test method were used to test the significance of the collected data. Data summarization: compared to the English interpreting pre-test scores, the mean interpreting score of the experimental group increased by 0.469, and the mean interpreting score of the control group increased by 0.223. The t-statistic of the interpreting post-test scores was 4.105, and the corresponding p-value was 0.003<0.05. There was also a mean score of the experimental group’s English translation post-test scores that was higher than the control group’s score of 5.923, and the value of the p-value was 0.036. Overall, there is a significant difference between the control group and the experimental group in both English translation and English interpretation scores. In order to improve the final ESP teaching model, a reform strategy based on vocational needs is proposed.

A Study of the Utility of Artificial Translation in English Reading Comprehension

Abstract Human translation always has an incomparable role in the process of English learning, especially in the promotion of English reading comprehension. This paper centers on the research problem of selecting suitable research objects, combining questionnaire surveys and test methods to obtain relevant data on artificial translation and English reading comprehension. Then, it analyzes the correlation between the dimensions of artificial translation and the effect on English reading comprehension, constructs the multiple regression model of artificial intelligence on English reading comprehension, and proposes the corresponding significance test method. The results show that the correlation coefficients between the five dimensions of human translation and English reading comprehension scores are, in descending order: accuracy (0.824) > completeness (0.636) > fitness (0.579) > fluency (0.485) > consistency (0.372), which are all significantly positively correlated with English reading scores. The regression results showed that the level of manual translation dimension explained 26.7% of the variation in reading achievement. Among the human translation dimensions, accuracy, completeness, and consistency have significant positive effects on English reading comprehension. The study suggests that human translation accuracy, completeness, and consistency can be optimized to improve English reading comprehension.

Reconciling the Divides: A Dynamic Integrative Analysis of Variability and Commonality in (Pre)primary School English Development in Switzerland

The main goal of this paper is to suggest a combination of data analyses – notably generalized additive models, time-series clustering methodology, visual methods for significance testing and qualitative analyses – that relate to Complex Dynamic Systems Theory (CDST). To this end, we report on findings from a larger project conducted in a bilingual (pre)primary school in Switzerland, aiming to elucidate the complex ways that L2 English development emerges over time in 45 children who received German/English bilingual instruction over a period of eight years (age 5-12) in combination with emerging extracurricular exposure to English. The results reveal that increased extracurricular activities in English are particularly noticeable during periods of rapid development, but the effects seem temporally limited to the end stages of testing and strongly hinge on the cluster in question (i.e., learners with trajectorial similarities). We relate the findings to the “authenticity gap” between English inside and outside of school, as clusters who perceive a discrepancy between in- and out-of-school encounters with English also show rapid development that is characterized by increased English exposure during extracurricular activities. Methodological implications of adopting non-linear models, which can model complex dynamic relationships in order to better reconcile generalizability, variability, and individuality, are discussed.

MOSER: Scalable Network Motif Discovery Using Serial Test

Given a graph G , a motif (e.g., 3-node clique) is a fundamental building block for G. Recently, motif-based graph analysis has attracted much attention due to its efficacy in tasks such as clustering, ranking, and link prediction. These tasks require Network Motif Discovery (NMD) at the early stage to identify the motifs of G. However, existing NMD solutions have two drawbacks: (1) Lack of theoretical guarantees on the quality of the samples generated using the existing methods, and (2) inefficient algorithms, which are not scalable for large graphs. These limitations hinder the exploration of motifs for analyzing large graphs. To address the above issues, we propose a novel solution named MOSER ( MO tif Discovery using SER ial Test). This novel NMD framework leverages a significance testing method known as the serial test, which differs from the existing solutions. We further propose two fast incremental subgraph counting algorithms, allowing MOSER to scale to larger graphs than ever possible before. Extensive experimental results show that using MOSER can improve the state-of-the-art up to 5 orders of magnitude in efficiency and that the motifs found by MOSER facilitate downstream tasks such as link prediction.

Working Memory Training for Children Using the Adaptive, Self-Select, and Stepwise Approaches to Setting the Difficulty Level of Training Activities: Protocol for a Randomized Controlled Trial.

A common yet untested assumption of cognitive training in children is that activities should be adaptive, with difficulty adjusted to the individual's performance in order to maximize improvements on untrained tasks (known as transfer). Working memory training provides the ideal testbed to systematically examine this assumption as it is one of the most widely studied domains in the cognitive training literature, and is critical for children's learning, including following instructions and reasoning. This trial aimed to examine children's outcomes of working memory training using adaptive, self-select (child selects difficulty level), and stepwise (difficulty level increases incrementally) approaches to setting the difficulty of training activities compared to an active control condition immediately and 6-month postintervention. While the aim is exploratory, we hypothesized that children allocated to a working memory training condition would show greater improvements: (1) on near transfer measures compared to intermediate and far transfer measures and (2) immediately postintervention compared to 6-month postintervention. This double-blinded, active-controlled, parallel-group randomized trial aimed to recruit 128 children aged 7 to 11 years from 1 metropolitan primary school in Melbourne, Australia. Following baseline testing, children were randomized into 1 of 4 conditions: adaptive, self-select, or stepwise working memory training, or active control. An experimental intervention embedded in Minecraft was developed for teachers to deliver in class over 2 consecutive weeks (10 × 20-minute sessions). The working memory training comprised 2 training activities with processing demands similar to daily activities: backward span and following instructions. The control comprised creative activities. Pre- and postintervention, children completed a set of working memory tests (near and intermediate transfer) and the Raven's Standard Progressive Matrices (far transfer) to determine training outcomes, as well as motivation questionnaires to determine if motivations toward learning and the intervention were similar across conditions. Caregivers completed the ADHD-Rating Scale-5 to measure their child's attention (far transfer). Statistical analysis will include traditional null hypothesis significance testing and Bayesian methods to quantify evidence for both the null and alternative hypotheses. Data collection concluded in December 2022. Data are currently being processed and analyzed. This trial will determine whether the adaptive approach to setting the difficulty of training activities maximizes cognitive training outcomes for children. This trial has several strengths: it adopts best practices for cognitive training studies (design, methods, and analysis plan); uses a range of measures to detect discrete levels of transfer; has a 6-month postintervention assessment; is appropriately powered; and uses an experimental working memory training intervention based on our current understanding of the cognitive mechanisms of training. Findings will inform future research and design of cognitive training interventions and highlight the value of the evidence-based principles of cognitive training. Australian New Zealand Clinical Trials Registry, ACTRN12621000990820; https://www.anzctr.org.au/ACTRN12621000990820.aspx. DERR1-10.2196/47496.

An empirically adjusted weighted ordered p-values meta-analysis method for large-scale simultaneous significance testing in genomic experiments

Background Meta-analysis is a popular approach for combining results from multiple studies investigating the same questions. Meta-analysis has gained wide popularity in genomic analysis due to the availability of large volumes of genomic study results from public databases. In genomic meta-analysis, researchers, often, tend to combine p-values related to significance testing of a gene from multiple studies where thousands of genes are tested simultaneously. The traditional p-value combination approaches aim to find genes which are differentially expressed in at least one of studies. An alternative form of meta-analysis has, recently, gained popularity where the aim is to find genes that are consistently differentially expressed in a large number, possibly a majority, of studies. An approach based on weighted ordered p-values (WOP) has been developed, in the recent past, to perform the latter type of meta-analysis. Methods In this article, we discuss the limitations of the WOP meta-analysis method due to its adherence to the standard null distributional assumptions of classical meta-analysis that can lead to incorrect significance testing results. Moreover, we propose a robust meta-analysis method for simultaneous significance testing of multitude of genes that improves the WOP approach using an empirical modification. Results Through simulation studies, we demonstrate the superiority of our proposed method over the existing WOP meta-analysis by substantially reducing false discoveries of significant genes and controlling type-I error rates especially in the presence of unobserved confounding variables. We illustrate the utility of our proposed method through a variety of meta-analysis of genomic studies in different diseases.

A statistical approach for identifying primary substrates of ZSWIM8-mediated microRNA degradation in small-RNA sequencing data

BackgroundOne strategy for identifying targets of a regulatory factor is to perturb the factor and use high-throughput RNA sequencing to examine the consequences. However, distinguishing direct targets from secondary effects and experimental noise can be challenging when confounding signal is present in the background at varying levels.ResultsHere, we present a statistical modeling strategy to identify microRNAs that are primary substrates of target-directed miRNA degradation (TDMD) mediated by ZSWIM8. This method uses a bi-beta-uniform mixture (BBUM) model to separate primary from background signal components, leveraging the expectation that primary signal is restricted to upregulation and not downregulation upon loss of ZSWIM8. The BBUM model strategy retained the apparent sensitivity and specificity of the previous ad hoc approach but was more robust against outliers, achieved a more consistent stringency, and could be performed using a single cutoff of false discovery rate (FDR).ConclusionsWe developed the BBUM model, a robust statistical modeling strategy to account for background secondary signal in differential expression data. It performed well for identifying primary substrates of TDMD and should be useful for other applications in which the primary regulatory targets are only upregulated or only downregulated. The BBUM model, FDR-correction algorithm, and significance-testing methods are available as an R package at https://github.com/wyppeter/bbum.

Hyperspectral estimation of wheat stripe rust using fractional order differential equations and Gaussian process methods

Wheat stripe rust is the main cause of yield loss in winter wheat. For nondestructive monitoring of wheat stripe rust by remote sensing, a high-precision stripe rust monitoring model can be constructed so that field management can be performed rationally and environmental damage from large doses of pesticides and chemicals can be avoided. Fractional order differential (FOD) equations can be used to flexibly control the differential step size, which enhances the spectral information and reduces the background noise. In this study, the canopy hyperspectral data under the influence of wheat stripe rust were evaluated by FOD, and the disease severity level (SL) of stripe rust in the field was analyzed by evaluating the polar difference, coefficient of variation, and correlation coefficient. Subsequently, the spectral features associated with stripe rust were screened using significance tests and Gaussian process regression sigma (GPR sigma) analysis methods. Then, models for the various wheat stripe rust severity levels were established by Gaussian process regression (GPR) with different data inputs. The results showed that the 0.8–1.4 differential order could effectively improve the correlation between spectral bands and disease severity, and the optimal correlation between the 1.2 order differential spectra and wheat stripe rust severity improved by 15% compared with the original reflectance spectra. The GPR sigma band analysis method screens only 8 bands at order 1.2, the R2 between the model-predicted SL and the measured SL is improved by 13% compared to the original reflectance spectrum, and the RMSE and MAE are reduced by 34% and 39%, respectively. Compared with the significance test method, GPR sigma band analysis selected fewer bands, constructed models with higher accuracy and was more suitable for the construction of models for estimating the severity of wheat stripe rust disease.

Spatio-temporal assessment of the impacts of the trends in physical and biogeochemical parameters on the primary production of the Gulf of Guinea

This study applied ocean models data from Copernicus Marine Environment Monitoring Service (CMEMS) in assessing the impacts of the trends in key ocean parameters on the primary production of the Gulf of Guinea (GoG). Trend analyses, from 1993 to 2020, were done using linear regression and Mann-Kendall significance test methods to ascertain inter-annual and inter-seasonal variations and check the significance of the trends, respectively. Results affirm that temperature, salinity, nutrients, and oxygen play significant roles in the primary production of the GoG. Also, parameters such as temperature, salinity, chlorophyll-A, net primary production, phosphate, and dissolved oxygen have been experiencing increases between the study duration while silicate and nitrate have been declining in the GoG. However, there are regions and years with contrary values to the average trends. The varying level of significance of the trend showed that the impacts of the climate on the primary production of the GoG vary basin-wide.

Tracking Changing Evidence of Water Erosion in Ordos Plateau, China Using the Google Earth Engine

Ordos Plateau is one of the primary sources of sediment in the Yellow River, and changes in regional soil erosion directly affect the ecological status of the lower reaches of the Yellow River. Many recent studies have been published using remote sensing (RS) and geographic information systems (GIS) to evaluate soil erosion. In this study, much satellite remote sensing data in the Google Earth Engine (GEE) can better track soil erosion protection, which is significant in guiding the ecological protection and restoration of the Ordos Plateau and the Yellow River basin. In this study, we used GEE to observe the changes in soil erosion in the Ordos area from 2013 to 2021. The Theil–Sen procedure and Mann–Kendall significance test methods were used to evaluate the trend of land erosion in the Ordos area from 2013 to 2021. Based on GEE, the RUSLE is applied to evaluate soil erosion and analyze the changing trend. As a result, (1) we found that the annual change of soil and water loss in the Ordos Plateau showed three stages: 2013–2015, 2016–2018, and 2018–2021. After 2018, soil loss decreased from 14 × 1017 Mg in 2018 to 4 × 1017 Mg in 2021, which indicates that the environmental restoration project started in 2018 has achieved encouraging results. (2) The results showed that 40.9% of the regional soil erosion trend showed a significant decline, and 45.7% of the regional soil erosion trend showed a slight decline. Only 13.3% of the regional soil erosion is on the rise. (3) The test results of different land use types show that 87.3% of soil erosion occurs in bare and cultivated land. Because the terrain of Ordos is relatively flat, 95.39–96.17% of soil erosion occurs in areas with a slope of 0 to 5. (4) The reliability of the RUSLE model based on the GEE platform is proved by regression model verification of observation data and model prediction results. (5) GEE’s cloud-based features can provide data and scripts to users in developing countries which lack sufficient observation data or the necessary computing resources to develop these data. The results show that GEE has robust analysis and processing ability, can analyze a large amount of data, and can provide efficient digital products for soil erosion research.

Neuroscience Needs to Test Both Statistical and Scientific Hypotheses.

Experimental neuroscience typically uses "p-valued" statistical testing procedures (null hypothesis significance testing; NHST) in evaluating its results. The rote, often misguided, application of NHST (Gigerenzer, 2008) has led to errors and "questionable research practices." Although the problems could be avoided with better statistics training (Lakens, 2021), there have been calls to abandon NHST altogether. One suggestion is to replace NHST with "estimation statistics" (Cumming and Calin-Jageman, 2017; Calin-Jageman and Cumming, 2019). Estimation statistics emphasizes the uncertainty inherent in scientific investigations and uses metrics, e.g., confidence intervals (CIs), that draw attention to uncertainty. Besides procedural steps and methods, the Estimation Approach prefers expressing "quantitative," rather than "qualitative" conclusions and making generalizations, rather than testing scientific hypotheses. The Estimation Approach embodies a philosophy of science-its ultimate goals, experimental mindset, and specific aims-that diverges unhelpfully from what laboratory-based neuroscience needs. The Estimation Approach meshes naturally with, e.g., clinical neuroscience, drug development, human psychology, and social sciences. It fits less well with much of the neuroscience published in the Journal of Neuroscience, for example. In contrast, the philosophy behind NHST fits naturally with traditional, evaluative testing of scientific hypotheses. Finally, some Estimation Approach remedies, e.g., replication, ideally with "preregistration," are incompatible with much experimental neuroscience. This Dual Perspective essay argues that, while neuroscience can benefit from practical aspects of estimation statistics, entirely replacing conventional methods with the Estimation Approach would be a mistake. NHST testing should be retained and improved.SIGNIFICANCE STATEMENT Experimental neuroscience relies on statistical procedures to assess the meaning and importance of its research findings. Optimal scientific communication demands a common set of assumptions for expressing and evaluating results. Problems arising from misuse of conventional significance testing methods have led to a proposal to replace significance testing with an Estimation Statistics Approach. Practical elements of the Estimation Approach can usefully be incorporated into conventional methods. However, the prevailing philosophy of the Estimation Approach does not address certain important needs of much experimental neuroscience. Neuroscience should adopt beneficial elements of the Estimation Approach without giving up the advantages of significance testing.

Frequentist and Bayesian Hypothesis Testing: An Intuitive Guide for Urologists and Clinicians

AbstractGiven the limitations of frequentist method for null hypothesis significance testing, different authors recommend alternatives such as Bayesian inference. A poor understanding of both statistical frameworks is common among clinicians. The present is a gentle narrative review of the frequentist and Bayesian methods intended for physicians not familiar with mathematics. The frequentist p-value is the probability of finding a value equal to or higher than that observed in a study, assuming that the null hypothesis (H0) is true. The H0 is rejected or not based on a p threshold of 0.05, and this dichotomous approach does not express the probability that the alternative hypothesis (H1) is true. The Bayesian method calculates the probability of H1 and H0 considering prior odds and the Bayes factor (Bf). Prior odds are the researcher's belief about the probability of H1, and the Bf quantifies how consistent the data is concerning H1 and H0. The Bayesian prediction is not dichotomous but is expressed in continuous scales of the Bf and of the posterior odds. The JASP software enables the performance of both frequentist and Bayesian analyses in a friendly and intuitive way, and its application is displayed at the end of the paper. In conclusion, the frequentist method expresses how consistent the data is with H0 in terms of p-values, with no consideration of the probability of H1. The Bayesian model is a more comprehensive prediction because it quantifies in continuous scales the evidence for H1 versus H0 in terms of the Bf and the posterior odds.

A New Method of Significance Testing for Correlation-Coefficient Fields and Its Application

A New Method of Significance Testing for Correlation-Coefficient Fields and Its Application

Networks of worry\u2014towards a connectivity-based signature of late-life worry using higher criticism

Severe worry is a complex transdiagnostic phenotype independently associated with increased morbidity, including cognitive impairment and cardiovascular diseases. We investigated the neurobiological basis of worry in older adults by analyzing resting state fMRI using a large-scale network-based approach. We collected resting fMRI on 77 participants (>50 years old) with varying worry severity. We computed region-wise connectivity across the default mode network (DMN), anterior salience network, and left executive control network. All 22,366 correlations were regressed on worry severity and adjusted for age, sex, race, education, disease burden, depression, anxiety, rumination, and neuroticism. We employed higher criticism, a second-level method of significance testing for rare and weak features, to reveal the functional connectivity patterns associated with worry. The analysis suggests that worry has a complex, yet distinct signature associated with resting state functional connectivity. Intra-connectivities and inter-connectivities of the DMN comprise the dominant contribution. The anterior cingulate, temporal lobe, and thalamus are heavily represented with overwhelmingly negative association with worry. The prefrontal regions are also strongly represented with a mix of positive and negative associations with worry. Identifying the most salient connections may be useful for targeted interventions for reducing morbidity associated with severe worry in older adults.

Deep Tobit networks: A novel machine learning approach to microeconometrics

Tobit models (also called as “censored regression models” or classified as “sample selection models” in microeconometrics) have been widely applied to microeconometric problems with censored outcomes. However, due to their linear parametric settings and restrictive normality assumptions, the traditional Tobit models fail to capture the pervading nonlinearities and thus may be inadequate for microeconometric analysis with large-scale datasets. This paper proposes two novel deep neural networks for Tobit problems and explores machine learning approaches in the context of microeconometric modeling. We connect the censored outputs in Tobit models with some deep learning techniques, which are thought to be unrelated to microeconometrics, and use the rectified linear unit activation and a particularly designed network structure to implement the censored output mechanisms and realize the underlying econometric conceptions. The benchmark Tobit-I and Tobit-II models are then reformulated as two carefully designed deep feedforward neural networks named deep Tobit-I network and deep Tobit-II network, respectively. A novel significance testing method is developed based on the proposed networks. Compared with the traditional models, our networks with deep structures can effectively describe the underlying highly nonlinear relationships and achieve considerable improvements in fitting and prediction. With the novel testing method, the proposed networks enable highly accurate and sophisticated econometric analysis with minimal random assumptions. The encouraging numerical experiments on synthetic and realistic datasets demonstrate the utility and advantages of the proposed method.

The Effects of Company Image and Communication Platform Alignment on Investor Information Processing

ABSTRACTMotivated by firms' increasing use of new media technology for investor communications, we investigate how alignment between company image and communication platform affects investor judgment and decision making. In our first experiment, we demonstrate that investors expect alignment between firm image and the perception of the new media communication platform managers choose for investor relations. In a second experiment, we examine how this alignment affects investor judgment and decision making. We predict and find that greater platform-image alignment leads investors to experience subjective ease of processing, but does not change investment amounts. Additionally, we demonstrate an approach to conducting an explicit test of a null hypothesis by evaluating the convergence of null hypothesis significance testing (NHST) and Bayesian methods. Our findings have implications for researchers, firms, and investors, and add to a growing literature on new media disclosure.