Theory Of Inference Research Articles

Many Hands Make New Design? Exploring Consumers’ Design Newness Perceptions of Crowdsourced Products

This research builds on consumer inference theory to examine consumers’ design newness perceptions of crowdsourced products. Over the course of two experimental studies, we find that consumers perceive higher levels of design newness for products that are labeled as crowdsourced (selected and/or designed by users). In turn, these positive design newness perceptions increase consumers’ purchase intentions. Furthermore, our findings reveal that the design newness effect of crowdsourcing exists for both products with an aesthetic focus and products with a functional innovation focus. By examining the role of product design newness, these findings help us to further understand the underlying mechanisms driving consumers to value crowdsourced products.

Do Socially Responsible Firms Walk the Talk? CSR Performance and Defined Benefit Pension Policies

This study examines whether socially responsible firms are more likely to have well-funded employee pension programs. It also examines the relationship between corporate social responsibility (CSR) performance and management discretionary choice of pension accounting assumptions. Signaling theory suggests that managers use CSR performance as a credible signal of firms’ commitment to engage in socially responsible business practices to the stakeholder. Correspondent inference theory suggests that the stakeholder attributes CSR performance to firms’ reputation of serving the interests of stakeholder. To protect their reputation, firms must take actions that are consistent with the signal they send via firms’ CSR commitment. Maintaining responsible employee pension policies is a socially responsible action because the defined benefit plan is voluntary and employers have to bear greater investment risk and cost associated with the plan than the alternative retirement plan. Using panel data of 13,099 firms-years across 1,428 U.S. firms from 1992 to 2015, this study finds that firms with higher CSR scores are less likely to have underfunded pension and they report higher pension net assets than their counterparts. More socially responsible firms also adopt more responsible (i.e., conservative) pension accounting assumptions (discount rate and rate of compensation increase) to compute estimated pension benefit obligations.

Psychological Test System and Signal Processing Algorithm for Intelligent Wheelchair

Intelligent wheelchair based on BCI is a hot spot in recent research, and brain activity is actually a reflection of psychological activity. Completing the design of psychological test system is the key to BCI. ERP reflects the psychological activity of the brain, is a psychological test good tools such as cognitive neuroscience research. And there is psychological test system is mostly based on the traditional theory of external behavior inference, unable to objectively evaluate people’s psychological activity. In the study of psychological tests and event related potential theory, the article improved FastICA algorithm, and applied to feature extraction of ERP. Eventually set up a complete set of psychological test system. Experiments show that the system can effectively detect the ERP feature extraction, and faster, better than other conventional psychological test system. So as to lay the foundation for the completion of the design of Intelligent wheelchair.

Affective experience in the predictive mind: a review and new integrative account

This paper aims to offer an account of affective experiences within Predictive Processing, a novel framework that considers the brain to be a dynamical, hierarchical, Bayesian hypothesis-testing mechanism. We begin by outlining a set of common features of affective experiences (or feelings) that a PP-theory should aim to explain: feelings are conscious, they have valence, they motivate behaviour, and they are intentional states with particular and formal objects. We then review existing theories of affective experiences within Predictive Processing and delineate two families of theories: Interoceptive Inference Theories (which state that feelings are determined by interoceptive predictions) and Error Dynamics Theories (which state that feelings are determined by properties of error dynamics). We highlight the strengths and shortcomings of each family of theories and develop a synthesis: the Affective Inference Theory. Affective Inference Theory claims that valence corresponds to the expected rate of prediction error reduction. In turn, the particular object of a feeling is the object predicted to be the most likely cause of expected changes in prediction error rate, and the formal object of a feeling is a predictive model of the expected changes in prediction error rate caused by a given particular object. Finally, our theory shows how affective experiences bias action selection, directing the organism towards allostasis and towards optimal levels of uncertainty in order to minimise prediction error over time.

A STATISTICAL METHODOLOGY FOR ASSESSING THE MAXIMAL STRENGTH OF TAIL DEPENDENCE

AbstractSeveral diagonal-based tail dependence indices have been suggested in the literature to quantify tail dependence. They have well-developed statistical inference theories but tend to underestimate tail dependence. For those problems when assessing the maximal strength of dependence is important (e.g., co-movements of financial instruments), the maximal tail dependence index was introduced, but it has so far lacked empirical estimators and statistical inference results, thus hindering its practical use. In the present paper, we suggest an empirical estimator for the index, explore its statistical properties, and illustrate its performance on simulated data.

A Hypersequent Solution to the Inferentialist Problem of Modality

The standard inferentialist approaches to modal logic tend to suffer from not being able to uniquely characterize the modal operators, require that introduction and elimination rules be interdefined, or rely on the introduction of possible-world like indexes into the object language itself. In this paper I introduce a hypersequent calculus that is flexible enough to capture many of the standard modal logics and does not suffer from the above problems. It is therefore an ideal candidate to underwrite an inferentialist theory of meaning for modal operators. Here I treat specifically the modal logics K, D, T, S4, B, and S5. I show that the calculi are adequate for each set of models, and show that they meet a large set of criteria that are generally thought necessary for a calculus to underwrite a theory of meaning.

Random multi-scale kernel-based Bayesian distribution regression learning

The effective embedding estimation of distribution and the construction of regression model with strong representation ability are two key problems of distribution regression. This paper proposes a random multi-scale kernel-based Bayesian distribution regression (RMK-BDR) learning framework. Vector-valued kernel mean embedding (KME) estimators with a same dimension which is chosen adaptively to the data are introduced in the first stage of distribution regression learning. Then, a linear combination of multi-scale Gaussian kernels with different scale parameters randomly sampled from a predefined distribution is used as the regression model. Sparsity priors are added on those linear combination weights. Under the Bayesian inference theory, a prediction distribution of the response variable is obtained. A series of experiment results verify the effectiveness of the proposed algorithm.

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Bayesian inference of solid-liquid interfacial properties out of equilibrium.

Solid-liquid interfacial properties out of equilibrium provide the essential information required for understanding and controlling solidification microstructures in metallic materials. However, few studies have attempted to reveal all interfacial properties out of equilibrium in detail. The present study proposes an approach for simultaneously estimating all interfacial properties in a pure metal below the melting point on the basis of the Bayesian inference theory. The solid-liquid interfacial energy, interfacial mobility, and anisotropy parameters in pure Fe are estimated by combining molecular dynamics simulation with phase-field simulation using an ensemble Kalman filter, which is a data assimilation technique. Furthermore, the temperature dependences of all interfacial parameters are computed and discussed. In summary, the proposed multiscale approach integrates atomistic and microstructural simulations within the framework of data science and it has considerable potential for a wide variety of applications in materials engineering.

A New Robust Inference for Predictive Quantile Regression

For predictive quantile regressions with highly persistent regressors, a conventional test statistic suffers from a serious size distortion and its limiting distribution relies on the unknown persistence degree of predictors. This paper proposes a double-weighted approach to offer a robust inferential theory across all types of persistent regressors. We first estimate a quantile regression with an auxiliary regressor, which is generated as a weighted combination of an exogenous random walk process and a bounded transformation of the original regressor. With a similar spirit of rotation in factor analysis, one can then construct a weighted estimator using the estimated coefficients of the original predictor and the auxiliary regressor. Under some mild conditions, it shows that the self-normalized test statistic based on the weighted estimator converges to a standard normal distribution. Our new approach enjoys a nice property that it can reach the local power under the optimal rate T with nonstationary predictor and squared root of T for stationary predictor, respectively. More importantly, our approach can be easily used to characterize mixed persistence degrees in multiple regressions. Simulations and empirical studies are provided to demonstrate the effectiveness of the newly proposed approach. The heterogenous predictability of US stock returns at different quantile levels is reexamined.

Research on Design and Implementation of Knowledge Map Storage Access System Based on Ontology Reasoning

In order to support the intelligent combat capability of future wars, the network information system needs in-depth research and exploration in the field of artificial intelligence. This paper designs a distributed aggregation storage mode with the characteristics of balanced storage load distribution and local node aggregation storage under Big Table model. A distributed parallel query engine that uses the Group-By mode to distribute parallel computation query trees. A knowledge map completion method based on Bayesian inference is proposed. Bayesian probabilistic inference theory and RDF implication inference rules are used to jointly infer the potential relationships between entity nodes to predict the relationship between new nodes and original nodes, thus improving the mining efficiency of potential factors in the model and the accuracy of prediction of unknown relationships. Based on Big Table model, the entity sets stored row by row are evenly divided and stored through random prefix and pre-partition operations to realize load balancing. At the same time, random prefixes can be uniformly distributed to nodes for storing entities of the same type, and aggregated by entity category on a single node. With the continuous development of knowledge map technology, future knowledge map learning and reasoning technology can be integrated with new fields such as machine depth learning, cloud computing, block chain, big data, biological genetic engineering, etc. to play an important social value.

Inferential theory for heterogeneity and cointegration in large panels

This paper provides an estimation and testing framework to assess the presence and the extent of slope heterogeneity and cointegration when the units are a mixture of spurious and/or cointegrating regressions. We propose two moment estimators for the degree of heterogeneity (measured by the dispersion of the slope coefficients around their average) and for the fraction of spurious regressions, which are found to be consistent in the whole parameter space. Based on these estimators, two tests for the null hypotheses of slope homogeneity and for cointegration are proposed. Monte Carlo simulations show that both tests have the correct size and satisfactory power.

De-Biased Graphical Lasso for High-Frequency Data.

This paper develops a new statistical inference theory for the precision matrix of high-frequency data in a high-dimensional setting. The focus is not only on point estimation but also on interval estimation and hypothesis testing for entries of the precision matrix. To accomplish this purpose, we establish an abstract asymptotic theory for the weighted graphical Lasso and its de-biased version without specifying the form of the initial covariance estimator. We also extend the scope of the theory to the case that a known factor structure is present in the data. The developed theory is applied to the concrete situation where we can use the realized covariance matrix as the initial covariance estimator, and we obtain a feasible asymptotic distribution theory to construct (simultaneous) confidence intervals and (multiple) testing procedures for entries of the precision matrix.

Bayesian gates for reliable logical operations under noisy conditions.

The reliability of logical operations is indispensable for the reliable operation of computational systems. Since the downsizing of microfabrication generates nonnegligible noise in these systems, a new approach for designing noise-immune gates is required. In this paper, we demonstrate that noise-immune gates can be designed by combining Bayesian inference theory with the idea of computation over noisy channels. To reveal their practical advantages, the performance of these gates is evaluated in comparison with a stochastic resonance-based gate proposed previously. We also demonstrate that, in a high noise-level situation, this approach for computation can be better than a conventional one that conducts information transmission and computation separately.

Trusting Others: A Pareto Distribution of Source and Message Credibility Among News Reporters

This study uses the case study of journalists to explore the socio-cognitive nature of interpersonal trust in growingly deceptive ecosystems. Journalists are ideal test subjects to explore these issues as professional trust allocators, who receive immediate feedback on right and wrong trust decisions. The study differentiates, for the first time, between source and message credibility evaluations, based on a combination of qualitative and quantitative methods. Findings show that journalists can distinguish source and message credibility. However, in practice they rely on source evaluations as an “autopilot” default mode, shifting gears to observations of source and message credibility in epistemically complex cases. The proportion between both is close to Pareto distribution. This extreme division challenges both inductive and mixed inference theories of epistemic trust and suggests revisiting the “typification” doctrine of newswork. Data partially support the hegemony and “epistemic injustice” theory, showing that traditional credibility criteria might trigger the exclusion of nontraditional voices.

The effect of sustainable certification reputation on consumer behavior in the fashion industry: Focusing on the mechanism of congruence

ABSTRACTA sustainable certification is a crucial environmental cue used by consumers when they have little information on a focal product. Based on categorization inference theory, this study investigates how certification reputation can influence consumer behavior toward sustainable products. The data were gathered from consumers who experienced organic cosmetics and analyzed through structural equation modeling. As a result, certification reputation was found to have a positive influence on the congruence between product and certification, certification trust, and attitude toward product. Congruence between product and certification has a positive influence on certification trust, which predicts attitude toward product and purchase intention. Finally, the effect of certification trust on attitude toward product is mediated sequentially by the congruence between product, certification, and certification trust. This research extends the concept of sustainable certification into the fashion industry, which will enable companies with a sustainable certification to leverage their products.

A Statistical Foundation for Assessing the Maximal Strength of Tail Dependence

Several diagonal-based tail dependence indices have been suggested in the literature to quantify tail dependence. They have well-developed statistical inference theories but tend to underestimate tail dependence. For those problems when assessing the maximal strength of dependence is important (e.g., co-movements of financial instruments), the maximal tail dependence index was introduced, but it has so far lacked empirical estimators and statistical inference results, thus hindering its practical use. In the present paper we suggest an empirical estimator for the index, explore its statistical properties, and illustrate its performance on simulated data.

Unravelling the Neurobiology of Interoceptive Inference.

What is the function of the insular cortex? Two recent studies (by Gehrlach et al. and Livneh et al.) offer new insight into how this enigmatic brain region integrates interoception and exteroception in the service of emotion. The findings provide much needed causal evidence for the nascent theory of interoceptive inference.

Statistical inferences for price staleness

This paper proposes a nonparametric theory for statistical inferences on zero returns of high-frequency asset prices. Using an infill asymptotic design, we derive limit theorems for the percentage of zero returns observed on a finite time interval and for other related quantities. Within this framework, we develop two nonparametric tests. First, we test whether intra-day zero returns are independent and identically distributed. Second, we test whether intra-day variation of the likelihood of occurrence of zero returns can be solely explained by a deterministic diurnal pattern. In an empirical application to ten representative stocks of the NYSE, we provide evidence that the null of independent and identically distributed intra-day zero returns can be conclusively rejected. We further find that a deterministic diurnal pattern is not sufficient to explain the intra-day variability of the distribution of zero returns.

Bootstrapping Laplace Transforms of Volatility

This paper studies inference for the realized Laplace transform (RLT) of volatility in a fixed-span setting using bootstrap methods. Specifically, since standard wild bootstraps provide inconsistent inference, we propose local Gaussian (LG) and modified wild (MW) bootstrap procedures, and establish their first-order asymptotic validity. Moreover, motivated by its superior finite sample performance in simulations, we use Edgeworth expansions to show that the LG inference achieves second-order asymptotic refinements. To further broaden the scope of the bootstraps, we provide new Laplace transform-based estimators of the spot variance as well as the covariance, correlation and beta between two semimartingales, and adapt our inference procedures to the requisite scenario. We establish central limit theory for our estimators and show first-order asymptotic validity of their associated bootstraps. Not surprisingly, a Monte Carlo study shows that the LG bootstrap outperforms the MW bootstrap and existing (first-order) feasible inference theory in finite samples. Moreover, it demonstrates that our new spot measure estimators and inference procedures are very accurate. Finally, we illustrate the use of the new methods by examining the volatility of, and the coherence between, stocks and bonds during the large equity sell-off in December 2018.