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PurposeThe technology used by an organization is significantly influenced by the organization's preferred competitive capabilities. The Internet of things (IoT) is an important technology, which is implemented by most prominent business organizations. The purpose of this paper is to investigate the relationship between an organization's strategies and the IoT architectures implemented by the organization.Design/methodology/approachThis study has been carried out on primary data collected with the help of a structured questionnaire. The data have been analyzed by statistical techniques like cluster analysis and discriminant analysis through SPSS.FindingsThe empirical investigation of data revealed that there is a relationship between organizational strategy and IoT architectures. The three-layered architecture of the IoT is most suitable for caretakers; the three-, four- or five- layered architectures are suitable for marketeers; whereas innovators find it more suitable to use five- or more-layered architecture of the IoT. This paper draws the conclusion based on maximum likelihood rather than using statistical analyses like ANOVA. The idea behind using the maximum likelihood estimate is that there are many subjective parameters in deciding the architectures of the IoT. These subjective parameters are difficult to quantify, so it is not possible to apply ANOVA on these parameters.Research limitations/implicationsThis study considers three organizational strategies; the relationship between other organizational strategies and IoT architecture will be studied in future.Practical implicationsThis study offers multiple opportunities to practitioners and consulting firms of the IoT to adopt a suitable IoT architecture according to the organizational strategy. This study equips IoT development engineers to select suitable technology for data capturing, data transmission, and data management and access for an IoT architecture.Originality/valueAlthough a lot of work has already been done on the architecture of IoT for different industries and businesses, to the best of our knowledge, this is the first study that relates organizational strategies to IoT architectures. This study applies to all the major industry types.

There is evidence of an association between posttraumatic stress disorder (PTSD) and criminal justice involvement among military veterans. For this study, we systematically reviewed the literature to examine the association between PTSD and criminal justice involvement among military veterans, assess the magnitude of this association, and identify strengths and limitations of the underlying evidence. Five databases were searched for a larger scoping review, and observational studies that assessed PTSD and criminal justice involvement were selected from the scoping review database (N = 191). Meta-analyses were conducted, pooling odds ratios (ORs) via restricted maximum likelihood random-effects models. The main outcomes were criminal justice involvement (i.e., documentation of arrest) and PTSD (i.e., PTSD assessment score indicating probable PTSD). Of 143 unique articles identified, 10 studies were eligible for the meta-analysis. Veterans with PTSD had higher odds of criminal justice involvement (OR = 1.61, 95% CI [1.16, 2.23], p = .002) and arrest for violent offenses (OR = 1.59, 95% CI [1.15, 2.19], p = .002) compared to veterans without PTSD. The odds ratio of criminal justice involvement among military veterans with PTSD assessed using the PTSD Checklist was 1.98, 95% CI [1.08, 3.63], p = .014. Considerable heterogeneity was identified, but no evidence of publication bias was found. Criminal justice involvement and PTSD are linked among military veterans, highlighting an important need for clinicians and healthcare systems working with this population to prioritize PTSD treatment to reduce veterans' new and recurring risk of criminal justice involvement.

Fundação para a Ciência e a Tecnologia (FCT) ; ALBAN ; European Union ; FCT: SFRH/BD/47821/2008 ; European Union: E07D402597BR ; We evaluated genetic and environmental factors affecting age at first farrowing of sows in the Brazilian southeast. For this purpose, 466 observations regarding the age at first farrowing were made for Dalland-C40 (c) animals belonging to two herds. The effects of the environmental factors on this trait were assessed by means of a model that included, as random effects, the influence of the sow's father and mother and, as fixed effects, the influence the year of birth, the herd and the birth season, along with the covariable litter size at birth. The variance components were estimated using the derivative-free restricted maximum likelihood method. The estimated mean was 354.8 +/- 25.87 days, with a coefficient of variation of 7.29%. Significant effects on the trait were observed for the herd, the year and the season of birth; but a linear effect of litter size at birth on the age at first farrowing was not observed. The boar did not significantly contribute to the variation occurring among the sows, whereas the sow's mother caused significant variation. The heritability estimate for the age at first farrowing was 0.44 +/- 0.15, which is considered high. We concluded that herd effect and year and season of birth should be taken into consideration for an accurate genetic comparison; consequently, the animals should be joined into contemporary groups.

This article scrutinises the repercussion of the regional trade agreements on Indian exports. The gravity model has been used for the analysis of two decades of panel data from 30 countries, spanning from 2001 to 2019. The gravity equation is estimated by the Ordinary Least Square (OLS) and Poisson pseudo-maximum likelihood (PPML) techniques using panel data regression. The PPML approach addresses the heteroskedasticity bias that is typically present in the OLS method. The study has revealed that the PPML results are more encouraging than those obtained based on the traditional OLS approach. According to the empirical findings, India's trading partners in South Asia benefit more from its economic integration than India does. However, the measurement of country-specific degrees of globalisation has a negligible effect on fostering trade across select nations. JEL Codes: C1, F1, F2, F6

We propose a Kronecker product model for correlation or covariance matrices in the large dimensional case. The number of parameters of the model increases logarithmically with the dimension of the matrix. We propose a minimum distance (MD) estimator based on a log-linear property of the model, as well as a one-step estimator, which is a one-step approximation to the quasi-maximum likelihood estimator (QMLE). We establish rates of convergence and central limit theorems (CLT) for our estimators in the large dimensional case. A specification test and tools for Kronecker product model selection and inference are provided. In a Monte Carlo study where a Kronecker product model is correctly specified, our estimators exhibit superior performance. In an empirical application to portfolio choice for S&P500 daily returns, we demonstrate that certain Kronecker product models are good approximations to the general covariance matrix.

ABSTRACTIn this paper we propose ridge regression estimators for probit models since the commonly applied maximum likelihood (ML) method is sensitive to multicollinearity. An extensive Monte Carlo study is conducted where the performance of the ML method and the probit ridge regression (PRR) is investigated when the data are collinear. In the simulation study we evaluate a number of methods of estimating the ridge parameter k that have recently been developed for use in linear regression analysis. The results from the simulation study show that there is at least one group of the estimators of k that regularly has a lower mean squared error than the ML method for all different situations that have been evaluated. Finally, we show the benefit of the new method using the classical Dehejia and Wahba dataset which is based on a labour market experiment.

Sub‐Saharan African countries have traditionally lagged the rest of the developing world in terms of overall trade relative to gross domestic product. But, there is growing interest among these countries to initiate trade policies and improve quality of institutions as a way to promote trade and boost foreign direct investment. This article extends the gravity model of trade to include proxies for trade reform policy and institutional quality among the 15 countries of the Economic Community of West African States (ECOWAS) for data spanning 1984–2006. Alternative methods of estimation based on ordinary least squares, Heckman two‐step procedure, and Poisson pseudo‐maximum likelihood produce predictions that are consistent with the standard gravity model. They further highlight the evidence of restrictive trade policies and weak institutions that contribute to the failure of ECOWAS countries to boost bilateral trade. (JELF13, F15, O19, O55)

This article examines fractional processes as alternatives to random walks in emerging foreign exchange rate markets. Sowell's (1992) joint maximum likelihood is used to estimate the ARFIMA parameters and test for random walks. The results show that, in most cases, the emerging market exchange rates follow fractionally integrated processes. Forecasts of exchange rates based on the fractionally integrated autoregressive moving average models are compared to those from the benchmark random walk models. A Harvey, Leybourne and Newbold (1997) test of equality of forecast performance indicates that the ARFIMA forecasts are more efficient in the multi-step-ahead forecasts than the random walk model forecasts. The presence of fractional integration is seen to be associated with market inefficiency in the exchange markets examined. The evidence suggests that fractional integrated processes are viable alternatives to random walks for describing and forecasting exchange rates in the emerging markets.

This article proposes and tests a three-dimension, shortened instrument for public service motivation based on Perry's (1996) exploratory 24-item scale. A confirmatory factor analysis is used with both a diagonally weighted least squares and a modified version of maximum likelihood estimation that are more appropriate for ordinal indicators. Data from the National Administrative Studies Project, consisting of mail surveys of managers engaged in information management activities working in state-level primary health and human service agencies, is used ( n = 274; response rate = 53%). Results indicate good support for the shortened scale compared to Perry's original work. Findings corroborate Perry's theorized dimensions and items in the exploratory instrument. An exploratory analysis evaluating modifications to the tested model suggests possible improvements in two dimensions but does not undermine the overall, supportive results and suggests the importance of continued item and scale development.

A resource selection probability function is a function that gives the prob-
ability that a resource unit (e.g., a plot of land) that is described by a set of habitat
variables X1 to Xp will be used by an animal or group of animals in a certain period of
time. The estimation of a resource selection function is usually based on the comparison
of a sample of resource units used by an animal with a sample of the resource units
that were available for use, with both samples being assumed to be effectively randomly
selected from the relevant populations. In this paper the possibility of using a modified
sampling scheme is examined, with the used units obtained by line transect sampling. A
logistic regression type of model is proposed, with estimation by conditional maximum
likelihood. A simulation study indicates that the proposed method should be useful in
practice.

AbstractIn this article we study the estimation of the average excess life θ in a two‐parameter exponential distribution with a known linear relationship between α (the minimum life) and θ of the form α = aθ, where a is known and positive. A comparison of the efficiencies of estimators which are linear combinations of the smallest sample value and the sample sum of deviations from the smallest sample value and the maximum likelihood estimators is made for various sample sizes and different values of a. It is shown that these estimators are dominated in the risk by the minimum‐risk scale equivariant estimator based on sufficient statistics. A class of Bayes estimators for inverted gamma priors is constructed and shown to include a minimum‐risk scale equivariant estimator in it. All the members of this class can be computed easily.

The exchange rate is one of the main components that represents the status of a country's economic condition. This study mainly focuses on identifying an appropriate distribution that can capture the asymmetric behaviour of important currency exchange rates related to Sri Lanka. The true behaviour of the exchange rates of USD, EURO, JPY, AUD, GBP, CHF, SGD and CAD in terms of LKR was determined using flexible distributions of generalised lambda distribution (GLD), Normal Inverse Gaussian and Skew-Normal. The parameter estimation was carried out by maximum likelihood estimation (MLE), while for GLD, few more parameter estimation techniques were applied. To understand how well the fitted distributions identify the real behaviour of data, we conducted the goodness-of-fit (GOF) tests. The results from Cramer–Von Mises (CvM) and Anderson–Darling (AD) GOF tests pointed out that the exchange rates data follow a GLD with the parameter estimation of maximum product of spacings (MPS). Further, plots of histograms with theoretical densities, cumulative distribution functions (CDFs) with empirical CDF and Quantile–Quantile plots illustrated the same idea. This study presented the findings based on different suitable parameter estimation techniques and this study is the first investigation on the distribution identification of exchange rates in Sri Lanka. JEL Codes: C12,C13,G10

Abstract. It is necessary to evaluate aggregate damage probability to multiple buildings when performing probabilistic risk assessment for the buildings. The purpose of this study is to demonstrate a method of tsunami hazard and risk assessment for two buildings far away from each other, using copulas of tsunami hazards that consider the nonlinear spatial correlation of tsunami wave heights. First, we simulated the wave heights considering uncertainty by varying the slip amount and fault depths. The frequency distributions of the wave heights were evaluated via the response surface method. Based on the distributions and numerically simulated wave heights, we estimated the optimal copula via maximum likelihood estimation. Subsequently, we evaluated the joint distributions of the wave heights and the aggregate damage probabilities via the marginal distributions and the estimated copulas. As a result, the aggregate damage probability of the 99th percentile value was approximately 1.0 % higher and the maximum value was approximately 3.0 % higher while considering the wave height correlation. We clearly showed the usefulness of copula modeling considering the wave height correlation in evaluating the probabilistic risk of multiple buildings. We only demonstrated the risk evaluation method for two buildings, but the effect of the wave height correlation on the results is expected to increase if more points are targeted.

Recent research shows that the efforts to limit climate change should focus on reducing the emissions of carbon dioxide over other greenhouse gases or air pollutants. Many countries are paying substantial attention to carbon emissions to improve air quality and public health. The largest source of carbon emissions from human activities in some countries in Europe and elsewhere is from burning fossil fuels for electricity, heat, and transportation. The prices of fuel and carbon emissions can influence each other. Owing to the importance of carbon emissions and their connection to fossil fuels, and the possibility of [1] Granger (1980) causality in spot and futures prices, returns, and volatility of carbon emissions, crude oil and coal have recently become very important research topics. For the USA, daily spot and futures prices are available for crude oil and coal, but there are no daily futures prices for carbon emissions. For the European Union (EU), there are no daily spot prices for coal or carbon emissions, but there are daily futures prices for crude oil, coal and carbon emissions. For this reason, daily prices will be used to analyse Granger causality and volatility spillovers in spot and futures prices of carbon emissions, crude oil, and coal. As the estimators are based on quasi-maximum likelihood estimators (QMLE) under the incorrect assumption of a normal distribution, we modify the likelihood ratio (LR) test to a quasi-likelihood ratio test (QLR) to test the multivariate conditional volatility Diagonal BEKK model, which estimates and tests volatility spillovers, and has valid regularity conditions and asymptotic properties, against the alternative Full BEKK model, which also estimates volatility spillovers, but has valid regularity conditions and asymptotic properties only under the null hypothesis of zero off-diagonal elements. Dynamic hedging strategies by using optimal hedge ratios are suggested to analyse market fluctuations in the spot and futures returns and volatility of carbon emissions, crude oil, and coal prices.