This study compares reconciliation techniques and base forecast methods to forecast a hierarchical time series of the number of fire spots in Brazil between 2011 and 2022. A three-level hierarchical time series was considered, comprising fire spots in Brazil, disaggregated by biome, and further disaggregated by the municipality. The autoregressive integrated moving average (ARIMA), the exponential smoothing (ETS), and the Prophet models were tested for baseline forecasts, and nine reconciliation approaches, including top-down, bottom-up, middle-out, and optimal combination methods, were considered to ensure coherence in the forecasts. Due to the need for transformation to ensure positive forecasts, two data transformations were considered: the logarithm of the number of fire spots plus one and the square root of the number of fire spots plus 0.5. To assess forecast accuracy, the data were split into training data for estimating model parameters and test data for evaluating forecast accuracy. The results show that the ARIMA model with the logarithmic transformation provides overall better forecast accuracy. The BU, MinT(s), and WLS(v) yielded the best results among the reconciliation techniques.

The two-parameter logistic (2PL) item response theory model is a statistical model for analyzing multivariate binary data. In this article, two groups are brought onto a common metric using the 2PL model using linking methods. The linking methods of mean–mean linking, mean–geometric–mean linking, and Haebara linking are investigated in nonrobust and robust specifications in the presence of differential item functioning (DIF). M-estimation theory is applied to derive linking errors for the studied linking methods. However, estimated linking errors are prone to sampling error in estimated item parameters, thus resulting in artificially increased the linking error estimates in finite samples. For this reason, a bias-corrected linking error estimate is proposed. The usefulness of the modified linking error estimate is demonstrated in a simulation study. It is shown that a simultaneous assessment of the standard error and linking error in a total error must be conducted to obtain valid statistical inference. In the computation of the total error, using the bias-corrected linking error estimate instead of the usually employed linking error provides more accurate coverage rates.

The use of cure-rate survival models has grown in recent years. Even so, proposals to perform the goodness of fit of these models have not been so frequent. However, residual analysis can be used to check the adequacy of a fitted regression model. In this context, we provide Cox–Snell residuals for Poisson-exponentiated Weibull regression with cure fraction. We developed several simulations under different scenarios for studying the distributions of these residuals. They were applied to a melanoma dataset for illustrative purposes.

By collecting and expanding several numerical recipes developed in previous work, we implement an object-oriented Python code, based on the networkX library, for the realization of the configuration model and Newman rewiring. The software can be applied to any kind of network and “target” correlations, but it is tested with focus on scale-free networks and assortative correlations. In order to generate the degree sequence we use the method of “random hubs”, which gives networks with minimal fluctuations. For the assortative rewiring we use the simple Vazquez-Weigt matrix as a test in the case of random networks; since it does not appear to be effective in the case of scale-free networks, we subsequently turn to another recipe which generates matrices with decreasing off-diagonal elements. The rewiring procedure is also important at the theoretical level, in order to test which types of statistically acceptable correlations can actually be realized in concrete networks. From the point of view of applications, its main use is in the construction of correlated networks for the solution of dynamical or diffusion processes through an analysis of the evolution of single nodes, i.e., beyond the Heterogeneous Mean Field approximation. As an example, we report on an application to the Bass diffusion model, with calculations of the time tmax of the diffusion peak. The same networks can additionally be exported in environments for agent-based simulations like NetLogo.

Discrete choice models (DCMs) are applied in many fields and in the statistical modelling of consumer behavior. This paper focuses on a form of choice experiment, best–worst scaling in discrete choice experiments (DCEs), and the transition probability of a choice of a consumer over time. The analysis was conducted by using simulated data (choice pairs) based on data from Flynn’s (2007) ‘Quality of Life Experiment’. Most of the traditional approaches assume the choice alternatives are mutually exclusive over time, which is a questionable assumption. We introduced a new copula-based model (CO-CUB) for the transition probability, which can handle the dependent structure of best–worst choices while applying a very practical constraint. We used a conditional logit model to calculate the utility at consecutive time points and spread it to future time points under dynamic programming. We suggest that the CO-CUB transition probability algorithm is a novel way to analyze and predict choices in future time points by expressing human choice behavior. The numerical results inform decision making, help formulate strategy and learning algorithms under dynamic utility in time for best–worst DCEs.

The concept of sustainable development appeared as a response to the attempt to improve the quality of human life, simultaneously with the preservation of the environment. For this reason, two of the 17 Sustainable Development Goals are dedicated to life below water (SDG14) and on land (SDG15). In the course of this research, comprehensive information on the extent of degradation in Romania’s primary ecosystems was furnished, along with an exploration of the key factors precipitating this phenomenon. This investigation delves into the perspectives of 42 counties, scrutinizing the level of degradation in forest ecosystems, grasslands, lakes and rivers. The analysis commences with a presentation of descriptive statistics pertaining to each scrutinized system, followed by an elucidation of the primary causes contributing to its degradation. Subsequently, a cluster analysis is conducted on the counties of the country. One of these causes is the presence of intense industrial activity in certain areas, so it is even more important to accelerate the transition to a green economy in order to help the environment regenerate.

Although large data sets are generally viewed as advantageous for their ability to provide more precise and reliable evidence, it is often overlooked that these benefits are contingent upon certain conditions being met. The primary condition is the approximate validity (statistical adequacy) of the probabilistic assumptions comprising the statistical model Mθ(x) applied to the data. In the case of a statistically adequate Mθ(x) and a given significance level α, as n increases, the power of a test increases, and the p-value decreases due to the inherent trade-off between type I and type II error probabilities in frequentist testing. This trade-off raises concerns about the reliability of declaring ‘statistical significance’ based on conventional significance levels when n is exceptionally large. To address this issue, the author proposes that a principled approach, in the form of post-data severity (SEV) evaluation, be employed. The SEV evaluation represents a post-data error probability that converts unduly data-specific ‘accept/reject H0 results’ into evidence either supporting or contradicting inferential claims regarding the parameters of interest. This approach offers a more nuanced and robust perspective in navigating the challenges posed by the large n problem.

The time-between-events idea is commonly used for monitoring high-quality processes. This study aims to monitor the increase and/or decrease in the process mean rapidly using a one-sided exponentially weighted moving average (EWMA) chart for the detection of upward or downward mean shifts using a truncated gamma distribution. The use of the truncation method helps to enhance and improve the sensitivity of the proposed chart. The performance of the proposed chart with known and estimated parameters is analyzed by using the run length properties, including the average run length (ARL) and standard deviation run length (SDRL), through extensive Monte Carlo simulation. The numerical results show that the proposed scheme is more sensitive than the existing ones. Finally, the chart is implemented in real-world situations to highlight the significance of the proposed chart.

Based upon the vector representation of complex numbers and the vector exponential function, we introduce the vector representation of characteristic functions and consider some of its elementary properties such as its polar representation and a vector power expansion.

Effective sample size (ESS) consists of an equivalent number of sampling units of a georeferenced variable that would produce the same sampling error, as it considers the information that each georeferenced sampling unit contains about itself as well as in relation to its neighboring sampling units. This measure can provide useful information in the planning of future georeferenced sampling for spatial variability experiments. The objective of this article was to develop a bivariate methodology for ESS (ESSbi), considering the bivariate Gaussian common component model (BGCCM), which accounts both for the spatial correlation between the two variables and for the individual spatial association. All properties affecting the univariate methodology were verified for ESSbi using simulation studies or algebraic methods, including scenarios to verify the impact of the BGCCM common range parameter on the estimated ESSbi values. ESSbi was applied to real organic matter (OM) and sum of bases (SB) data from an agricultural area. The study found that 60% of the sample observations of the OM–SB pair contained spatially redundant information. The reduced sample configuration proved efficient by preserving spatial variability when comparing the original and reduced OM maps, using SB as a covariate. The Tau concordance index confirmed moderate accuracy between the maps.