The main goal of seed storage is to maintain its quality from harvesting to sowing time. Among all factors, storage temperature and seed moisture content are the most important factors affecting seed longevity. This experiment was conducted at University of Tehran, Department of Agronomy and Plant Breeding during 1394 to determine the Ellis and Roberts deterioration model of Chenopodium quinoa seeds and introduce constants of viability equation. Seed viability and initial moisture content was measured and after that seeds were adjusted to 5, 9, 13 and 17% moisture content and sealed hermetically in Nano packets. Storage temperatures were 5, 15, 25 and 35˚ C. The interval of sampling depended on the storage conditions. Seed viability constants were estimated to predict seed longevity in this species and relationship between sigma and moisture content and temperatures was determined. After probit analysis, survival curves were depicted in each condition. Results showed that seeds with 5% moisture content stored at 5° C had the highest germination percentage, and after 8 months seed viability decreased from 98% to 94%. But, seeds which were stored at 17% moisture content and 25 and 35° C had the highest deterioration rate. Estimates of KE, CW, CH and CQ were 2. 93, 0. 51, 0. 019 and 0. 00031, respectively. Also, the results showed that seed longevity decrease with increased seed moisture and temperature.

In this paper, a multivariate fundamental skew probit (MFSP) model is used to model correlated ordinal responses which are constructed from the multivariate fundamental skew normal (MFSN) distribution originate to the greater flexibility of MFSN. To achieve an appropriate VC structure for reaching reliable statistical inferences, many types of variance covariance (VC) structures are considered to model MFSN. Simulation methods are used to find the properties of the parameters estimate. The Schizophrenia Collaborative Study data invokes the proposed MFSN model. The results confirm that the first-order autoregressive (AR(1)) structure substantially enhances the estimation of the parameters. Furthermore, over time the drugs effect the schizophrenia treatment, noticeably.

Land degradation significantly threatens global food security and ecosystems, necessitating effective landscape restoration measures, particularly among smallholder farmers in vulnerable areas like the Womba watershed. This study investigates the localized socio-economic and physical factors influencing the adoption of landscape restoration practices within the Womba watershed. Utilizing data from 337 randomly selected household heads, along with focus group discussions and key informant interviews, the data were analyzed using descriptive and multivariate probit modeling estimation. The findings reveal that physical restoration practices, biological land management practices, and agronomic measures are prevalent among farmers. Key factors influencing adoption include gender dynamics, with male-headed households favoring physical interventions, while female-headed households prefer agronomic approaches. Age negatively impacts the adoption of physical and biological practices, while education correlates positively with agronomic methods. Family size enhances agronomic adoption, and access to credit significantly increases the likelihood of implementing diverse restoration strategies. Village memberships are positively associated with four landscape restoration practices at a 1% significance level. Notably, while farmers recognize the ecological benefits of these practices, their perceptions of socio-economic advantages remain limited. To promote broader adoption of restoration initiatives, policymakers should enhance educational outreach on the long-term socio-economic benefits and improve access to credit and extension services. Integrating these dimensions into policy frameworks will foster greater participation from both male and female farmers, ultimately supporting sustainable development in the Womba watershed and beyond.

Inspired by the interactive discrete choice logit models [Aggarwal, 2019], this paper presents the advanced families of discrete choice models, such as nested logit, mixed logit, and probit models to consider the interaction among the attributes. Besides the DM’ s attitudinal character is also taken into consideration in the computation of choice probabilities. The proposed choice models make use of Choquet integal and the recent attitudinal Choquet integral. The models are termed as Choquet nested logit (CNL), Choquet mixed multinomial logit (CMMNL), and Choquet multinomial probit (CMP). These are further extended to represent the DM’ s attitudinal character, and termed as attitudinal CNL, attitudinal CMMNL, and attitudinal CMP.

In this paper we construct a modeling for detection of banks which are experiencing serious problems. Sample and variable set of the study contains 30 banks of Iran during 2006-2014 and their financial ratios. Well known multivariate statistical technique (principal component analysis) was used to explore the basic financial characteristics of the banks, and discriminant Logit and Probit models were estimated based on these characteristics. Results suggest that the model can be used as an analytical decision support tool in both on-site and off-site bank monitoring system to detect the banks which are experiencing serious problems.

In some medical studies, we may have several measurements on each patient. Sometimes these longitudinal data may be measured for several response variables, in this case, although the responses can be modeled separately, such an approach reduces the power and efficiency in estimating the effects of auxiliary variables on the response variable. In the analysis of such data, in addition to the analysis of the dependence between repeated measures related to each of the response variables, the dependence between the responses should also be considered. Among the methods used in recent years to model multivariate data is the copulafunction. One of the most important advantages of using the copula function compared to the longitudinal multivariate modeling of the data in the classic way is that, in addition to the normal distribution, any other distribution other than the normal can be considered as marginal distributions. Also, marginal distributions can even have different distributions. In situations where the data have a multivariate structure, one of the ways to form multivariate distributions is to use vine pair-copula function. In this study, we form a multivariate longitudinal structure by using the vine pair copula functions and compare these models with the model obtained from the fitting of the multivariate normal copula function. Then we will introduce the best model using the Akaike information criterion and at the end we will use the presented model on the data of the estimation of the effect of nutrition on growth.

For tropical countries such as Indonesia, flooding is generally caused by high rainfall above normal. Almost all cities in Indonesia experience flooding every year, including DKI Jakarta, the capital city of Indonesia. Based on data from the National Disaster Management Agency (BNPB) in 2020, East Jakarta is a city that is prone to flooding. Because there are so many losses caused by flooding, it is necessary to have a disaster mitigation effort to minimize the possible risk of flooding. One risk mitigation due to natural disasters is buying insurance products. However, not all people buy floodimpacted insurance products because of their economic and social factors. This research aims to create a model with Probit Regression Model to determine the factors that influence Indonesian's interest in buying flood-impacted insurance products. The results show that eight (8) factors significantly affect Indonesia's interest in purchasing flood-impacted insurance products, namely X1 (Contract/Lease and Family Owned), X2 (Married), X4 (Age of the Head of the Household), X7 (Has experienced a flood), X8 (Frequency of flood experience), X9 (flood height), and X15 (Total Property Loss due to flooding). In the end, this research calculated the level of model accuracy and obtained 84. 3%.

Aim: The purpose of this research is to formulate a program and design a suitable model for environmental monitoring of water quality in order to reduce the costs of water quality monitoring and evaluation in No.1 Zabol Chahnimeh. Material & Methods: In this study eight sampling stations were selected in the bed of the reservoir No.1 in order to monitoring and measuring physical, chemical and microbial parameters. Sampling was conducted systematically on a month basis for one year. Then the data were analyzed with the help of multivariate statistical methods such as cluster analysis, Principal Component Analysis (PCA) and factor analysis. Findings: Using multivariate statistical methods, No. 4, 5, 6 stations were selected as the main stations for sampling and factors of temperature, turbidity, DO, COD, magnesium, sodium, calcium, phosphate, chlorine, nitrite, sodium Absorption Ratio and pH was determined as the main water quality indicators. It was revealed that temperature, DO, nitrate and turbidity parameters should be sampled monthly, but the other parameters could be sampled periodically or even quarterly to model an optimal monitoring of water quality in reservoir No.1 during a year. Conclusion: In general, the evaluation of the quality status of this valuable water resource by reducing the number of sampling stations, eliminating some unnecessary parameters and reducing the frequency of sampling completely and without defects and at a much lower cost is done for this reason, having the lowest costs and proper timing, management of the main reservoir will be possible considering all effective parameters.