Abstract: In Chile, there is a lack of evidence about the impact of polyvictimization on the emergence of suicidal thoughts among children and young people. Thus, this study aims to explore the association between the episodes of polyvictimization suffered by Chilean children and young people and the manifestation of signs related to suicidal tendencies. To achieve this purpose, secondary data from the First Polyvictimization Survey on Children and Adolescents of 2017 were analyzed, and a binomial logistic regression model was applied to establish the probability that young people are experiencing suicidal ideation episodes. The main findings show that women between the ages of 13 and 15 years, who are in seventh grade and second in subsidized schools, are more likely to express suicidal ideas, which increases if they have suffered different types of victimization, particularly physical violence, psychological aggression, and sexual abuse.
Abstract: Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.
Abstract: The under-5 mortality rate is high in sub-Saharan Africa with Lesotho being amongst the highest under-5 mortality rates in the world. The objective of the study is to determine the factors associated with under-5 mortality in Lesotho. The data used for this analysis come from the nationally representative household survey called the 2009 Lesotho Demographic and Health Survey. Odds ratios produced by the logistic regression models were used to measure the effect of each independent variable on the dependent variable. Female children were significantly 38% less likely to die than male children. Children who were breastfed for 13 to 18 months and those who were breastfed for more than 19 months were significantly less likely to die than those who were breastfed for 12 months or less. Furthermore, children of mothers who stayed in Quthing, Qacha’s Nek and Thaba Tseka ran the greatest risk of dying. The results suggested that: sex of child, type of birth, breastfeeding duration, district, source of energy and marital status were significant predictors of under-5 mortality, after correcting for all variables.
Abstract: Myocardial infarction is one of the leading causes of
death in the world. Some of these deaths occur even before the
patient reaches the hospital. Myocardial infarction occurs as a result
of impaired blood supply. Because the most of these deaths are due to
coronary artery disease, hence the awareness of the warning signs of
a heart attack is essential. Some heart attacks are sudden and intense,
but most of them start slowly, with mild pain or discomfort, then
early detection and successful treatment of these symptoms is vital to
save them. Therefore, importance and usefulness of a system
designing to assist physicians in early diagnosis of the acute heart
attacks is obvious. The main purpose of this study would be to enable patients to
become better informed about their condition and to encourage them
to seek professional care at an earlier stage in the appropriate
situations. For this purpose, the data were collected on 711 heart
patients in Iran hospitals. 28 attributes of clinical factors can be
reported by patients; were studied. Three logistic regression models
were made on the basis of the 28 features to predict the risk of heart
attacks. The best logistic regression model in terms of performance
had a C-index of 0.955 and with an accuracy of 94.9%. The variables,
severe chest pain, back pain, cold sweats, shortness of breath, nausea
and vomiting, were selected as the main features.
Abstract: International market expansion involves a strategic process of market entry decision through which a firm expands its operation from domestic to the international domain. Hence, entry timing choices require the needs to balance the early entry risks and the problems in losing opportunities as a result of late entry into a new market. Questionnaire surveys administered to 115 Malaysian construction firms operating in 51 countries worldwide have resulted in 39.1 percent response rate. Factor analysis was used to determine the most significant factors affecting entry timing choices of the firms to penetrate the international market. A logistic regression analysis used to examine the firms’ entry timing choices, indicates that the model has correctly classified 89.5 per cent of cases as late movers. The findings reveal that the most significant factor influencing the construction firms’ choices as late movers was the firm factor related to the firm’s international experience, resources, competencies and financing capacity. The study also offers valuable information to construction firms with intention to internationalize their businesses.
Abstract: In July 1, 2007, Taiwan Stock Exchange (TWSE) on
market observation post system (MOPS) adds a new "Financial
reference database" for investors to do investment reference. This
database as a warning to public offering companies listed on the
public financial information and it original within eight targets. In
this paper, this database provided by the indicators for the application
of company financial crisis early warning model verify that the
database provided by the indicator forecast for the financial crisis,
whether or not companies have a high accuracy rate as opposed to
domestic and foreign scholars have positive results. There is use of
Logistic Regression Model application of the financial early warning
model, in which no joined back-conditions is the first model, joined it
in is the second model, has been taken occurred in the financial crisis
of companies to research samples and then business took place
before the financial crisis point with T-1 and T-2 sample data to do
positive analysis. The results show that this database provided the
debt ratio and net per share for the best forecast variables.
Abstract: A Matlab based software for logistic regression is developed to enhance the process of teaching quantitative topics and assist researchers with analyzing wide area of applications where categorical data is involved. The software offers an option of performing stepwise logistic regression to select the most significant predictors. The software includes a feature to detect influential observations in data, and investigates the effect of dropping or misclassifying an observation on a predictor variable. The input data may consist either as a set of individual responses (yes/no) with the predictor variables or as grouped records summarizing various categories for each unique set of predictor variables' values. Graphical displays are used to output various statistical results and to assess the goodness of fit of the logistic regression model. The software recognizes possible convergence constraints when present in data, and the user is notified accordingly.
Abstract: In this paper we present a novel approach for density estimation. The proposed approach is based on using the logistic regression model to get initial density estimation for the given empirical density. The empirical data does not exactly follow the logistic regression model, so, there will be a deviation between the empirical density and the density estimated using logistic regression model. This deviation may be positive and/or negative. In this paper we use a linear combination of Gaussian (LCG) with positive and negative components as a model for this deviation. Also, we will use the expectation maximization (EM) algorithm to estimate the parameters of LCG. Experiments on real images demonstrate the accuracy of our approach.
Abstract: The main aim of this study is to identify the most
influential variables that cause defects on the items produced by a
casting company located in Turkey. To this end, one of the items
produced by the company with high defective percentage rates is
selected. Two approaches-the regression analysis and decision treesare
used to model the relationship between process parameters and
defect types. Although logistic regression models failed, decision tree
model gives meaningful results. Based on these results, it can be
claimed that the decision tree approach is a promising technique for
determining the most important process variables.
Abstract: Cutting tools are widely used in manufacturing processes and drilling is the most commonly used machining process. Although drill-bits used in drilling may not be expensive, their breakage can cause damage to expensive work piece being drilled and at the same time has major impact on productivity. Predicting drill-bit breakage, therefore, is important in reducing cost and improving productivity. This study uses twenty features extracted from two degradation signals viz., thrust force and torque. The methodology used involves developing and comparing decision tree, random forest, and multinomial logistic regression models for classifying and predicting drill-bit breakage using degradation signals.
Abstract: This is a cross-cultural study that determines South
African multinational enterprises (MNEs) entry strategies as they
invest in Africa. An integrated theoretical framework comprising the
transaction cost theory, Uppsala model, eclectic paradigm and the
distance framework was adopted. A sample of 40 South African
MNEs with 415 existing FDI entries in Africa was drawn. Using an
ordered logistic regression model, the impact of culture on the choice
of degree of control by South African MNEs in Africa was
determined. Cultural distance was one of significant factors that
influenced South African MNEs- choice of degree of control.
Furthermore, South African MNEs are risk averse in all countries in
Africa but minimize the risks differently across sectors. Service
sectors chooses to own their subsidiaries 100% and avoid dealing
with the locals while manufacturing, resources and construction
choose to have a local partner to share the risk.
Abstract: In multi-parameter family of distributions, conditions
for a modified maximum likelihood estimator to be second order
admissible are given. Applying these results to the multi-parameter
logistic regression model, it is shown that the maximum likelihood
estimator is always second order inadmissible. Also, conditions for
the Berkson estimator to be second order admissible are given.
Abstract: The purposes of this study were as follows to evaluate
the economic value of Phu Kradueng National Park by the travel cost
method (TCM) and the contingent valuation method (CVM) and to
estimate the demand for traveling and the willingness to pay. The
data for this study were collected by conducting two large scale
surveys on users and non-users. A total of 1,016 users and 1,034
non-users were interviewed. The data were analyzed using multiple
linear regression analysis, logistic regression model and the
consumer surplus (CS) was the integral of demand function for trips.
The survey found, were as follows:
1)Using the travel cost method which provides an estimate of direct
benefits to park users, we found that visitors- total willingness to pay
per visit was 2,284.57 bath, of which 958.29 bath was travel cost,
1,129.82 bath was expenditure for accommodation, food, and
services, and 166.66 bath was consumer surplus or the visitors -net
gain or satisfaction from the visit (the integral of demand function for
trips).
2) Thai visitors to Phu Kradueng National Park were further willing
to pay an average of 646.84 bath per head per year to ensure the
continued existence of Phu Kradueng National Park and to preserve
their option to use it in the future.
3) Thai non-visitors, on the other hand, are willing to pay an average
of 212.61 bath per head per year for the option and existence value
provided by the Park.
4) The total economic value of Phu Kradueng National Park to Thai
visitors and non-visitors taken together stands today at 9,249.55
million bath per year.
5) The users- average willingness to pay for access to Phu Kradueng
National Park rises
from 40 bath to 84.66 bath per head per trip for improved services
such as road improvement, increased cleanliness, and upgraded
information.
This paper was needed to investigate of the potential market
demand for bio prospecting in Phu Kradueng national Park and to
investigate how a larger share of the economic benefits of tourism
could be distributed income to the local residents.
Abstract: Martingale model diagnostic for assessing the fit of logistic regression model to recurrent events data are studied. One way of assessing the fit is by plotting the empirical standard deviation of the standardized martingale residual processes. Here we used another diagnostic plot based on martingale residual covariance. We investigated the plot performance under several types of model misspecification. Clearly the method has correctly picked up the wrong model. Also we present a test statistic that supplement the inspection of the two diagnostic. The test statistic power agrees with what we have seen in the plots of the estimated martingale covariance.
Abstract: The goal of this research is discovering the
determinants of the success or failure of external cooperation in small
and medium enterprises (SMEs). For this, a survey was given to 190
SMEs that experienced external cooperation within the last 3 years. A
logistic regression model was used to derive organizational or strategic
characteristics that significantly influence whether external
collaboration of domestic SMEs is successful or not. Results suggest
that research and development (R&D) features in general
characteristics (both idea creation and discovering market
opportunities) that focused on and emphasized indirected-market
stakeholders (such as complementary companies and affiliates) and
strategies in innovative strategic characteristics raise the probability of
successful external cooperation. This can be used meaningfully to
build a policy or strategy for inducing successful external cooperation
or to understand the innovation of SMEs.
Abstract: To evaluate the ability to predict xerostomia after
radiotherapy, we constructed and compared neural network and
logistic regression models. In this study, 61 patients who completed a
questionnaire about their quality of life (QoL) before and after a full
course of radiation therapy were included. Based on this questionnaire,
some statistical data about the condition of the patients’ salivary
glands were obtained, and these subjects were included as the inputs of
the neural network and logistic regression models in order to predict
the probability of xerostomia. Seven variables were then selected from
the statistical data according to Cramer’s V and point-biserial
correlation values and were trained by each model to obtain the
respective outputs which were 0.88 and 0.89 for AUC, 9.20 and 7.65
for SSE, and 13.7% and 19.0% for MAPE, respectively. These
parameters demonstrate that both neural network and logistic
regression methods are effective for predicting conditions of parotid
glands.