CUANDO NOS VAMOS A CRUZAR UNA CALLE A PIE, ESTIMAMOS ELĪNCHO DE LA CALLE, LA VELOCIDAD QUE TRAE UN AUTO, LA TODO MUNDO HACE ESTIMACIONES, POR EJEMPLO: PROPORCIONADOS POR UNA MUESTRA, UTILIZANDO LA INFERENCIA PARA ESTIMAR LOS VALORES DE UNA POBLACIÓN A PARTIR DE LOS DATOS A limiting aspect of the model is that it has worked on a closed population, which by definition could have a significant impact on obtaining the values of the estimators.ESTIMACIÓN ESTADÍSTICA: ES UNA TÉCNICA ESTADÍSTICA QUE SIRVE It can be ix concluded that enforcement actions have a positive effect for the company because the customer always attempt to regularize their status moroso.Y client, (3) the computational model presented here in the form of a flow chart and implemented in the R language, as shown in the appendices, have yielded values for the estimators under study. The cumulative probability of defaulting customers have qualified as an enforcement action has an average of 0.16, while the cumulative probability of regularization is greater than the cumulative rate regularizing after enforcement action. (2) The behavior of the hazard function 23, i.e., no adjustment to risk, also has a curved segment where the slope is maximum, which is explained by an increase in the risk of progression to stabilize, since enforcement action, during the month of May. The conclusions reached in the work are described as follows: (1) The hazard rate estimated from 23 model is small, interpreted this as the expected user performing an enforcement action is transferred to the regularized status, enforcement actions will result rather a protective factor, may be favorable to allow someone Sedalib enforcement action. Also, to obtain estimators has developed a program in the R language that works on the hypothesis of transition and final states do not allow returns for intermediate states. Modeling handover times and allies claim under a Markov process 'Illness - Death' censored intervals and loss of intermediate transition states, increases the accuracy of the estimates of the time and risk functions. (3) Develop a computer model in the program R In the application of the 'Disease - Death' Markov process with a customer base of Sedalib SA intervals are considered censored coercive actions of customers, and the loss of transition states for regularization. The specific goals were: (1) To estimate the risk of a customer defaulting regularization given enforcement action, and residence times of delinquent customer until the occurrence of regularization or enforcement action, (2) Find the probability accumulated delinquent clients who underwent coercive action in the field of study scenarios and their probability accumulated regularization and rate risk. Since this question was raised the overall goal, described as: 'Determining the delinquent behavior of users by a Markov process' disease - death 'applied to a database company Sedalib SA January - September 2012. This model has three states 1, 2 and 3 for 'health', 'illness' and 'death' respectively and only supports transitions 1 → 2.1 → 3.2 → 3, this process is also called the Markov because the probability of transition from one state to another is independent of the time spent in the initial state To apply this model to the database defaulters Sedalib company, raised the following question: 'How is the delinquent behavior of users by a Markov process' disease - death 'applied to a database SA firm Sedalib January - September 2012?. In this research, we state the properties of nonparametric estimation method on a model of 'disease - Death' Markov process.

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