# Journal of Mathematics and Statistics

## New Kind of Statistical Methods

DOI : 10.3844/jmssp.2014.423.425

Journal of Mathematics and Statistics

Volume 10, Issue 4

Pages 423-425

### Editorial

Florence Nightingale said that the Statistics is the most important science in the whole world: For upon it depends the practical application of every other science and every art: The one science essential to all political and social administration, all education, all organization based on experience, for it only gives results of our experience. Basic forms of statistics have been used since the beginning of civilization. It is obvious that we need statistical methods since Statistics appears in almost all areas of science, technology, research and wherever data is obtained for the purpose of finding information. Statistics is the study of data and how it can be collected, analyzed and presented in order to answer questions pertaining to the world around us. Statistics was also described as the science of making conclusions in the presence of uncertainty. To deal with the uncertainty, statistical conventional methods based on probability theory have been utilized since 17th century. The improvements in computer technology over recent decades make it possible to use intelligent approaches for analyzing the data and dealing with the uncertainty (Aladag et al., 2014a). Using such techniques in Statistics has provided some important advantages (Yolcu et al., 2013). At the present time, a method based on the philosophy of intelligent approaches is a new kind of statistical methods.

It should be noted that making a direct comparison between the conventional methods and the intelligent techniques is nonsense. It would be a crucial mistake to think these approaches separately. They are pieces of a whole since they are just different answers for the same question. Instead of comparing them against each other, both can be used together to reach better results. In other words, a better answer can be obtained by taking advantage of both approaches. Thus, the problems included in these approaches can be dealt with and better methods can be constructed. A key point is that a simple hybrid method, which combines some of these approaches, should not be considered only as a better method for data analysis. Instead of this, philosophies of these approaches should be combined. To do this, the philosophies of these approaches have to be known well. For example, a standard ANN model for time series forecasting do not have a Moving Average (MA) term. This method works like an Autoregressive (AR) model because of the characteristics of the method. A hybrid method can be generated by combining MA model and ANN method in order to analyze time series which have both AR and MA structures. It is expected that a hybrid approach will give more accurate forecasts than those obtained from MA models or standard ANN models (Egrioglu et al., 2013). However, it would be wiser to generate a new ANN model which includes MA term. To do this, a new ANN architecture that includes MA term should be constructed and may be a new training algorithm should be improved to train this new architecture. Such a forecasting approach will produce very accurate results for real-world time series including both AR and MA structures. Another example can be given for fuzzy logic and probability theory based methods. Probability theory concerns about random variables which take crisp values. In a fuzzy system, variables take fuzzy values. Instead of using a hybrid method combines a fuzzy logic based method and a method based on probability theory, a new probabilistic fuzzy approach can be generated. To do this, may be the definition of random fuzzy variable can be described firstly.

To sum up, statistical methods are evolving and improving in accordance with the needs of the day. Thanks to the intelligent methods, more effective statistical approaches have been recently developed in the literature (Aladag et al., 2014b). For future studies, the following issues have to be considered carefully:

• The intelligent approaches have also some crucial problems in spite of their success in data analysis. Some of these problems could be dealt with by utilizing the probability theory and the conventional approaches

• The intelligent techniques can generally produce better results than those obtained from the conventional methods. However, it does not mean that the conventional methods are not useful or unnecessary. Theory of the conventional approaches should be well understood since new methods such as the intelligent ones can be generated only if shortcomings and disadvantages of the conventional methods are known well. In addition, the conventional approaches can give desired results in some cases. Furthermore, the theory of the conventional approaches should be used as a guide to develop new methods

• All intelligent techniques are not based on the probability theory. However, this does not mean that the intelligent approaches are not based on any theory. These approaches work on the basis of advanced strategies. These strategies should be coded in a proper computer language. This requires the art of computer programming

• The conventional methods based on probability theory and the intelligent methods can be combined in order to develop new efficient data analysis techniques. When such a technique is being constructed, the philosophies of these methods should be taken into account as mentioned before. Therefore, the philosophies of both approaches have to be known well

At the present time, the intelligent techniques have to be utilized in Statistics. Ignoring the importance of the intelligent methods in data analysis would be a great mistake. Of course the intelligent methods have some problems as with the conventional methods based on the probability theory (Aladag et al., 2012). As G.E.P. Box said, "All models are wrong, but some are useful." The question, how the data can be analyzed better, will stay the same while the answer will be changing. We, researchers and practitioners, will keep seeking a better answer for this question. In other words, the methods such as deterministic, methods based on probability theory, intelligent or hybrid approaches for data analysis will continue to evolve and improve. Also, new kinds of statistical methods will be proposed in the future.

### References

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