Decisions about entry modes for telecom companies into digital music business: An empirical case study

The digital music market offers an opportunity for telecom companies to furnish value-added services. However, little effort has been made to study empirically how to make a decision about entry modes for telecom companies into digital music business. This study therefore investigates various strategies that telecom companies may implement to enter the digital music industry. The results of a series of expert surveys reveal that the preference ranking of entry modes is acquisition, joint venture, and contractual agreement, respectively. Moreover, the leading key factors in selecting entry modes are complementary capabilities, hostility of environment, relational risk between firms as partners, commitment of resources, and availability of expertise. Furthermore, an importance-performance analysis was conducted to understand the different aspects that are of different performances and importance in each entry mode. The implications of the findings are also discussed. --Telecom companies,Digital music business,Entry modes,Value added

CoViD-19: an automatic, semiparametric estimation method for the population infected in Italy

This is the final version. Available from PeerJ via the DOI in this record. Data Availability: The following information was supplied regarding data availability: Data and code are freely available at GitHub: https://github.com/pcm-dpc/COVID-19/tree/master/dati-regioniTo date, official data on the number of people infected with the SARS-CoV-2—responsible for the Covid-19—have been released by the Italian Government just on the basis of a non-representative sample of population which tested positive for the swab. However a reliable estimation of the number of infected, including asymptomatic people, turns out to be crucial in the preparation of operational schemes and to estimate the future number of people, who will require, to different extents, medical attentions. In order to overcome the current data shortcoming, this article proposes a bootstrap-driven, estimation procedure for the number of people infected with the SARS-CoV-2. This method is designed to be robust, automatic and suitable to generate estimations at regional level. Obtained results show that, while official data at March the 12th report 12.839 cases in Italy, people infected with the SARS-CoV-2 could be as high as 105.789

A wavelet threshold denoising procedure for multimodel predictions: An application to economic time series

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this recordNoise-affected economic time series, realizations of stochastic processes exhibiting complex and possibly nonlinear dynamics, are dealt with. This is often the case of time series found in economics, which notoriously suffer from problems such as low signal-to-noise ratios, asymmetric cycles and multiregimes patterns. In such a framework, even sophisticated statistical models might generate suboptimal predictions, whose quality can further deteriorate unless time consuming updating or deeper model revision procedures are carried out on a regular basis. However, when the models' outcomes are expected to be disseminated in timeliness manner (as in the case of Central Banks or national statistical offices), their modification might not be a viable solution, due to time constraints. On the other hand, if the application of simpler linear models usually entails relatively easier tuning-up procedures, this would come at the expenses of the quality of the predictions yielded. A mixed, self-tuning forecasting method is therefore proposed. This is an automatic, 2-stage procedure, able to generate predictions by exploiting the denoising capabilities provided by the wavelet theory in conjunction with a compounded forecasting generator. Its out-of-sample performances are evaluated through an empirical study carried out on macroeconomic time series

Smoothing parameter estimation for first order discrete time infinite impulse response filters

This is the final version. Available from MedCrave Group via the DOI in this record. Discrete time Infinite Impulse Response low-pass filters are widely used in many fields such as engineering, physics and economics. Once applied to a given time series, they have the ability to pass low frequencies and attenuate high frequencies. As a result, the data are expected to be less noisy. A properly filtered signal, is generally more informative with positive repercussions involving qualitative aspects – e.g. visual inspection and interpretation – as well as quantitative ones, such as its digital processing and mathematical modelling. In order to effectively disentangle signal and noise, the filter smoothing constant, which controls the degree of smoothness in First Order Discrete Time Infinite Impulse Response Filters, has to be carefully selected. The proposed method conditions the estimation of the smoothing parameter to a modified version of the information criterion of the type Hannan - Quinn which in turns is built using the Estimated Log Likelihood Function of a model of the class SARIMA (Seasonal Auto Regressive Moving Average). Theoretical evidences as well as an empirical study conducted on a particularly noisy time series will be presented

time series chaos detection and assessment via scale dependent lyapunov exponent

Many dynamical systems in a wide range of disciplines -- such as engineering, economy and biology -- exhibit complex behaviors generated by nonlinear components which might result in deterministic chaos. While in lab--controlled setups its detection and level estimation is in general a doable task, usually the same does not hold for many practical applications. This is because experimental conditions imply facts like low signal--to--noise ratios, small sample sizes and not--repeatability of the experiment, so that the performances of the tools commonly employed for chaos detection can be seriously affected. To tackle this problem, a combined approach based on wavelet and chaos theory is proposed. This is a procedure designed to provide the analyst with qualitative and quantitative information, hopefully conducive to a better understanding of the dynamical system the time series under investigation is generated from. The chaos detector considered is the well known Lyapunov Exponent. A real life application, using the Italian Electric Market price index, is employed to corroborate the validity of the proposed approach

Forecasting the COVID-19 diffusion in Italy and the related occupancy of intensive care units

This is the final version. Available from Hindawi via the DOI in this record. Data Availability: The data used to support the findings of this study are available from the corresponding author upon request.This paper provides a model-based method for the forecast of the total number of currently COVID-19 positive individuals and of the occupancy of the available intensive care units in Italy. The predictions obtained—for a time horizon of 10 days starting from March 29th—will be provided at a national as well as at a more disaggregated level, following a criterion based on the magnitude of the phenomenon. While those regions hit the most by the pandemic have been kept separated, those less affected regions have been aggregated into homogeneous macroareas. Results show that—within the forecast period considered (March 29th–April 7th)—all of the Italian regions will show a decreasing number of COVID-19 positive people. The same will be observed for the number of people who will need to be hospitalized in an intensive care unit. These estimates are valid under constancy of the government’s current containment policies. In this scenario, northern regions will remain the most affected ones, whereas no significant outbreaks are foreseen in the southern regions

Time series chaos detection and assessment via scale dependent Lyapunov Exponent

This is the final version. Available from the Canadian Center of Science and Education via the DOI in this record. Many dynamical systems in a wide range of disciplines -- such as engineering, economy and biology -- exhibit complex behaviors generated by   nonlinear components which might result in deterministic chaos. While in  lab--controlled setups its detection and level estimation  is in general a doable task, usually the same  does not hold for many   practical applications. This is because experimental conditions imply facts like low signal--to--noise ratios, small sample sizes and not--repeatability  of the experiment, so that the performances of the tools commonly employed for chaos detection can be seriously affected.  To tackle this problem, a combined approach based on wavelet and chaos theory is proposed. This is a procedure designed to provide the analyst with qualitative and quantitative information, hopefully conducive to a better understanding of the dynamical system the time series under investigation is generated from. The chaos detector considered is the well known Lyapunov Exponent. A real life application, using the Italian Electric Market price index, is employed to corroborate the validity of the proposed approach.</jats:p

Loss of fitting and distance prediction in fixed vs updated ARIMA models

This is the final version. Available from Global Journals via the DOI in this record. In many cases, it might be advisable to keep an operational time series model fixed for a given span of time, instead of updating it as a new datum becomes available. One common case, is represented by model–based deseasonalization procedures, whose time series models are updated on a regular basis by National Statistical Offices. In fact, in order to minimize the extent of the revisions and grant a greater stability of the already released figures, the interval in between two updating processes is kept "reasonably" long (e.g. one year). Other cases can be found in many contexts, e.g. in engineering for structural reliability analysis or in all those cases where model re–estimation is not a practical or even a viable options, e.g. due to time constraints or computational issues. Clearly, the inevitable trade–off between a fixed models and its updated counterpart, e.g. in terms of fitting performances, out–of–sample prediction capabilities or dynamics explanation should be always accounted for

Bootstrap order determination for ARMA models: a comparison between different model selection criteria

This is the final version. Available from Hindawi via the DOI in this record. The present paper deals with the order selection of models of the class for autoregressive moving average. A novel method—previously designed to enhance the selection capabilities of the Akaike Information Criterion and successfully tested—is now extended to the other three popular selectors commonly used by both theoretical statisticians and practitioners. They are the final prediction error, the Bayesian information criterion, and the Hannan-Quinn information criterion which are employed in conjunction with a semiparametric bootstrap scheme of the type sieve

Influencia da redução da espessura da camada suporte na eficiencia da filtração lenta com uso de mantas sinteticas não tecidas para aguas de abastecimento

Orientador: Jose Euclides Stipp PaternianiDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia CivilResumo: O presente trabalho foi realizado com a finalidade de investigar a influência da substituição do pedregulho da camada suporte por mantas sintéticas não tecidas na eficiência da filtração lenta, em areia, para tratamento de águas de abastecimento. A investigação experimental foi realizada em instalação piloto constituída de uma unidade de pré-filtração em pedregulho com escoamento ascendente e três unidades de filtração lenta, as quais possuíam como meio fíltrante areia (tamanho efetivo igual a 0,25 mm, coeficiente de desuniformidade igual a 2,4 e tamanho dos grãos variando entre 0,053 a 0,85 mm) e mantas ( M2: gramatura= 380 g/m2; M3: gramatura= 450 g/m2 e espessura total= 8 mm) instaladas no topo deste. As três unidades de filtração lenta diferiam entre si pela espessura e composição da camada suporte, sendo que, a primeira unidade denominada de FL 1 possuía como camada suporte pedregulho (2,00 mm a 6,25 mm), a segunda unidade (FL2) possuía pedregulho (6,25 mm a 21,0 mm) e manta (M1: gramatura= 300 g/m2, espessura= 2 mm) e a terceira unidade, FL3, possuía apenas manta (M1: gramatura= 300 g/m2, espessura= 2 mm) como camada suporte. Foram realizados ensaios com taxas de filtração de 3 m3/m2dia, 6 m3/m2dia e 9 m3/m2dia. A eficiência das unidades foi avaliada através da medição dos seguintes parâmetros: turbidez, cor aparente, coliformes totais, coliformes fecais, ferro, manganês, oxigênio dissolvido, sólidos suspensos totais e pH. Os resultados obtidos indicam que não houve diferença significativa entre os três filtros quanto a eficiência de remoção de impurezas, indicando a viabilidade da substituição do pedregulho por mantas sintéticas não tecidas (M1: gramatura= 300 g/m2, espessura= 2 mm) na camada suporte de unidades de filtração lenta, possibilitando assim, o prolongamento da duração da carreira de filtração e também a contribuição na redução dos custos de implantação dessa tecnologia de tratamento de águas, tornando-a mais acessível às regiões menos favorecidasAbstract: The purpose of this study was to investigate the influence of the gravel suporter bed replacement for nonwoven synthetic fabrics in slow sand filtration for treating water supplies. The experimental investigation was carried out in a pilot plant built with an upflow roughing filter unit and three slow sand filtration units, which were made by sand (Te = 0,25mm, CO = 2.4 and grain size varying between 0,053m and 0,85mm and nonwoven synthetic fabrics (M2: mass per unit area = 380 g/m2, M3: mass per unit area = 450 g/m2 and thickness = 8 mm) installed on the sand layer top. The three units of slow sand filtration (FL 1, FL2, FL3) were different considering the thickness and composition of the gravel suporter bed. The first unit, FL 1 has gravel size varying between 2.00 mm to 6,25 mm, the second unit, FL2, has gravel size varying between 6,25 mm to 21,0 mm plus nonwoven synthetic fabric (M1: mass per unit area = 300 g/m2, thickness = 2 mm) and the third unit, FL3, has only fabric (M1: mass per unit area = 300 g/m2, thickness = 2 mm). The filtration rates used in the essays were 3m3/m2day, 6m3/m2day e 9m3/m2day. The slow sand filtration units efficiency was evaluated by measurement of the main water quality parameters: turbidity, apparent color, total coliforms, fecal coliforms, iron, manganese, dissolved oxygen, total suspended solids and pH. The results showed that the efficiency of impurities removal did not have significant difference between the three slow sand filters, indicating that the substitution of the gravel by nonwoven synthetic fabric (M1: mass per unit area = 300 g/m, thickness = 2 mm) viability in the suporter bed for slow sand filtration. This makes possible to extend the filtration run duration and also to reduct the technology implantation costs, making it accessible to developing countries and regionsMestradoSaneamento e AmbienteMestre em Engenharia Civi